Modeling of secondary organic aerosols (SOA) based on two commonly used air quality models in China: Consistent S/IVOCs contribution but large differences in SOA aging

Secondary organic aerosol (SOA) is an important component of atmospheric fine particulate matter (PM2.5), with contributions from anthropogenic and biogenic volatile organic compounds (AVOC and BVOC) and semi- (SVOC) and intermediate volatility organic compounds (IVOC). Policymakers need to know which SOA precursors are important but accurate simulation of SOA magnitude and contributions remain uncertain. Findings from existing SOA modeling studies have many inconsistencies due to differing emission inventory methodologies/assumptions, air quality model (AQM) algorithms, and other aspects of study methodologies. To address some of the inconsistencies, we investigated the role of different AQM SOA algorithms by applying two commonly used models, CAMx and CMAQ, with consistent emission inventories to simulate SOA concentrations and contributions for July and November 2018 in China. Both models have a volatility basis set (VBS) SOA algorithm but with different parameters and treatments of SOA photochemical aging. SOA generated from BVOC (i.e., BSOA) is found to be more important in southern China. In contrast, SOA generated from anthropogenic precursors is more prevalent in the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin and Central China. Both models indicate negligible SOA formation from SVOC emissions compared to other precursors. In July, when BVOC emissions are abundant, SOA is predominantly contributed by BSOA (except for NCP), followed by IVOC-SOA (i.e., SOA produced from IVOC) and ASOA (i.e., SOA produced from anthropogenic VOC). In contrast, in November, IVOC became the leading SOA contributor for all selected regions except PRD, illustrating the important contribution of IVOC emissions to SOA formation. While both models generally agree in terms of the spatial distributions and seasonal variations of different SOA components, CMAQ tends to predict higher BSOA, while CAMx generates higher ASOA concentrations. As a result, CMAQ results suggest that BSOA concentration is always higher than ASOA in November, while CAMx emphasizes the importance of ASOA. Utilizing a conceptual model, we found that different treatment of SOA aging between the two models is a major cause of differences in simulated ASOA concentrations. The step-wise SOA aging scheme implemented in the CAMx VBS (based on gas-phase reactions with OH radical and similar to other models) exhibits a strong enhancement effect on simulated ASOA concentrations, and this effect increases with the ambient organic aerosol (OA) concentrations. The CMAQ aerosol module implements a different SOA aging scheme that represents particle-phase oligomerization and has smaller impacts on total OA. Different structures and/or parameters of the SOA aging schemes are being used in current models, which could greatly affect model simulations of OA in ways that are difficult to anticipate. Our results indicate that future control policies should aim at reducing IVOC emissions as well as traditional VOC emissions. In addition, aging schemes are the major driver in CMAQ vs. CAMx treatments of ASOA and their resulting predicted mass. More sophisticated measurement data (e.g., with resolved OA components) and/or chamber experiments (e.g., investigating how aging influences SOA yields) are needed to better characterize SOA aging and constrain model parameterizations.