Preprints
https://doi.org/10.5194/acp-2021-1075
https://doi.org/10.5194/acp-2021-1075
 
25 Jan 2022
25 Jan 2022
Status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

A machine learning approach to quantify meteorological drivers of recent ozone pollution in China

Xiang Weng1, Grant Forster1,2, and Peer Nowack1,3 Xiang Weng et al.
  • 1School of Environmental Sciences, University of East Anglia, Norwich, NR47TJ, UK
  • 2National Centre for Atmospheric Sciences, University of East Anglia, Norwich, NR47TJ, UK
  • 3Grantham Institute – Climate Change and the Environment, Department of Physics, and the Data Science Institute, Imperial College London, London SW7 2AZ, UK

Abstract. Surface ozone concentrations have been increasing in many regions of China for the past few years, in contrast to policy-driven declines in other key air pollutants such as particulate matter. While the central role of meteorology in modulating ozone pollution is widely acknowledged, its quantitative contribution remains highly uncertain. Here, we use a data-driven machine learning approach to assess the impacts of meteorology on surface ozone variations in China for the years 2015 to 2019, considering the months of highest ozone pollution from April to October. To quantify the importance of various meteorological driver variables, we apply non-linear random forest regression (RFR) and linear ridge regression (RR) to learn relationships between meteorological variability and surface ozone in China, and contrast the results to those obtained with the widely used multiple linear regression (MLR) and stepwise MLR. We show that RFR outperforms the three linear methods when predicting ozone using only local meteorological predictor variables. This implies the importance of non-linear relationships between local meteorological factors and ozone, which are not captured by linear regression algorithms. In addition, we find that including non-local meteorological predictors can further improve the modelling skill of RR, particularly for Southern China, highlighting the importance of large-scale meteorological phenomena for ozone pollution in that region. Overall, RFR and RR are in close agreement concerning the leading meteorological drivers behind regional ozone pollution. For example, we find that temperature variations are the dominant meteorological driver for ozone pollution in Northern China (e.g., Beijing Tianjin Hebei region), whereas variations in relative humidity are the most important factor in Southern China (e.g., Pearl River Delta). Variability in surface solar radiation modulates photochemistry but was not considered as such in previous controlling factor analyses, and is found to be the most important predictor in the Yangtze River Delta and Sichuan Basin regions. In general, our analysis underlines that hot and dry weather conditions with high sunlight intensity are strongly related to high ozone pollution across China. This further validates our novel approach to quantify the central role of meteorology. By contrasting our meteorological ozone predictions with ozone measurements between 2015 and 2019, we estimate that almost half of the observed ozone trends across China might have been caused by meteorological variabilities on average. We highlight that these insights are of particular importance given possible increases in the frequency and intensity of weather extremes such as heatwaves under climate change.

Xiang Weng et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-1075', Anonymous Referee #1, 13 Mar 2022
    • AC1: 'Reply on RC1', Xiang Weng, 14 Apr 2022
  • RC2: 'Comment on acp-2021-1075', Anonymous Referee #2, 14 Mar 2022
    • AC2: 'Reply on RC2', Xiang Weng, 14 Apr 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-1075', Anonymous Referee #1, 13 Mar 2022
    • AC1: 'Reply on RC1', Xiang Weng, 14 Apr 2022
  • RC2: 'Comment on acp-2021-1075', Anonymous Referee #2, 14 Mar 2022
    • AC2: 'Reply on RC2', Xiang Weng, 14 Apr 2022

Xiang Weng et al.

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Short summary
We compare two machine learning approaches to quantify meteorological drivers behind recent surface ozone across China. Our novel approaches show an overall better agreement between predicted and observed ozone than commonly used methods such as multiple linear regression, and we highlight several key implications of our analysis. For example, we find that almost half of the observed ozone trend between 2015 and 2019 on average might have been driven by meteorological factors.
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