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We use secondary organic aerosol (SOA) production data from an ensemble ofunburned fuels measured in a smog chamber to test various SOA formationmodels. The evaluation considered data from 11 different fuels includinggasoline, multiple diesels, and various jet fuels. The fuels are complexmixtures of species; they span a wide range of volatility and molecularstructure to provide a challenging test for the SOA models. We evaluatedthree different versions of the SOA model used in the Community MultiscaleAir Quality (CMAQ) model. The simplest and most widely used version of thatmodel only accounts for the volatile species (species with less than or equal to 12carbons) in the fuels. It had very little skill in predicting the observedSOA formation (<i>R</i><sup>2</sup> = 0.04, fractional error = 108%). Incorporatingall of the lower-volatility fuel species (species with more than 12 carbons)into the standard CMAQ SOA model did not improve model performancesignificantly. Both versions of the CMAQ SOA model over-predicted SOAformation from a synthetic jet fuel and under-predicted SOA formation fromdiesels because of an overly simplistic representation of the SOA formationfrom alkanes that did not account for the effects of molecular size andstructure. An extended version of the CMAQ SOA model that accounted for allorganics and the influence of molecular size and structure of alkanesreproduced the experimental data. This underscores the importance ofaccounting for all low-volatility organics and information on alkanemolecular size and structure in SOA models. We also investigated fitting anSOA model based solely on the volatility of the precursor mixture to theexperimental data. This model could describe the observed SOA formation withrelatively few free parameters, demonstrating the importance of precursorvolatility for SOA formation. The exceptions were exotic fuels such assynthetic jet fuel that expose the central assumption of thevolatility-dependent model that most emissions consist of complex mixtureswith similar distribution of molecular classes. Despite its shortcomings, SOAformation as a function of volatility may be sufficient for modeling SOAformation in chemical transport models

atmospheric chemistry and physics

Testing secondary organic aerosol models using smog chamber data for complex precursor mixtures: influence of precursor volatility and molecular structure