Compositional Analysis of Adsorbed Organic Aerosol on a Microresonator Mass Sensor View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-09

AUTHORS

Arthur T. Zielinski, Steven J. Campbell, Ashwin A. Seshia, Roderic L. Jones, Markus Kalberer, Chiara Giorio

ABSTRACT

Aerosol mass measurements are a key air pollution parameter that is regulated in most countries. Beyond mass measurements, the precise composition of the aerosol is essential in identifying sources and impacts on health and climate. The conventional method for simultaneously quantifying mass and composition is to collect aerosol onto filter or impactor samples followed by laboratory analysis. This approach requires long collection times—providing poor time resolution for mass measurements—and long sample preparation prior to analysis. The first limitation can be circumvented with microresonators, which are novel particulate mass sensors with high mass sensitivities and time resolutions. In addition, direct surface analysis techniques, like liquid extraction surface analysis mass spectrometry (LESA–MS), shorten sample preparation times. This work combines, for the first time, the high time resolution mass measurements of a microresonator with the integrated compositional analysis of LESA–MS. Laboratory-produced secondary organic aerosol were collected onto a microresonator via impaction with LESA–MS being used to analyze the chemical composition afterwards. The results were compared with classic filter extraction methods and literature with the final spectra matching the expected reaction products. The combined technique demonstrates an extension to current microresonator applications and illustrates their potential for ambient aerosol studies. More... »

PAGES

118-129

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41810-018-0029-1

DOI

http://dx.doi.org/10.1007/s41810-018-0029-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105106731


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RDF/XML is a standard XML format for linked data.

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