Abstract

The secondary organic aerosol (SOA) formation from photooxidation of gasoline vapor was simulated by using the UNIfied Partitioning Aerosol phase Reaction (UNIPAR) model, which predicted SOA growth via multiphase reactions of hydrocarbons. The Carbon Bond 6 (CB6r3) mechanism was incorporated with the SOA model to estimate the hydrocarbon consumption and the concentration of radicals (i.e., RO2 and HO2), which were closely related to atmospheric aging of gas products. Oxygenated products were lumped according to their volatilities and reactivity and linked to stoichiometric coefficients and their physicochemical parameters, which were dynamically constructed at different NOx levels and degrees of gas aging. To assess the gasoline SOA potential in ambient air, model parameters were corrected for gas–wall partitioning (GWP), which was predicted by a qualitative structure activity relationship for explicit products. The simulated gasoline SOA mass was evaluated against observed data obtained in the UF-APHOR chamber under ambient sunlight. The influence of environmental conditions on gasoline SOA was characterized under varying NOx levels, aerosol acidity, humidity, temperature, and concentrations of aqueous salts and gasoline vapor. Both the measured and simulated gasoline SOA formation was sensitive to seeded conditions (acidity and hygroscopicity) and NOx levels. A considerable difference in SOA mass appeared before and after efflorescence relative humidity in the presence of salted aqueous solution. SOA growth in the presence of aqueous reactions was more impacted by temperature than that in absence of seed. The impact of GWP on SOA formation was generally significant, and it appeared to be higher in the absence of wet salts. We conclude that the SOA model in the corpus with both heterogeneous reactions and the model parameters corrected for GWP is essential to accurately predict SOA mass in ambient air.

Highlights

  • The atmospheric oxidation of hydrocarbons (HCs) produces ozone in the troposphere through a photochemical cycle of nitrogen oxides

  • The oligomers in secondary organic aerosol (SOA) formed from photooxidation of precursor HCs in chambers and the ambient air has been identified as 25 %–80 % of the SOA mass (Kalberer et al, 2004; 2006; Tolocka et al, 2004; Gross et al, 2006; Hallquist et al, 2009)

  • MWin is the averaged molecular weight of inorganic aerosol, and γin,i is the activity coefficient of i in inorganic phase. γor,i is assumed as unity, while γin,i is semi-empirically estimated with a polynomial equation, determined by fitting the γin,i estimated by the aerosol inorganic–organic mixtures functional groups activity coefficient (AIOMFAC) (Zuend et al, 2011): γin,i = e0.035MWi −2.704 ln(O:Ci )−1.121HBi −0.33FS−0.022(RH), (3)

Read more

Summary

Introduction

The atmospheric oxidation of hydrocarbons (HCs) produces ozone in the troposphere through a photochemical cycle of nitrogen oxides. The atmospheric process of HCs can produce semi-volatile oxygenated products that can form secondary organic aerosol (SOA) through either gas–particle partitioning or aerosolphase reactions. The formation of oligomers was considered in the SOA module of Community Multiscale Air Quality (CMAQ) as a first-order reaction of condensed organic species, resulting in the improvement of spatial and temporal trends of SOA mass in particular for biogenic SOA (Carlton et al, 2010). Pye et al (2017) evaluate the importance of aerosol– water–organic interactions in the CMAQ model accounting for the uptake of water onto the hydrophilic organics (Pye et al, 2017) Despite such efforts, the performance of SOA formation in representing spatial and seasonal variation in ambient aerosol tends to underestimate total aerosol mass in the southern and western US (Appel et al, 2021). (RH), seed conditions, and the concentration of HC, was investigated

Chamber experiment
Model descriptions
Lumped organic species
Multiphase partitioning
OMAR: SOA growth via aerosol-phase reactions
OMP: SOA formation via partitioning
Model parameters in the absence of GWP bias
Aromatic SOA simulation with UNIPAR-CB6r3
Gasoline SOA simulation with UNIPAR-CB6r3
Sensitivity and uncertainties
Atmospheric implication
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call