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https://doi.org/10.5194/acp-2020-469
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-469
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Jul 2020

27 Jul 2020

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This preprint is currently under review for the journal ACP.

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

Roland Stirnberg1,2, Jan Cermak1,2, Simone Kotthaus3, Martial Haeffelin3, Hendrik Andersen1,2, Julia Fuchs1,2, Miae Kim1,2, Jean-Eudes Petit4, and Olivier Favez5 Roland Stirnberg et al.
  • 1Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  • 2Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  • 3Institut Pierre Simon Laplace, École Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
  • 4Laboratoire des Sciences du Climat et de l’Environnement, CEA/Orme des Merisiers, Gif sur Yvette, France
  • 5Institut National de l’Environnement Industriel et des Risques, Parc Technologique ALATA, Verneuil en Halatte, France

Abstract. 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 contribute to atmospheric PM concentrations. However, the scientific understanding of the complex mechanisms leading to high pollution episodes is inconclusive, as the effects of meteorological variables are not easy to separate and quantify. In this study, a novel, data-driven approach based on empirical relationships is used to characterise the role of meteorology on atmospheric concentrations of PM1. 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 contributions of each meteorological feature to modeled PM1 concentrations are quantified using SHapley Additive exPlanation (SHAP) regression values. Meteorological contributions to PM1 concentrations are analysed in selected high-resolution case studies, contrasting season-specific processes. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLH), low temperatures, low wind speeds or inflow from northeastern wind directions. Contributions of MLHs to the winter pollution episodes are quantified to be on average ~ 5 µg/m³ for MLHs below 500 m agl. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution of as much as ~ 9 µg/m³. Northeasterly winds are found to contribute ~ 5 µg/m³ to total PM1 concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. However, in calm conditions (i.e. wind speeds < ~ 2 m/s), the lack of dispersion leads to increased PM1 concentrations by ~ 3 µg/m³. Unusually high PM1 concentrations in summer are generally lower compared to winter peak concentrations, and are characterised by a higher content of organics. Meteorological drivers of summer peak PM1 concentrations are temperatures above ~ 25 °C (contributions of up to ~ 2.5 µg/m³), dry spells of several days (maximum contributions of ~ 1.5 µg/m³) and wind speeds below ~ 2 m/s (maximum contributions of ~ 3 µg/m³). High-resolution case studies show a large variability of processes, which together lead to high PM1 concentrations. Processes vary even within seasons. A high pollution episode in January 2016 is shown to be driven by a drop in temperature (maximum contributions of 11 µg/m³), which enhances formation of secondary inorganic aerosols (SIA) and likely causes an increase in local wood-burning emissions. In contrast, during December 2016, high PM1 concentrations are caused mainly by a shallow MLH and low wind speeds. It is shown that an observed decrease in pollution levels is linked to a change in wind direction, advecting cleaner, maritime air to the PM measurement site (combined contributions of u- and v-wind-components of ~ −4 µg/m³). The application of SHAP regression values within a machine learning framework presents a novel and promising way of analysing observational data sets in environmental sciences.

Roland Stirnberg et al.

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Short summary
Air pollution endangers human health and poses a problem particularly in densely populated areas. Here, periods of high particle concentrations are analysed for a suburban site southwest of Paris to better understand Its drivers. A machine learning model is set up, which establishes empirical relationships between air pollution and meteorological parameters. Air pollution is excaberated by low temperatures and low mixed layer heights, but processes vary substantially between and within seasons.
Air pollution endangers human health and poses a problem particularly in densely populated...
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