气候变化领域集成服务门户(CCPortal): Source apportionment of environmental combustion sources using excitation emission matrix fluorescence spectroscopy and machine learning

CCPortal

DOI	10.1016/j.atmosenv.2021.118501
	Source apportionment of environmental combustion sources using excitation emission matrix fluorescence spectroscopy and machine learning
	Rutherford J.W.; Larson T.V.; Gould T.; Seto E.; Novosselov I.V.; Posner J.D.
发表日期	2021
ISSN	1352-2310
卷号	259
英文摘要	The link between particulate matter (PM) air pollution and negative health effects is well-established. Air pollution was estimated to cause 4.9 million deaths in 2017 and PM was responsible for 94% of these deaths. In order to inform effective mitigation strategies in the future, further study of PM and its health effects is important. Here, we present a method for identifying sources of combustion generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy and machine learning (ML) algorithms. PM samples were collected during a health effects exposure assessment panel study in Seattle. We use archived field samples from the exposure study and the associated positive matrix factorization (PMF) source apportionment based on X-ray fluorescence and light absorbing carbon measurements to train convolutional neural network and principal component regression algorithms. We show EEM spectra from cyclohexane extracts of the archived filter samples can be used to accurately apportion mobile and vegetative burning sources but were unable to detect crustal dust, Cl-rich, secondary sulfate and fuel oil sources. The use of this EEM-ML approach may be used to conduct PM exposure studies that include source apportionment of combustion sources. © 2021 Elsevier Ltd
关键词	Excitation emission fluorescence spectroscopy Machine learning Particulate matter Source apportionment
语种	英语
scopus关键词	Air pollution; Combustion; Factorization; Fluorescence; Fluorescence spectroscopy; Health; Matrix algebra; Neural networks; Particles (particulate matter); Principal component analysis; Sulfur compounds; Combustion sources; Excitation emission matrices; Excitation-emission fluorescence spectroscopies; Excitation-emission matrix fluorescence spectroscopies; Health effects; Machine-learning; Mitigation strategy; Particulate Matter; Particulate matter air pollution; Source apportionment; Machine learning; carbon; cyclohexane; fuel oil; sulfate; accuracy assessment; assessment method; atmospheric pollution; combustion; machine learning; mitigation; particulate matter; regression analysis; sampling; source apportionment; strategic approach; Article; combustion; convolutional neural network; environmental exposure; machine learning; panel study; particulate matter; principal component analysis; spectrofluorometry; X ray fluorescence; Seattle; United States; Washington [United States]
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248369
作者单位	Department of Chemical Engineering, University of Washington, Seattle, WA, United States; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States; Department of Mechanical Engineering, University of Washington, Seattle, WA, United States; Department of Family Medicine, University of Washington, Seattle, WA, United States
推荐引用方式 GB/T 7714	Rutherford J.W.,Larson T.V.,Gould T.,et al. Source apportionment of environmental combustion sources using excitation emission matrix fluorescence spectroscopy and machine learning[J],2021,259.
APA	Rutherford J.W.,Larson T.V.,Gould T.,Seto E.,Novosselov I.V.,&Posner J.D..(2021).Source apportionment of environmental combustion sources using excitation emission matrix fluorescence spectroscopy and machine learning.ATMOSPHERIC ENVIRONMENT,259.
MLA	Rutherford J.W.,et al."Source apportionment of environmental combustion sources using excitation emission matrix fluorescence spectroscopy and machine learning".ATMOSPHERIC ENVIRONMENT 259(2021).