Articles | Volume 22, issue 12
https://doi.org/10.5194/acp-22-8385-2022
© Author(s) 2022. 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-22-8385-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jun 2022
Research article |
| 29 Jun 2022
| 29 Jun 2022
A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019
School of Environmental Sciences, University of East Anglia,
Norwich, NR47 TJ, UK
Grant L. Forster
School of Environmental Sciences, University of East Anglia,
Norwich, NR47 TJ, UK
National Centre for Atmospheric Science, University of East
Anglia, Norwich, NR47 TJ, UK
Peer Nowack
School of Environmental Sciences, University of East Anglia,
Norwich, NR47 TJ, UK
Grantham Institute – Climate Change and the Environment, Imperial College London, London SW7 2AZ, UK
Department of Physics, Imperial College London, London SW7 2AZ, UK
Data Science Institute, Imperial College London, London SW7 2AZ, UK
Viewed
Total article views: 3,477 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 Jan 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,547 | 881 | 49 | 3,477 | 197 | 20 | 51 |
- HTML: 2,547
- PDF: 881
- XML: 49
- Total: 3,477
- Supplement: 197
- BibTeX: 20
- EndNote: 51
Total article views: 2,579 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 29 Jun 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,935 | 617 | 27 | 2,579 | 123 | 12 | 39 |
- HTML: 1,935
- PDF: 617
- XML: 27
- Total: 2,579
- Supplement: 123
- BibTeX: 12
- EndNote: 39
Total article views: 898 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 Jan 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 612 | 264 | 22 | 898 | 74 | 8 | 12 |
- HTML: 612
- PDF: 264
- XML: 22
- Total: 898
- Supplement: 74
- BibTeX: 8
- EndNote: 12
Viewed (geographical distribution)
Total article views: 3,477 (including HTML, PDF, and XML)
Thereof 3,611 with geography defined
and -134 with unknown origin.
Total article views: 2,579 (including HTML, PDF, and XML)
Thereof 2,707 with geography defined
and -128 with unknown origin.
Total article views: 898 (including HTML, PDF, and XML)
Thereof 904 with geography defined
and -6 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- Large Modeling Uncertainty in Projecting Decadal Surface Ozone Changes Over City Clusters of China X. Weng et al. 10.1029/2023GL103241
- Sustained emission reductions have restrained the ozone pollution over China Y. Wang et al. 10.1038/s41561-023-01284-2
- Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data H. Li et al. 10.5194/acp-23-1131-2023
- Spatiotemporal variations in meteorological influences on ambient ozone in China: A machine learning approach T. Li et al. 10.1016/j.apr.2023.101720
- Impact of the strong wintertime East Asian trough on the concurrent PM2.5 and surface O3 in eastern China X. An et al. 10.1016/j.atmosenv.2023.119846
- Short-term forecasting of ozone air pollution across Europe with transformers S. Hickman et al. 10.1017/eds.2023.37
- O3–precursor relationship over multiple patterns of timescale: a case study in Zibo, Shandong Province, China Z. Zheng et al. 10.5194/acp-23-2649-2023
- Shifts of Formation Regimes and Increases of Atmospheric Oxidation Led to Ozone Increase in North China Plain and Yangtze River Delta From 2016 to 2019 S. Zhu et al. 10.1029/2022JD038373
- Hybrid machine learning model for hourly ozone concentrations prediction and exposure risk assessment W. Lingxia et al. 10.1016/j.apr.2023.101916
- Factors driving changes in surface ozone in 44 coastal cities in China X. Liu et al. 10.1007/s11869-023-01446-6
- Deep Learning‐Based Ensemble Forecasts and Predictability Assessments for Surface Ozone Pollution A. Zhang et al. 10.1029/2022GL102611
- Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China M. Cheng et al. 10.1016/j.scitotenv.2023.163146
- The role of NOx in Co-occurrence of O3 and PM2.5 pollution driven by wintertime east Asian monsoon in Hainan J. Zhan et al. 10.1016/j.jenvman.2023.118645
- Contrasting changes in ozone during 2019–2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions Y. Ni et al. 10.1016/j.scitotenv.2023.168272
- Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021 M. Wang et al. 10.1016/j.scitotenv.2023.167763
15 citations as recorded by crossref.
- Large Modeling Uncertainty in Projecting Decadal Surface Ozone Changes Over City Clusters of China X. Weng et al. 10.1029/2023GL103241
- Sustained emission reductions have restrained the ozone pollution over China Y. Wang et al. 10.1038/s41561-023-01284-2
- Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data H. Li et al. 10.5194/acp-23-1131-2023
- Spatiotemporal variations in meteorological influences on ambient ozone in China: A machine learning approach T. Li et al. 10.1016/j.apr.2023.101720
- Impact of the strong wintertime East Asian trough on the concurrent PM2.5 and surface O3 in eastern China X. An et al. 10.1016/j.atmosenv.2023.119846
- Short-term forecasting of ozone air pollution across Europe with transformers S. Hickman et al. 10.1017/eds.2023.37
- O3–precursor relationship over multiple patterns of timescale: a case study in Zibo, Shandong Province, China Z. Zheng et al. 10.5194/acp-23-2649-2023
- Shifts of Formation Regimes and Increases of Atmospheric Oxidation Led to Ozone Increase in North China Plain and Yangtze River Delta From 2016 to 2019 S. Zhu et al. 10.1029/2022JD038373
- Hybrid machine learning model for hourly ozone concentrations prediction and exposure risk assessment W. Lingxia et al. 10.1016/j.apr.2023.101916
- Factors driving changes in surface ozone in 44 coastal cities in China X. Liu et al. 10.1007/s11869-023-01446-6
- Deep Learning‐Based Ensemble Forecasts and Predictability Assessments for Surface Ozone Pollution A. Zhang et al. 10.1029/2022GL102611
- Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China M. Cheng et al. 10.1016/j.scitotenv.2023.163146
- The role of NOx in Co-occurrence of O3 and PM2.5 pollution driven by wintertime east Asian monsoon in Hainan J. Zhan et al. 10.1016/j.jenvman.2023.118645
- Contrasting changes in ozone during 2019–2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions Y. Ni et al. 10.1016/j.scitotenv.2023.168272
- Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021 M. Wang et al. 10.1016/j.scitotenv.2023.167763
Latest update: 10 Dec 2023
Short summary
We use machine learning to quantify the meteorological drivers behind surface ozone variations in China between 2015 and 2019. Our novel approaches show improved performance when compared to previous analysis methods. We highlight that nonlinearity in driver relationships and the impacts of large-scale meteorological phenomena are key to understanding ozone pollution. Moreover, we find that almost half of the observed ozone trend between 2015 and 2019 might have been driven by meteorology.
We use machine learning to quantify the meteorological drivers behind surface ozone variations...
Altmetrics
Final-revised paper
Preprint