Simulation of SOA formation using CMAQ model with updated emissions and parameterizations for the KORUS-AQ, GMAP 2021, and SIJAQ 2022 campaigns

Abstract

Secondary Organic Aerosol (SOA), which constitutes a considerable fraction of Particle Matter, is underestimated in chemical transport model (CTM) used for atmospheric modeling caused by uncertainties in SOA parameters in CTM. In this study, to improve the uncertainties of CTM and alleviate the underestimation of organic aerosol (OA), we conducted to CMAQ model using the update of the emissions and the new SOA parameters. First, new yield data through smog chamber experiments under East Asia atmospheric conditions—mixed high-NOx, SO2, NH3, and RH—were applied to volatile organic compound (VOC) precursors: benzene, toluene, xylene, isoprene, and monoterpenes. Second, enthalpies of vaporizations data applied VOC precursors benzene, toluene, xylene, and monoterpenes that we selected as surrogate SOA species using molecular dynamic simulation (MDS). And, in order to reduce the uncertainty of the emission inventories, the monoterpenes of BVOC was increased by 3 times through a sensitivity test. These parameterizations were validated by comparing the observed data from three campaign periods: KORUS-AQ, GMAP 2021, and SIJAQ 2022. We compared the OA concentrations in each campaign period by adding new emissions, new yields, and new enthalpies. Among them, when all parameters were applied, underestimation improved the most. Specifically, for the KORUS-AQ period, the mean bias (MB) from ground observations decreased from -1.29 to 0.02 μg/m³; from aircraft observations, MB decreased from -2.64 to -0.77 μg/m³. Similarly, for the GMAP 2021 period, the MB decreased from -3.53 to -1.62 μg/m³. Furthermore, during the SIJAQ 2022 period, the MB decreased from -2.68 to -1.27 μg/m³. In conclusion, the newly applied SOA parameters of both new yields and enthalpies of vaporization, calculated considering East Asian atmospheric conditions, improved the model's underestimation in all campaigns. In future work, it is necessary to analyze seasonal and meteorological factors of OA using abundant aircraft observation data and observed values of various chemical species.