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
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Robert Woodward-Massey, Roberto Sommariva, Lisa K. Whalley, Danny R. Cryer, Trevor Ingham, William J1 Bloss, Sam Cox, James D. Lee, Chris P. Reed, Leigh R. Crilley, Louisa J. Kramer, Brian J. Bandy, Grant L. Forster, Claire E. Reeves, Paul S. Monks, and Dwayne E. Heard
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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...
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