Abstract
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.
Highlights
Secondary organic aerosol (SOA) is aerosol mass formed from the oxidation of gas-phase organic species emitted by natural and anthropogenic sources
We compared predictions from two different types of SOA models to published data from smogchamber experiments conducted with different types of unburned fuel (Jathar et al, 2013): (1) three different variants of the SOA model that used volatility- and molecular structure-resolved schemes used in the Community Multiscale Air Quality (CMAQ) model and other chemical transport models and (2) a volatility-dependent model that relates SOA production only to the precursor volatility and ignores molecular structure
Jathar et al (2013) present data from twenty-three highNOx photo-oxidation experiments conducted in the Carnegie Mellon University smog chamber to quantify the SOA formation from eleven different fuels
Summary
Secondary organic aerosol (SOA) is aerosol mass formed from the oxidation of gas-phase organic species emitted by natural and anthropogenic sources. Pye and Seinfeld (2010) proposed a single-step mechanism for SVOC where the products of oxidation were two orders of magnitude lower in volatility than the precursor and used SOA mass-yield data for naphthalene as a surrogate for unspeciated IVOCs These studies have adopted somewhat different approaches, they all show that including unspeciated organics in SOA models helps close large gaps between predicted and measured SOA mass concentrations (Shrivastava et al, 2008; Tsimpidi et al, 2009; Dzepina et al, 2010; Pye and Seinfeld, 2010; Jathar et al, 2011). We compared predictions from two different types of SOA models to published data from smogchamber experiments conducted with different types of unburned fuel (Jathar et al, 2013): (1) three different variants of the SOA model that used volatility- and molecular structure-resolved schemes used in the Community Multiscale Air Quality (CMAQ) model and other chemical transport models and (2) a volatility-dependent model that relates SOA production only to the precursor volatility and ignores molecular structure
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