气候变化领域集成服务门户(CCPortal): Excitation emission matrix fluorescence spectroscopy for combustion generated particulate matter source identification

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DOI	10.1016/j.atmosenv.2019.117065
	Excitation emission matrix fluorescence spectroscopy for combustion generated particulate matter source identification
	Rutherford J.W.; Dawson-Elli N.; Manicone A.M.; Korshin G.V.; Novosselov I.V.; Seto E.; Posner J.D.
发表日期	2020
ISSN	13522310
卷号	220
英文摘要	The inhalation of particulate matter (PM) is a significant health risk associated with reduced life expectancy due to increased cardio-pulmonary disease and exacerbation of respiratory diseases such as asthma and pneumonia. PM originates from natural and anthropogenic sources including combustion engines, cigarettes, agricultural burning, and forest fires. Identifying the source of PM can inform effective mitigation strategies and policies, but this is difficult to do using current techniques. Here we present a method for identifying PM source using excitation emission matrix (EEM) fluorescence spectroscopy and a machine learning algorithm. We collected combustion generated PM2.5 from wood burning, diesel exhaust, and cigarettes using filters. Filters were weighted to determine mass concentration followed by extraction into cyclohexane and analysis by EEM fluorescence spectroscopy. Spectra obtained from each source served as training data for a convolutional neural network (CNN) used for source identification in mixed samples. This method can predict the presence or absence of the three laboratory sources with an overall accuracy of 89% when the threshold for classifying a source as present is 1.1 μg/m3 in air over a 24-h sampling time. The limit of detection for cigarette, diesel and wood are 0.7, 2.6, 0.9 μg/m3, respectively, in air assuming a 24-h sampling time at an air sampling rate of 1.8 L per minute. We applied the CNN algorithm developed using the laboratory training data to a small set of field samples and found the algorithm was effective in some cases but would require a training data set containing more samples to be more broadly applicable. © 2019 Elsevier Ltd
英文关键词	Diesel; Fluorescence; Neural network; Particulate matter; Source apportionment; Woodsmoke
学科领域	Combustion; Deforestation; Diesel engines; Fluorescence; Fluorescence spectroscopy; Health risks; Learning algorithms; Machine learning; Matrix algebra; Neural networks; Pulmonary diseases; Tobacco; Convolutional neural network; Diesel; Excitation-emission matrix fluorescence spectroscopies; Excitation-emission matrix fluorescences; Particulate Matter; Source apportionment; Source identification; Woodsmoke; Particles (particulate matter); cyclohexane; politef; polycyclic aromatic hydrocarbon; air sampling; anthropogenic source; artificial neural network; diesel; health risk; life expectancy; particulate matter; source apportionment; source identification; spectroscopy; air monitoring; air sampling; algorithm; Article; asthma; burn; chemical composition; combustion; convolutional neural network; diagnostic accuracy; excitation emission matrix spectrofluorometry; exhaust gas; limit of detection; machine learning; particulate matter; personal monitoring; priority journal; sensitivity and specificity; spectrofluorometry
语种	英语
scopus关键词	Combustion; Deforestation; Diesel engines; Fluorescence; Fluorescence spectroscopy; Health risks; Learning algorithms; Machine learning; Matrix algebra; Neural networks; Pulmonary diseases; Tobacco; Convolutional neural network; Diesel; Excitation-emission matrix fluorescence spectroscopies; Excitation-emission matrix fluorescences; Particulate Matter; Source apportionment; Source identification; Woodsmoke; Particles (particulate matter); cyclohexane; politef; polycyclic aromatic hydrocarbon; air sampling; anthropogenic source; artificial neural network; diesel; health risk; life expectancy; particulate matter; source apportionment; source identification; spectroscopy; air monitoring; air sampling; algorithm; Article; asthma; burn; chemical composition; combustion; convolutional neural network; diagnostic accuracy; excitation emission matrix spectrofluorometry; exhaust gas; limit of detection; machine learning; particulate matter; personal monitoring; priority journal; sensitivity and specificity; spectrofluorometry
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/120879
作者单位	Department of Chemical Engineering, University of Washington, United States; Department of Mechanical Engineering, University of Washington, United States; Department of Family Medicine, University of Washington, United States; Environmental and Occupational Health Sciences, University of Washington, United States; Department of Medicine: Pulmonary, Critical Care and Sleep Medicine, University of Washington, United States
推荐引用方式 GB/T 7714	Rutherford J.W.,Dawson-Elli N.,Manicone A.M.,et al. Excitation emission matrix fluorescence spectroscopy for combustion generated particulate matter source identification[J],2020,220.
APA	Rutherford J.W..,Dawson-Elli N..,Manicone A.M..,Korshin G.V..,Novosselov I.V..,...&Posner J.D..(2020).Excitation emission matrix fluorescence spectroscopy for combustion generated particulate matter source identification.Atmospheric Environment,220.
MLA	Rutherford J.W.,et al."Excitation emission matrix fluorescence spectroscopy for combustion generated particulate matter source identification".Atmospheric Environment 220(2020).