Towards automated inclusion of autoxidation chemistry in models: from precursors to atmospheric implications
In the last few decades, atmospheric formation of secondary organic aerosols (SOA) has gained increasing attention due to their impact on air quality and climate. However, methods to predict their abundance are mainly empirical and may fail under real atmospheric conditions. In this work, a close-to...
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Published in | Environmental science: atmospheres Vol. 4; no. 8; pp. 879 - 896 |
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Main Authors | , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
RSC
08.08.2024
|
Subjects | |
Online Access | Get full text |
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Summary: | In the last few decades, atmospheric formation of secondary organic aerosols (SOA) has gained increasing attention due to their impact on air quality and climate. However, methods to predict their abundance are mainly empirical and may fail under real atmospheric conditions. In this work, a close-to-mechanistic approach allowing SOA quantification is presented, with a focus on a chain-like chemical reaction called "autoxidation". A novel framework is employed to (a) describe the gas-phase chemistry, (b) predict the products' molecular structures and (c) explore the contribution of autoxidation chemistry on SOA formation under various conditions. As a proof of concept, the method is applied to benzene, an important anthropogenic SOA precursor. Our results suggest autoxidation to explain up to 100% of the benzene-SOA formed under low-NO
laboratory conditions. Under atmospheric-like day-time conditions, the calculated benzene-aerosol mass continuously forms, as expected based on prior work. Additionally, a prompt increase, driven by the NO
radical, is predicted by the model at dawn. This increase has not yet been explored experimentally and stresses the potential for atmospheric SOA formation
secondary oxidation of benzene by O
and NO
. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2634-3606 2634-3606 |
DOI: | 10.1039/d4ea00054d |