May 8, 2012
Journal Article

COBRA: A Computational Brewing Application for Predicting the Molecular Composition of Organic Aerosols

Abstract

Atmospheric organic aerosols (OA) represent a significant fraction of airborne particulate matter and can impact climate, visibility, and human health. These mixtures are difficult to characterize experimentally due to the enormous complexity and dynamic nature of their chemical composition. We introduce a novel Computational Brewing Application (COBRA) and apply it to modeling oligomerization chemistry stemming from condensation and addition reactions of monomers pertinent to secondary organic aerosol (SOA) formed by photooxidation of isoprene. COBRA uses two lists as input: a list of chemical structures comprising the molecular starting pool, and a list of rules defining potential reactions between molecules. Reactions are performed iteratively, with products of all previous iterations serving as reactants for the next one. The simulation generated thousands of molecular structures in the mass range of 120-500 Da, and correctly predicted ~70% of the individual SOA constituents observed by high-resolution mass spectrometry (HR-MS). Selected predicted structures were confirmed with tandem mass spectrometry. Esterification and hemiacetal formation reactions were shown to play the most significant role in oligomer formation, whereas aldol condensation was shown to be insignificant. COBRA is not limited to atmospheric aerosol chemistry, but is broadly applicable to the prediction of reaction products in other complex mixtures for which reasonable reaction mechanisms and seed molecules can be supplied by experimental or theoretical methods.

Revised: September 24, 2012 | Published: May 8, 2012

Citation

Fooshee D.R., T.B. Nguyen, S.A. Nizkorodov, J. Laskin, A. Laskin, and P. Baldi. 2012. COBRA: A Computational Brewing Application for Predicting the Molecular Composition of Organic Aerosols. Environmental Science & Technology 46, no. 11:6048-6055. PNNL-SA-85407. doi:10.1021/es3003734