Journal
ATMOSPHERIC CHEMISTRY AND PHYSICS
Volume 22, Issue 1, Pages 625-639Publisher
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/acp-22-625-2022
Keywords
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Funding
- National Science Foundation [AGS1923651]
- National Research Foundation of Korea [2020M3G1A1114556]
- National Institute of Environmental Research [NIER-2020-01-01-010]
- Korea Environmental Industry & Technology Institute (KEITI) [NIER-2020-01-01-010] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2020M3G1A1114556] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This study used the UNIPAR model to simulate the formation of secondary organic aerosol (SOA) from gasoline fuel and evaluated the model using experimental data. The results showed that gasoline SOA formation is mainly driven by aerosol-phase reactions and is sensitive to seed conditions, with a positive correlation with humidity and aerosol acidity. There is also a significant difference in SOA formation potential between arid and humid regions. Additionally, aqueous reactions and temperature have an impact on the formation of gasoline SOA.
Heterogeneous chemistry of oxidized carbons in aerosol phase is known to significantly contribute to secondary organic aerosol (SOA) burdens. The UNIfied Partitioning Aerosol phase Reaction (UNIPAR) model was developed to process the multiphase chemistry of various oxygenated organics into SOA mass predictions in the presence of salted aqueous phase. In this study, the UNIPAR model simulated the SOA formation from gasoline fuel, which is a major contributor to the observed concentration of SOA in urban areas. The oxygenated products, predicted by the explicit mechanism, were lumped according to their volatility and reactivity and linked to stoichiometric coefficients which were dynamically constructed by predetermined mathematical equations at different NOx levels and degrees of gas aging. To improve the model feasibility in regional scales, the UNIPAR model was coupled with the Carbon Bond 6 (CB6r3) mechanism. CB6r3 estimated the hydrocarbon consumption and the concentration of radicals (i.e., RO2 and HO2) to process atmospheric aging of gas products. The organic species concentrations, estimated by stoichiometric coefficient array and the consumption of hydrocarbons, were applied to form gasoline SOA via multiphase partitioning and aerosol-phase reactions. To improve the gasoline SOA potential in ambient air, model parameters were also corrected for gas-wall partitioning (GWP). The simulated gasoline SOA mass was evaluated against observed data obtained in the University of Florida Atmospheric PHotochemical Outdoor Reactor (UF-APHOR) chamber under varying sunlight, NOx levels, aerosol acidity, humidity, temperature, and concentrations of aqueous salts and gasoline vapor. Overall, gasoline SOA was dominantly produced via aerosol-phase reaction, regardless of the seed conditions owing to heterogeneous reactions of reactive multifunctional organic products. Both the measured and simulated gasoline SOA was sensitive to seed conditions showing a significant increase in SOA mass with increasing aerosol acidity and water content. A considerable difference in SOA mass appeared between two inorganic aerosol states (dry aerosol vs. wet aerosol) suggesting a large difference in SOA formation potential between arid (western United States) and humid regions (eastern United States). Additionally, aqueous reactions of organic products increased the sensitivity of gasoline SOA formation to NOx levels as well as temperature. The impact of the chamber wall on SOA formation was generally significant, and it appeared to be higher in the absence of wet salts. Based on the evaluation of UNIPAR against chamber data from 10 aromatic hydrocarbons and gasoline fuel, we conclude that the UNIPAR model with both heterogeneous reactions and the model parameters corrected for GWP can improve the ability to accurately estimate SOA mass in regional scales.
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