Modeling of Organic Aerosol in Seoul Using CMAQ with AERO7

Abstract

The Community Multiscale Air Quality (CMAQ) model with the 7th generation aerosol module (AERO7) was employed to simulate organic aerosol (OA) in Seoul, Korea, for the year 2016. The goal of the present study includes the 1-year simulation of OA using WRF-CMAQ with recently EPA-developed AERO7 with pcVOC (potential VOC from combustion) scale factor revision and analysis of the seasonal behavior of OA surrogate species in Seoul. The AERO7, the most recent version of the aerosol module of the CMAQ model, includes a new secondary organic aerosol (SOA) species, pcSOA (potential SOA from combustion), to resolve the inherent under-prediction problem of OA. The AERO7 classified OA into three groups: primary organic aerosol (POA), anthropogenic SOA (ASOA), and biogenic SOA (BSOA). Each OA group was further classified into 6~15 individual OA surrogate species according to volatility and oxygen content to model the aging of OA and the formation of SOA. The hourly emissions of POA and SOA precursors were compiled and fed into the CMAQ to successfully simulate seasonal variations of OA compositions and ambient organic-matter to organic-carbon ratios (OM/OC). The model simulation showed that the POA and ASOA were major organic groups in the cool months (from November to March) while BSOA was a major organic group in the warm months (from April to October) in Seoul. The simulated OM/OCs ranged from 1.5~2.1 in Seoul, which agreed well with AMS measurements in Seoul in May 2016.

The formation and transport of HNO3 over the Yellow Sea and its impact on the January 2018 PM2.5 episode in Seoul

“…Our previous works examined the performances of the current implementation of the CMAQ and the WRF, 16,20 and therefore in this work, we focused on performance review on meteorological and chemical parameters closely related to the PM 2.5 episode, which included PM 2.5 , nitrate, NO 2 , and O 3 . Table 2 shows the model performance for predicting hourly wind speed, PM 2.5 , nitrate, NO 2 , and O 3 concentrations with statistical metrics recommended by Emery et al (2001; 2017): correlation coefficient ( R ), mean bias (MB), and root mean square error (RMSE) for wind speed, and R , normalized mean bias (NMB), and normalized mean error (NME) for PM 2.5 , nitrate, NO 2 , and O 3 .…”

“…Detailed descriptions of the CMAQ modification and the physical parameterization options selected for the WRF were presented in our previous works. 16,20 Global analysis and forecast data were imported to generate initial and boundary conditions and a grid nudging-based Four-Dimensional Data Assimilation (FDDA) for the CMAQ as well as WRF was carried out; the National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analysis data with 0.25° grid resolution was used for the WRF and the Copernicus Atmosphere Monitoring Service (CAMS) forecast and reanalysis system with 0.4° grid resolution from European Centre for Medium-range Weather Forecasts (ECMWF) for the CMAQ.…”