气候变化领域集成服务门户(CCPortal): Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

CCPortal

DOI	10.1029/2019JD031380
	Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning
	Stirnberg R.; Cermak J.; Fuchs J.; Andersen H.
发表日期	2020
ISSN	2169897X
卷号	125 期号:4
英文摘要	The quantification of factors leading to harmfully high levels of particulate matter (PM) remains challenging. This study presents a novel approach using a statistical model that is trained to predict hourly concentrations of particles smaller than 10 (Formula presented.) m (PM10) by combining satellite-borne aerosol optical depth (AOD) with meteorological and land-use parameters. The model is shown to accurately predict PM10 (overall R2 = 0.77, RMSE = 7.44 μg/m3) for measurement sites in Germany. The capability of satellite observations to map and monitor surface air pollution is assessed by investigating the relationship between AOD and PM10 in the same modeling setup. Sensitivity analyses show that important drivers of modeled PM10 include multiday mean wind flow, boundary layer height (BLH), day of year (DOY), and temperature. Different mechanisms associated with elevated PM10 concentrations are identified in winter and summer. In winter, mean predictions of PM10 concentrations >35 μg/m3 occur when BLH is below ~ 500 m. Paired with multiday easterly wind flow, mean model predictions surpass 40 μg/m3 of PM10. In summer, PM10 concentrations seemingly are less driven by meteorology, but by emission or chemical particle formation processes, which are not included in the model. The relationship between AOD and predicted PM10 concentrations depends to a large extent on ambient meteorological conditions. Results suggest that AOD can be used to assess air quality at ground level in a machine learning approach linking it with meteorological conditions. ©2020. The Authors.
英文关键词	aerosol optical depth; air quality; drivers of air pollution; machine learning; MAIAC; PM10
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/186155
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
推荐引用方式 GB/T 7714	Stirnberg R.,Cermak J.,Fuchs J.,et al. Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning[J],2020,125(4).
APA	Stirnberg R.,Cermak J.,Fuchs J.,&Andersen H..(2020).Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning.Journal of Geophysical Research: Atmospheres,125(4).
MLA	Stirnberg R.,et al."Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning".Journal of Geophysical Research: Atmospheres 125.4(2020).