2013 · 12 citations · 44 references
Natural SciencesChemical EngineeringChemistryAir PollutionAtmospheric ChemistryChemical KineticsAerosol ChemistryAerosol ScienceChemical Kinetics ModelingKinetic ModelingSecondary Aerosol FormationSecondary Organic AerosolsAerosol PartitioningAromatic HydrocarbonsOrganic AerosolsRegional Air Quality ModelingSecondary Aerosol Formation ModelingSecondary Organic Aerosol FormationStoichiometric CoefficientsStoichiometric Modeling
Abstract. The Unified Partitioning-Aerosol phase Reaction (UNIPAR) model has been developed to predict the secondary organic aerosol (SOA) formation through multiphase reactions. An explicit gas-kinetic model was employed to express gas-phase oxidation of aromatic hydrocarbons. Gas-phase products are grouped based on volatility (6 levels) and reactivity (5 levels) and used to construct the stoichiometric coefficients (αi,j) matrix, the set of parameters used to describe the concentrations of organic compounds in multiphase. Weighting of the αi,j matrix as a function of NOx improved the evaluation of NOx effects on SOA. The total amount of organic matter (OMT) is predicted by two modules in the UNIPAR model: OMP by a partitioning process and OMAR by aerosol-phase reactions. OMP is estimated using the SOA partitioning model that has been used in a regional air quality model (CMAQ 5.0.1). OMAR predicts multiphase reactions of organic compounds, such as oligomerization, acid-catalyzed reactions, and organosulfate (OS) formation. The model was evaluated with the SOA data produced from the photooxidation of toluene and 1,3,5-trimethylbenzene using an outdoor reactor (UF-APHOR chamber). The model reasonably simulates SOA formation under various aerosol acidities, NOx concentrations, humidities and temperatures. Furthermore, the OS fraction in the SOA predicted by the model was in good agreement with the experimentally measured OS fraction.
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Gas/Particle Partitioning and Secondary Organic Aerosol Yields
Jay R. Odum, Thorsten Hoffmann, Frank Bowman et al. · Environmental Science & Technology · 1996 · 1.8K citations