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Abstract. Heterogeneous chemistry of oxidized carbons in aerosol phase is known to significantly contribute to secondary organic aerosol (SOA) burdens. TheUNIfied Partitioning Aerosol phase Reaction (UNIPAR) model was developed to process the multiphase chemistry of various oxygenated organics into SOAmass predictions in the presence of salted aqueous phase. In this study, the UNIPAR model simulated the SOA formation from gasoline fuel, which is amajor contributor to the observed concentration of SOA in urban areas. The oxygenated products, predicted by the explicit mechanism, were lumpedaccording 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 bystoichiometric coefficient array and the consumption of hydrocarbons, were applied to form gasoline SOA via multiphase partitioning andaerosol-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 againstmore »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 SOAwas dominantly produced via aerosol-phase reaction, regardless of the seed conditions owing to heterogeneous reactions of reactive multifunctionalorganic 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.« less

Secondary Organic Aerosol (SOA) from diesel fuel is known to be significantly sourced from the atmospheric oxidation of aliphatic hydrocarbons. In this study, the formation of linear-alkane SOA was predicted using the Unified Partitioning Aerosol Phase Reaction (UNIPAR) model that simulated multiphase reactions of hydrocarbons. In the model, the formation of oxygenated products from the photooxidation of linear alkanes was simulated using a near-explicit gas kinetic mechanism. Autoxidation paths integrated with alkyl peroxy radicals were added to the Master Chemical Mechanismv3.3.1 to improve the formation of low volatility products in the gas phase and better predict SOA mass. The resulting gas products were then classified into volatility-reactivity based lumping groups that are linked to mass-based stoichiometric coefficients. The SOA mass in the UNIPAR model is produced via three major pathways: partitioning of gaseous oxidized products onto both the organic and wet inorganic phases; oligomerization in organic phase; and aqueous reactions (acid-catalyzed oligomerization and organosulfate formation) in the inorganic phase. The model performance was demonstrated for SOA data that were produced through the photooxidation of a homologous series of linear alkanes ranging from C9 to C15 under varying environments (NOx levels, temperature, and inorganic seed conditions) in a large outdoor photochemicalmore »smog chamber. The product distributions of linear alkanes were mathematically predicted as a function of carbon number using an incremental volatility coefficient (IVC) to cover a wide range of alkane lengths. The prediction of alkane SOA using the incremental volatility-based product distributions, which were obtained with C9-C12 alkanes, was evaluated for prediction of C13 and C15 chamber data and further extrapolated to predict the SOA from longer chain alkanes (C15) that can be found in diesel. The model simulation of linear alkanes in diesel fuel suggests that SOA mass is mainly produced by alkanes C15 and higher. Alkane SOA is insignificantly impacted by the aqueous reaction of organic species in the wet inorganic phase due to the hydrophobicity of products, but significantly influenced by gas-particle partitioning.« less

