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.

This paper presents the development of the global to mesoscale air quality forecast and analysis system (GMAF) and its application to particulate matter under 2.5 μm (PM_2.5) forecast in Korea. The GMAF combined a mesoscale model with a global data assimilation system by the grid nudging based four-dimensional data assimilation (FDDA). The grid nudging based FDDA developed for weather forecast and analysis was extended to air quality forecast and analysis for the first time as an alternative to data assimilation of surface monitoring data. The below cloud scavenging module and the secondary organic formation module of the community multiscale air quality model (CMAQ) were modified and subsequently verified by comparing with the PM speciation observation from the PM supersite. The observation data collected from the criteria air pollutant monitoring networks in Korea were used to evaluate forecast performance of GMAF for the year of 2016. The GMAF showed good performance in forecasting the daily mean PM_2.5 concentrations at Seoul; the correlation coefficient between the observed and forecasted PM_2.5 concentrations was 0.78; the normalized mean error was 25%; the probability of detection for the events exceeding the national PM_2.5 standard was 0.81 whereas the false alarm rate was only 0.38. Both the hybrid bias correction technique and the Kalman filter bias adjustment technique were implemented into the GMAF as postprocessors. For the continuous and the categorical performance metrics examined, the Kalman filter bias adjustment technique performed better than the hybrid bias correction technique.