Testing secondary organic aerosol models using smog chamber data for complex precursor mixtures: influence of precursor volatility and molecular structure
- 1Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, USA
- *now at: Civil and Environmental Engineering, University of California, Davis, CA, USA
Abstract. We use secondary organic aerosol (SOA) production data from an ensemble of unburned fuels measured in a smog chamber to test various SOA formation models. The evaluation considered data from 11 different fuels including gasoline, multiple diesels, and various jet fuels. The fuels are complex mixtures of species; they span a wide range of volatility and molecular structure to provide a challenging test for the SOA models. We evaluated three different versions of the SOA model used in the Community Multiscale Air Quality (CMAQ) model. The simplest and most widely used version of that model only accounts for the volatile species (species with less than or equal to 12 carbons) in the fuels. It had very little skill in predicting the observed SOA formation (R2 = 0.04, fractional error = 108%). Incorporating all of the lower-volatility fuel species (species with more than 12 carbons) into the standard CMAQ SOA model did not improve model performance significantly. Both versions of the CMAQ SOA model over-predicted SOA formation from a synthetic jet fuel and under-predicted SOA formation from diesels because of an overly simplistic representation of the SOA formation from alkanes that did not account for the effects of molecular size and structure. An extended version of the CMAQ SOA model that accounted for all organics and the influence of molecular size and structure of alkanes reproduced the experimental data. This underscores the importance of accounting for all low-volatility organics and information on alkane molecular size and structure in SOA models. We also investigated fitting an SOA model based solely on the volatility of the precursor mixture to the experimental data. This model could describe the observed SOA formation with relatively few free parameters, demonstrating the importance of precursor volatility for SOA formation. The exceptions were exotic fuels such as synthetic jet fuel that expose the central assumption of the volatility-dependent model that most emissions consist of complex mixtures with similar distribution of molecular classes. Despite its shortcomings, SOA formation as a function of volatility may be sufficient for modeling SOA formation in chemical transport models.