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

Stirnberg, Roland 1,2; Cermak, Jan 1,2; Kotthaus, Simone; Haeffelin, Martial; Andersen, Hendrik 1,2; Fuchs, Julia 1,2; Kim, Miae 1,2; Petit, Jean-Eudes; Favez, Olivier
1 Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT)
2 Institut für Photogrammetrie und Fernerkundung (IPF), Karlsruher Institut für Technologie (KIT)

Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM1), continues to be a major health problem, and meteorology is known to substantially influence atmospheric PM concentrations. However, the scientific understanding of the ways in which complex interactions of meteorological factors lead to high-pollution episodes is inconclusive. In this study, a novel, data-driven approach based on empirical relationships is used to characterize and better understand the meteorology-driven component of PM1 variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM1 at a suburban site southwest of Paris, France, using meteorological variables as input features. The model is able to capture the majority of occurring variance of mean afternoon total PM1 concentrations (coefficient of determination (R2) of 0.58), with model performance depending on the individual PM1 species predicted. Based on the models, an isolation and quantification of individual, season-specific meteorological influences for process understanding at the measurement site is achieved using SHapley Additive exPlanation (SHAP) regression values. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000130967
Veröffentlicht am 26.03.2021
DOI: 10.5194/acp-21-3919-2021
Zitationen: 10
Web of Science
Zitationen: 10
Zitationen: 11
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
Institut für Meteorologie und Klimaforschung (IMK)
Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1680-7324
KITopen-ID: 1000130967
HGF-Programm 12.11.26 (POF IV, LK 01) Aerosol-Cloud-Climate-Interaction
Erschienen in Atmospheric chemistry and physics
Verlag European Geosciences Union (EGU)
Band 21
Heft 5
Seiten 3919–3948
Vorab online veröffentlicht am 17.03.2021
Nachgewiesen in Scopus
Web of Science
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