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| Titel |
Testing secondary organic aerosol models using smog chamber data for complex precursor mixtures: influence of precursor volatility and molecular structure |
| VerfasserIn |
S. H. Jathar, N. M. Donahue, P. J. Adams, A. L. Robinson |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 14, no. 11 ; Nr. 14, no. 11 (2014-06-11), S.5771-5780 |
| Datensatznummer |
250118785
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| Publikation (Nr.) |
 copernicus.org/acp-14-5771-2014.pdf |
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| Zusammenfassung |
| 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. |
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