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

Roland Stirnberg (1, 2) , Jan Cermak (1, 2) , Simone Kotthaus , Martial Haeffelin , Hendrik Andersen (1, 2) , Julia Fuchs (1, 2) , Miae Kim (1, 2) , Jean-Eudes Petit (3, 4) , Olivier Favez (5)

1 Institute of Meteorology and Climate Research
2 KIT - Karlsruhe Institute of Technology
3 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]
4 CAE - Chimie Atmosphérique Expérimentale
5 INERIS - Institut National de l'Environnement Industriel et des Risques

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. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLHs), 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 a.g.l. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution to predicted PM1 concentrations of as much as ∼9 µg/m3. Northeasterly winds are found to contribute ∼5 µg/m3 to predicted PM1 concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PM1 concentrations in summer 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), which cause a lack of dispersion. High-resolution case studies are conducted showing a large variability of processes that can lead to high-pollution episodes. The identification of these meteorological conditions that increase air pollution could help policy makers to adapt policy measures, issue warnings to the public, or assess the effectiveness of air pollution measures.

Domaines: Sciences de l'environnement

Roland Stirnberg, Jan Cermak, Simone Kotthaus, Martial Haeffelin, Hendrik Andersen, et al.. Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning. Atmospheric Chemistry and Physics, 2021, 21 (5), pp.3919-3948. ⟨10.5194/acp-21-3919-2021⟩. ⟨ineris-03218211⟩