Articles | Volume 21, issue 5
Atmos. Chem. Phys., 21, 3919–3948, 2021
https://doi.org/10.5194/acp-21-3919-2021
Atmos. Chem. Phys., 21, 3919–3948, 2021
https://doi.org/10.5194/acp-21-3919-2021

Research article 17 Mar 2021

Research article | 17 Mar 2021

Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning

Roland Stirnberg et al.

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Air pollution endangers human health and poses a problem particularly in densely populated areas. Here, an explainable machine learning approach is used to analyse periods of high particle concentrations for a suburban site southwest of Paris to better understand its atmospheric drivers. Air pollution is particularly excaberated by low temperatures and low mixed layer heights, but processes vary substantially between and within seasons.
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