Source apportionment of environmental combustion sources using excitation emission matrix fluorescence spectroscopy and machine learning. (15th August 2021)

• Record Type: Journal Article
• Authors: Rutherford, Jay W.; Larson, Timothy V.; Gould, Timothy; Seto, Edmund; Novosselov, Igor V.; Posner, Jonathan D.
• Is Part Of: Atmospheric environment. Volume 259(2021)
• Publication Date: 2021-08-15
• Subjects: Source apportionment -- Particulate matter -- Excitation emission fluorescence spectroscopy -- Machine learning; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2021.118501
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: 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. Graphical abstract: Image 1