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Excitation Emission Matrix Fluorescence Spectroscopy for Combustion Generated Particulate Matter Source Identification

preprint
submitted on 20.08.2019 and posted on 21.08.2019 by Jay Rutherford, Neal Dawson-Elli, Anne M. Manicone, Gregory V. Korshin, Igor V. Novosselov, Edmund Seto, Jonathan D. Posner
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 were used as machine learning training data 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-hour sampling time. We apply this method to a small set of field samples to evaluate its effectiveness.

Funding

NIBIB U01 EB021923

History

Email Address of Submitting Author

jayruth@uw.edu

Institution

University of Washington

Country

United States

ORCID For Submitting Author

0000-0002-7518-5447

Declaration of Conflict of Interest

The authors declare no conflict of interest

Version Notes

Submitted for review 5-Aug-2019

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