气候变化领域集成服务门户(CCPortal): Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning

CCPortal

DOI	10.5194/acp-21-3919-2021
	Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning
	Stirnberg R.; Cermak J.; Kotthaus S.; Haeffelin M.; Andersen H.; Fuchs J.; Kim M.; Petit J.-E.; Favez O.
发表日期	2021
ISSN	1680-7316
起始页码	3919
结束页码	3948
卷号	21 期号:5
英文摘要	Air pollution, in particular high concentrations of particulate matter smaller than 1 span classCombining double low lineinline-formulam in diameter (PMspan classCombining double low lineinline-formula1), 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 PMspan classCombining double low lineinline-formula1 variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PMspan classCombining double low lineinline-formula1 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 PMspan classCombining double low lineinline-formula1 concentrations (coefficient of determination (span classCombining double low lineinline-formulaiR/i2) of 0.58), with model performance depending on the individual PMspan classCombining double low lineinline-formula1 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 span classCombining double low lineinline-formulag1/45 span classCombining double low lineinline-formulag/mspan classCombining double low lineinline-formula3 for MLHs below span classCombining double low lineinline-formula<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 PMspan classCombining double low lineinline-formula1 concentrations of as much as span classCombining double low lineinline-formulag1/49 span classCombining double low lineinline-formulag/mspan classCombining double low lineinline-formula3. Northeasterly winds are found to contribute span classCombining double low lineinline-formulag1/45 span classCombining double low lineinline-formulag/mspan classCombining double low lineinline-formula3 to predicted PMspan classCombining double low lineinline-formula1 concentrations (combined effects of span classCombining double low lineinline-formulaiu/i-and span classCombining double low lineinline-formulaiv/i-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PMspan classCombining double low lineinline-formula1 concentrations in summer are temperatures above span classCombining double low lineinline-formulag1/425 span classCombining double low lineinline-formulagC (contributions of up to span classCombining double low lineinline-formulag1/42.5 span classCombining double low lineinline-formulag/mspan classCombining double low lineinline-formula3), dry spells of several days (maximum contributions of span classCombining double low lineinline-formulag1/41.5 span classCombining double low lineinline-formulag/mspan classCombining double low lineinline-formula3), and wind speeds below span classCombining double low lineinline-formulag1/42 m/s (maximum contributions of span classCombining double low lineinline-formulag1/43 span classCombining double low lineinline-formulag/mspan classCombining double low lineinline-formula3), 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./p. © 2021 Copernicus GmbH. All rights reserved.
语种	英语
scopus关键词	advection; atmospheric pollution; concentration (composition); empirical analysis; machine learning; particulate matter; regression analysis
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/247060
作者单位	Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Institut Pierre Simon Laplace, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France; Laboratoire des Sciences du Climat et de l'Environnement, CEA/Orme des Merisiers, Gif sur Yvette, France; Institut National de l'Environnement Industriel et des Risques, Parc Technologique ALATA, Verneuil en Halatte, France
推荐引用方式 GB/T 7714	Stirnberg R.,Cermak J.,Kotthaus S.,et al. Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning[J],2021,21(5).
APA	Stirnberg R..,Cermak J..,Kotthaus S..,Haeffelin M..,Andersen H..,...&Favez O..(2021).Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning.ATMOSPHERIC CHEMISTRY AND PHYSICS,21(5).
MLA	Stirnberg R.,et al."Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning".ATMOSPHERIC CHEMISTRY AND PHYSICS 21.5(2021).