Automated mechanism generation. Part 2: application to atmospheric chemistry of alkanes and oxygenates

In this study, an automated mechanism generation framework was applied to atmospheric chemistry of volatile organic compounds (VOCs) and nitrogen oxides (NO x ). The framework generates reactions with minimal input based on a small set of reaction operators and includes a hierarchy for specifying ra...

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Bibliographic Details
Published in:Journal of atmospheric chemistry 2009-06, Vol.63 (2), p.157-186
Main Authors: Khan, Shumaila S, Broadbelt, Linda J
Format: Article
Language:English
Subjects:
Air pollution
Applied sciences
Atmospheric chemistry
Atmospheric pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Automation
Chemical reactions
Earth and Environmental Science
Earth Sciences
Exact sciences and technology
Experimental data
Formaldehyde
Kinetic modeling
Mechanistic modeling
Nitrogen oxides
Organic compounds
Ozone formation
Photochemicals
Pollutants physicochemistry study: properties, effects, reactions, transport and distribution
Pollution
Smog
VOC mixtures
VOCs
Volatile organic compounds
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Summary:In this study, an automated mechanism generation framework was applied to atmospheric chemistry of volatile organic compounds (VOCs) and nitrogen oxides (NO x ). The framework generates reactions with minimal input based on a small set of reaction operators and includes a hierarchy for specifying rate constants for every reaction created. Mechanisms were generated for formaldehyde-air-NO x , acetaldehyde-formaldehyde-n-octane-air-NO x , and acetone-air-NO x , and the model results were compared to experimental data obtained from smog chambers and to the SAPRC-99 lumped models. The models generated captured the experimental data very well, and their mechanistic formulation provided new insights into the controlling reaction pathways to pollutant formation. The approach applied here is sufficiently general that it can be applied to a wide range of alkane and oxygenate mixtures.
ISSN:0167-7764
1573-0662
DOI:10.1007/s10874-010-9162-1