Articles | Volume 24, issue 1
https://doi.org/10.5194/acp-24-345-2024
© 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-24-345-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Jan 2024
Research article |
| 11 Jan 2024
| 11 Jan 2024
Multidecadal ozone trends in China and implications for human health and crop yields: a hybrid approach combining a chemical transport model and machine learning
Jia Mao
Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong SAR, China
Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong SAR, China
State Key Laboratory of Agrobiotechnology, and Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong SAR, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China
David H. Y. Yung
Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong SAR, China
Tiangang Yuan
Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong SAR, China
Kong T. Chau
Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong SAR, China
Zhaozhong Feng
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China
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Cited
17 citations as recorded by crossref.
- Differences in ozone formation among urban, suburban, and rural areas: A case study in a typical industrial city in the North China plain P. Liu et al. https://doi.org/10.1016/j.envpol.2026.127677
- Machine learning elucidates ubiquity of enhanced ozone air pollution in China linked to the spring festival effect B. Zhu et al. https://doi.org/10.1016/j.apr.2024.102127
- Impacts of surface ozone pollution on wheat production in China from 2005 to 2019: A comparison among different methodologies for ozone-crop relationships J. Mao et al. https://doi.org/10.1016/j.atmosenv.2025.121413
- Cycling of Gaseous Reactive Nitrogen Oxides and Its Role in Driving Secondary Pollution S. Wang et al. https://doi.org/10.1021/acs.est.5c11062
- The multi-scale insights into summer ozone sources and their impacts on maize yield over the North China Plain H. Du et al. https://doi.org/10.1016/j.atmosres.2026.108833
- Impacts of irrigation on ozone and fine particulate matter (PM2.5) air quality: implications for emission control strategies for intensively irrigated regions in China T. Yuan et al. https://doi.org/10.5194/acp-25-4211-2025
- Contrasting effects of elevated ozone and climate warming on photosynthetic phenology in the Yellow River Basin Q. Zhang et al. https://doi.org/10.1016/j.gecco.2025.e03824
- Dominant role of humidity thresholds in driving seasonal ozone production regimes in urban Guiyang, Yunnan-Guizhou Plateau Y. Yang et al. https://doi.org/10.3389/fenvs.2026.1791891
- A gradient boosting-based machine learning framework for improving atmospheric visibility numerical prediction C. Han et al. https://doi.org/10.1016/j.atmosres.2026.109012
- Plant responses to gaseous pollutants, biochemical and transcriptomic insights M. Urfa Gul et al. https://doi.org/10.3389/fpls.2026.1768073
- Specific-Source Insights into Changes of O3 Concentrations and Health Risks in China Y. Wang et al. https://doi.org/10.1021/acs.est.6c01808
- Phenology- and light-based parameterization of stomatal conductance model for urban woody species in northern China S. Li et al. https://doi.org/10.1016/j.envres.2024.119658
- Characterization and sources of volatile organic compounds in a provincial capital city of northern China in 2019–2024: Impact of public events H. Sui et al. https://doi.org/10.1016/j.atmosenv.2024.121000
- Attention scores and peak perception in long-term ozone prediction using deep learning Z. Xu et al. https://doi.org/10.1016/j.envsoft.2025.106467
- Quantifying Ozone-Driven Forest Losses in Southwestern China (2019–2023) Q. Xia et al. https://doi.org/10.3390/atmos16080927
- Changing ozone sensitivity in Fujian Province, China, during 2012–2021: Importance of controlling VOC emissions N. Chen et al. https://doi.org/10.1016/j.envpol.2024.124757
- A global land daily 10-km-resolution surface ozone dataset from 2013–2022 R. Wang et al. https://doi.org/10.1038/s41597-025-05990-x
17 citations as recorded by crossref.
- Differences in ozone formation among urban, suburban, and rural areas: A case study in a typical industrial city in the North China plain P. Liu et al. https://doi.org/10.1016/j.envpol.2026.127677
- Machine learning elucidates ubiquity of enhanced ozone air pollution in China linked to the spring festival effect B. Zhu et al. https://doi.org/10.1016/j.apr.2024.102127
- Impacts of surface ozone pollution on wheat production in China from 2005 to 2019: A comparison among different methodologies for ozone-crop relationships J. Mao et al. https://doi.org/10.1016/j.atmosenv.2025.121413
- Cycling of Gaseous Reactive Nitrogen Oxides and Its Role in Driving Secondary Pollution S. Wang et al. https://doi.org/10.1021/acs.est.5c11062
- The multi-scale insights into summer ozone sources and their impacts on maize yield over the North China Plain H. Du et al. https://doi.org/10.1016/j.atmosres.2026.108833
- Impacts of irrigation on ozone and fine particulate matter (PM2.5) air quality: implications for emission control strategies for intensively irrigated regions in China T. Yuan et al. https://doi.org/10.5194/acp-25-4211-2025
- Contrasting effects of elevated ozone and climate warming on photosynthetic phenology in the Yellow River Basin Q. Zhang et al. https://doi.org/10.1016/j.gecco.2025.e03824
- Dominant role of humidity thresholds in driving seasonal ozone production regimes in urban Guiyang, Yunnan-Guizhou Plateau Y. Yang et al. https://doi.org/10.3389/fenvs.2026.1791891
- A gradient boosting-based machine learning framework for improving atmospheric visibility numerical prediction C. Han et al. https://doi.org/10.1016/j.atmosres.2026.109012
- Plant responses to gaseous pollutants, biochemical and transcriptomic insights M. Urfa Gul et al. https://doi.org/10.3389/fpls.2026.1768073
- Specific-Source Insights into Changes of O3 Concentrations and Health Risks in China Y. Wang et al. https://doi.org/10.1021/acs.est.6c01808
- Phenology- and light-based parameterization of stomatal conductance model for urban woody species in northern China S. Li et al. https://doi.org/10.1016/j.envres.2024.119658
- Characterization and sources of volatile organic compounds in a provincial capital city of northern China in 2019–2024: Impact of public events H. Sui et al. https://doi.org/10.1016/j.atmosenv.2024.121000
- Attention scores and peak perception in long-term ozone prediction using deep learning Z. Xu et al. https://doi.org/10.1016/j.envsoft.2025.106467
- Quantifying Ozone-Driven Forest Losses in Southwestern China (2019–2023) Q. Xia et al. https://doi.org/10.3390/atmos16080927
- Changing ozone sensitivity in Fujian Province, China, during 2012–2021: Importance of controlling VOC emissions N. Chen et al. https://doi.org/10.1016/j.envpol.2024.124757
- A global land daily 10-km-resolution surface ozone dataset from 2013–2022 R. Wang et al. https://doi.org/10.1038/s41597-025-05990-x
Saved (final revised paper)
Latest update: 14 Jul 2026
Short summary
Surface ozone (O3) is well-known for posing great threats to both human health and agriculture worldwide. However, a multidecadal assessment of the impacts of O3 on public health and agriculture in China is lacking without sufficient O3 observations. We used a hybrid approach combining a chemical transport model and machine learning to provide a robust dataset of O3 concentrations over the past 4 decades in China, thereby filling the gap in the long-term O3 trend and impact assessment in China.
Surface ozone (O3) is well-known for posing great threats to both human health and agriculture...
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