Articles | Volume 22, issue 12
https://doi.org/10.5194/acp-22-8385-2022
https://doi.org/10.5194/acp-22-8385-2022
Research article
 | 
29 Jun 2022
Research article |  | 29 Jun 2022

A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019

Xiang Weng, Grant L. Forster, and Peer Nowack

Related authors

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
Xiao Lu, Yiming Liu, Jiayin Su, Xiang Weng, Tabish Ansari, Yuqiang Zhang, Guowen He, Yuqi Zhu, Haolin Wang, Ganquan Zeng, Jingyu Li, Cheng He, Shuai Li, Teerachai Amnuaylojaroen, Tim Butler, Qi Fan, Shaojia Fan, Grant L. Forster, Meng Gao, Jianlin Hu, Yugo Kanaya, Mohd Talib Latif, Keding Lu, Philippe Nédélec, Peer Nowack, Bastien Sauvage, Xiaobin Xu, Lin Zhang, Ke Li, Ja-Ho Koo, and Tatsuya Nagashima
EGUsphere, https://doi.org/10.5194/egusphere-2024-3702,https://doi.org/10.5194/egusphere-2024-3702, 2024
Short summary

Related subject area

Subject: Gases | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Assessing the relative impacts of satellite ozone and its precursor observations to improve global tropospheric ozone analysis using multiple chemical reanalysis systems
Takashi Sekiya, Emanuele Emili, Kazuyuki Miyazaki, Antje Inness, Zhen Qu, R. Bradley Pierce, Dylan Jones, Helen Worden, William Y. Y. Cheng, Vincent Huijnen, and Gerbrand Koren
Atmos. Chem. Phys., 25, 2243–2268, https://doi.org/10.5194/acp-25-2243-2025,https://doi.org/10.5194/acp-25-2243-2025, 2025
Short summary
Evaluating present-day and future impacts of agricultural ammonia emissions on atmospheric chemistry and climate
Maureen Beaudor, Didier Hauglustaine, Juliette Lathière, Martin Van Damme, Lieven Clarisse, and Nicolas Vuichard
Atmos. Chem. Phys., 25, 2017–2046, https://doi.org/10.5194/acp-25-2017-2025,https://doi.org/10.5194/acp-25-2017-2025, 2025
Short summary
Air-pollution-satellite-based CO2 emission inversion: system evaluation, sensitivity analysis, and future research direction
Hui Li, Jiaxin Qiu, and Bo Zheng
Atmos. Chem. Phys., 25, 1949–1963, https://doi.org/10.5194/acp-25-1949-2025,https://doi.org/10.5194/acp-25-1949-2025, 2025
Short summary
Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning
Yuqing Qiu, Xin Li, Wenxuan Chai, Yi Liu, Mengdi Song, Xudong Tian, Qiaoli Zou, Wenjun Lou, Wangyao Zhang, Juan Li, and Yuanhang Zhang
Atmos. Chem. Phys., 25, 1749–1763, https://doi.org/10.5194/acp-25-1749-2025,https://doi.org/10.5194/acp-25-1749-2025, 2025
Short summary
Quantification of regional net CO2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements
Jeongmin Yun, Junjie Liu, Brendan Byrne, Brad Weir, Lesley E. Ott, Kathryn McKain, Bianca C. Baier, Luciana V. Gatti, and Sebastien C. Biraud
Atmos. Chem. Phys., 25, 1725–1748, https://doi.org/10.5194/acp-25-1725-2025,https://doi.org/10.5194/acp-25-1725-2025, 2025
Short summary

Cited articles

Archibald, A. T., Turnock, S. T., Griffiths, P. T., Cox, T., Derwent, R. G., Knote, C., and Shin, M.: On the changes in surface ozone over the twenty-first century: sensitivity to changes in surface temperature and chemical mechanisms: 21st century changes in surface ozone, Philos. T. R. Soc. A, 378, 20190329, https://doi.org/10.1098/rsta.2019.0329, 2020. 
Bishop, C. M.: Pattern recognition and machine learning, Springer Science+Business Media, Singapore, ISBN 978-0387-31073-2, 2006. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Ceppi, P. and Nowack, P.: Observational evidence that cloud feedback amplifies global warming, P. Natl. Acad. Sci. USA, 118, 1–7, https://doi.org/10.1073/pnas.2026290118, 2021. 
Chang, L., Xu, J., Tie, X., and Gao, W.: The impact of Climate Change on the Western Pacific Subtropical High and the related ozone pollution in Shanghai, China, Sci. Rep.-UK, 9, 1–12, https://doi.org/10.1038/s41598-019-53103-7, 2019. 
Download
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.
Share
Altmetrics
Final-revised paper
Preprint