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Online Chemical Characterization of Food-Cooking Organic Aerosols: Implications for Source Apportionment

Journal article
Authors E. Reyes-Villegas
T. Bannan
Michael Le Breton
A. Mehra
M. Priestley
C. Percival
H. Coe
J. D. Allan
Published in Environmental Science & Technology
Volume 52
Issue 9
Pages 5308-5318
ISSN 0013-936X
Publication year 2018
Published at Department of Chemistry and Molecular Biology
Pages 5308-5318
Language en
Keywords mass-spectrometer data, high-resolution, particulate matter, thermal-decomposition, collection efficiencies, submicron particles, inorganic aerosols, burning emissions, molecular-weight, northwest china, Engineering, Environmental Sciences & Ecology
Subject categories Environmental engineering, Environmental Sciences, Ecology


Food-cooking organic aerosols (COA) are one of the primary sources of submicron particulate matter in urban environments. However, there are still many questions surrounding source apportionment related to instrumentation as well as semivolatile partitioning because COA evolve rapidly in the ambient air, making source apportionment more complex. Online measurements of emissions from cooking different types of food were performed in a laboratory to characterize particles and gases. Aerosol mass spectrometer (AMS) measurements showed that the relative ionization efficiency for OA was higher (1.56-3.06) relative to a typical value of 1.4, concluding that AMS is over-estimating COA and suggesting that previous studies likely over-estimated COA concentrations. Food-cooking mass spectra were generated using AMS, and gas and particle food markers were identified with filter inlets for gases and aerosols-chemical ionization mass spectrometer (CIMS) measurements to be used in future food cooking-source apportionment studies. However, there is a considerable variability in both gas and particle markers, and dilution plays an important role in the particle mass budget, showing the importance of using these markers with caution during receptor modeling. These findings can be used to better understand the chemical composition of COA, and they provides useful information to be used in future source-apportionment studies.

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