Meteorology-driven variability of air pollution (PM.sub.1) revealed with explainable machine learning.

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From: Atmospheric Chemistry and Physics(Vol. 21, Issue 5)
Publisher: Copernicus GmbH
Document Type: Article
Length: 432 words

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Abstract :

Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM.sub.1 ), 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 PM.sub.1 variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM.sub.1 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 PM.sub.1 concentrations (coefficient of determination (R.sup.2) of 0.58), with model performance depending on the individual PM.sub.1 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 â¼5 µg/m.sup.3 for MLHs below

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Gale Document Number: GALE|A655342542