We compare nine emission inventories of nitrogen oxides including four satellite-derived NO_x inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NO_x emissions over mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NO_x emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite-derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.

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.

Korean Photochemical Assessment Monitoring Stations (PAMS) have been established since the late 2001 to monitor ambient air concentrations of VOC species, which would enhance understanding photo-chemical formation of ozone and subsequently contribute to developing efficient ozone control strategies. The present study aims at identifying major VOC species and examining their trends by analyzing PAMS monitoring data collected from the year 2006 to 2016. All the 18 PAMS sites operated by the Ministry of Environment were included in the study. PAMS monitored the 56 target VOC species, which are classified into four groups, alkenes, lower alkanes (C≤3), higher alkanes (C≥4), aromatics. The higher alkanes and aromatics dominated over the lower alkanes and alkenes in the type 2 and 3 PAMS sites except Joongheung site. N-butane was a major alkane species, toluene was a major aromatic species and most of VOCs showed decreasing trends in these sites. On the other hand, only the alkenes showed decreasing trends at the Joongheung site in Yeosu. Major sources of abundant species such as ethane, propane, n-butane, toluene were estimated by analyzing seasonal variations, correlation with other VOC species, and emission profiles. A major source of n-butane was identified as LPG cars, while major sources of toluene varied considerably from one site to another. The lower alkanes were composed of ethane and propane, both of which showed a strong seasonal variation, low in the summer and high in the winter, indicating that a major source might be the heating by gaseous fuels. Ozone formation potentials of VOC species were evaluated by applying MIR and POCP to the measured VOC species concentrations. Toluene contributed the most to total ozone forming potentials followed by m,p-xylene for all the type 2 and 3 PAMS sites except for two sites in Yeosu-Gwangyang. Ethylene and propylene were the first and second contributors to total ozone forming potentials at Joongheung site in Yeosu.

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.