10th International Aerosol Conference September 2 - September 7, 2018 America's Center Convention Complex St. Louis, Missouri, USA
Abstract View
Excitation Emission Matrix Fluorescence Spectroscopy for Aerosol Source Identification
JAY RUTHERFORD, Neal Dawson-Elli, Igor Novosselov, Edmund Seto, Jonathan Posner, University of Washington
Abstract Number: 830 Working Group: Source Apportionment
Abstract The inhalation of particulate matter (PM) is a significant health risk that can reduce life expectancy due to increased cardio-pulmonary disease as well as exacerbate respiratory diseases such as asthma and pneumonia. PM originates from natural sources, as well as anthropogenic sources such as combustion engines, cigarettes, and agricultural fires. The standard method for quantifying personal exposure to PM is measuring the mass concentration of PM2.5 in air. Identifying the source of PM exposure can inform effective mitigation strategies and policies, but this is difficult to do using current personal monitoring techniques. Here we present a method for identifying PM source using excitation emission matrix (EEM) fluorescence spectroscopy and machine learning algorithms. We collected combustion generated PM2.5 from wood smoke, diesel soot, and cigarette smoke using personal exposure monitoring filters. Following gravimetric analysis to determine mass concentration, the filters were extracted into cyclohexane for analysis by EEM fluorescence spectroscopy. The spectra obtained from pure sources were used as training data for identification of the same sources in mixed samples using machine learning. The method can identify source signatures with a 4 hour sampling time at a mass concentration of 5 µg/m3. A pilot study of this EEM based source identification method is being evaluated as part of the Home Air In Agriculture - Pediatric Intervention Trial (HAPI) study in the Yakima valley of Washington using personal exposure monitoring units.