The daytime oxidation of biogenic hydrocarbons is attributed to both OH radicals and O3, while nighttime chemistry is dominated by the reaction with O3 and NO3 radicals. Here, the diurnal pattern of Secondary Organic Aerosol (SOA) originating from biogenic hydrocarbons was intensively evaluated under varying environmental conditions (temperature, humidity, sunlight intensity, NOx levels, and seed conditions) by using the UNIfied Partitioning Aerosol phase Reaction (UNIPAR) model, which comprises multiphase gas-particle partitioning and in-particle chemistry. The oxidized products of three different hydrocarbons (isoprene, α-pinene, and β-caryophyllene) were predicted by using near explicit gas mechanisms for four different oxidation paths (OH, O3, NO3, and O(3P)) during day and night. The gas mechanisms implemented the Master Chemical Mechanism (MCM v3.3.1), the reactions that formed low volatility products via peroxy radical (RO2) autoxidation, and self- and cross-reactions of nitrate-origin RO2. In the model, oxygenated products were then classified into volatility-reactivity base lumping species, which were dynamically constructed under varying NOx levels and aging scales. To increase feasibility, the UNIPAR model that equipped mathematical equations for stoichiometric coefficients and physicochemical parameters of lumping species was integrated with the SAPRC gas mechanism. The predictability of the UNIPAR model was demonstrated by simulating chamber-generated SOA data undermore »varying environments day and night. Overall, the SOA simulation decoupled to each oxidation path indicated that the nighttime isoprene SOA formation was dominated by the NO3-driven oxidation, regardless of NOx levels. However, the oxidation path to produce the nighttime α-pinene SOA gradually transited from the NO3-initiated reaction to ozonolysis as NOx levels decreased. For daytime SOA formation, both isoprene and α-pinene were dominated by the OH-radical initiated oxidation. The contribution of the O(3P) path to all biogenic SOA formation was negligible in daytime. Sunlight during daytime promotes the decomposition of oxidized products via photolysis and thus, reduces SOA yields. Nighttime α-pinene SOA yields were significantly higher than daytime SOA yields, although the nighttime α-pinene SOA yields gradually decreased with decreasing NOx levels. For isoprene, nighttime chemistry yielded higher SOA mass than daytime at the higher NOx level (isoprene/NOx > 5 ppbC/ppb). The daytime isoprene oxidation at the low NOx level formed epoxy-diols that significantly contributed SOA formation via heterogeneous chemistry. For isoprene and α-pinene, daytime SOA yields gradually increased with decreasing NOx levels. The daytime SOA produced more highly oxidized multifunctional products and thus, it was generally more sensitive to the aqueous reactions than the nighttime SOA. β-Caryophyllene, which rapidly oxidized and produced SOA with high yields, showed a relatively small variation in SOA yields from changes in environmental conditions (i.e., NOx levels, seed conditions, and diurnal pattern), and its SOA formation was mainly attributed to ozonolysis day and night. To mimic the nighttime α-pinene SOA formation under the polluted urban atmosphere, α-pinene SOA formation was simulated in the presence of gasoline fuel. The simulation suggested the growth of α-pinene SOA in the presence of gasoline fuel gas by the enhancement of the ozonolysis path under the excess amount of ozone, which is typical in urban air. We concluded that the oxidation of the biogenic hydrocarbon with O3 or NO3 radicals is a source to produce a sizable amount of nocturnal SOA, despite of the low emission at night.« less

The prediction of Secondary Organic Aerosol (SOA) in regional scales is traditionally performed by using gas-particle partitioning models. In the presence of inorganic salted wet aerosols, aqueous reactions of semivolatile organic compounds can also significantly contribute to SOA formation. The UNIfied Partitioning-Aerosol phase Reaction (UNIPAR) model utilizes the explicit gas mechanism to better predict SOA formation from multiphase reactions of hydrocarbons. In this work, the UNIPAR model was incorporated with the Comprehensive Air Quality Model with Extensions (CAMx) to predict the ambient concentration of organic matter (OM) in urban atmospheres during the Korean-United States Air Quality (2016 KORUS-AQ) campaign. The SOA mass predicted with the CAMx-UNIPAR model changed with varying levels of humidity and emissions and in turn, has the potential to improve the accuracy of OM simulations. The CAMx-UNIPAR model significantly improved the simulation of SOA formation under the wet condition, which often occurred during the KORUS-AQ campaign, through the consideration of aqueous reactions of reactive organic species and gas-aqueous partitioning. The contribution of aromatic SOA to total OM was significant during the low-level transport/haze period (24-31 May 2016) because aromatic oxygenated products are hydrophilic and reactive in aqueous aerosols. The OM mass predicted with the CAMx-UNIPAR model wasmore »compared with that predicted with the CAMx model integrated with the conventional two product model (SOAP). Based on estimated statistical parameters to predict OM mass, the performance of CAMx-UNIPAR was noticeably better than the conventional CAMx model although both SOA models underestimated OM compared to observed values, possibly due to missing precursor hydrocarbons such as sesquiterpenes, alkanes, and intermediate VOCs. The CAMx-UNIPAR model simulation suggested that in the urban areas of South Korea, terpene and anthropogenic emissions significantly contribute to SOA formation while isoprene SOA minimally impacts SOA formation.« less