Particle Phase Acidity and Oligomer Formation in Secondary Organic Aerosol

Citation data:

Environmental Science & Technology, ISSN: 0013-936X, Vol: 38, Issue: 24, Page: 6582-6589

Publication Year:
2004
Usage 243
Abstract Views 243
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Citations 237
Citation Indexes 237
Repository URL:
https://works.bepress.com/song-gao/39; https://nsuworks.nova.edu/cnso_chemphys_facarticles/47
DOI:
10.1021/es049125k
Author(s):
Gao, Song; Ng, Nga L.; Keywood, Melita D.; Varutbangkul, Varuntida; Bahreini, Roya; Nenes, Athanasios; He, Jiwen; Yoo, Kee Y.; Beauchamp, J. L.; Hodyss, Robert P.; Flagan, Richard C.; Seinfeld, John H. Show More Hide
Publisher(s):
American Chemical Society (ACS); NSUWorks
Tags:
Chemistry; Environmental Science; Environmental Chemistry
article description
A series of controlled laboratory experiments are carried out in dual Teflon chambers to examine the presence of oligomers in secondary organic aerosols (SOA) from hydrocarbon ozonolysis as well as to explore the effect of particle phase acidity on SOA formation. In all seven hydrocarbon systems studied (i.e., α-pinene, cyclohexene, 1-methyl cyclopentene, cycloheptene, 1-methyl cyclohexene, cyclooctene, and terpinolene), oligomers with MW from 250 to 1600 are present in the SOA formed, both in the absence and presence of seed particles and regardless of the seed particle acidity. These oligomers are comparable to, and in some cases, exceed the low molecular weight species (MW < 250) in ion intensities in the ion trap mass spectra, suggesting they may comprise a substantial fraction of the total aerosol mass. It is possible that oligomers are widely present in atmospheric organic aerosols, formed through acid- or base-catalyzed heterogeneous reactions. In addition, as the seed particle acidity increases, larger oligomers are formed more abundantly in the SOA; consequently, the overall SOA yield also increases. This explicit effect of particle phase acidity on the composition and yield of SOA may have important climatic consequences and need to be considered in relevant models.