A bottom-up emissions inventory is one of the most important data sets needed to understand air quality (AQ) and climate change (CC). Several emission inventories have been developed for Asia, including Transport and Chemical Evolution over the Pacific (TRACE-P), Regional Emission Inventory in Asia (REAS), and Inter-Continental Chemical Transport Experiment (INTEX) and, while these have been used successfully for many international studies, they have limitations including restricted amounts of information on pollutant types and low levels of transparency with respect to the polluting sectors or fuel types involved. To address these shortcomings, we developed: (1) a base-year, bottom-up anthropogenic emissions inventory for Asia, using the most current parameters and international frameworks (i.e., the Greenhouse gas—Air pollution INteractions and Synergies (GAINS) model); and (2) a base-year, natural emissions inventory for biogenic and biomass burning. For (1), we focused mainly on China, South Korea, and Japan; however, we also covered emission inventories for other regions in Asia using data covering recent energy/industry statistics, emission factors, and control technology penetration. The emissions inventory (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (CREATE)) covers 54 fuel classes, 201 subsectors, and 13 pollutants, namely SO₂, NO_x, CO, non-methane volatile organic compounds (NMVOC), NH₃, OC, BC, PM₁₀, PM_2.5, CO₂, CH₄, N₂O, and Hg. For the base-year natural emissions inventory, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and BlueSky-Asia frameworks were used to estimate biogenic and biomass burning emissions, respectively. Since the CREATE emission inventory was designed/developed using international climate change/air quality (CC/AQ) assessment frameworks, such as GAINS, and has been fully connected with the most comprehensive emissions modeling systems—such as the US Environmental Protection Agency (EPA) Chemical Manufacturing Area Source (CMAS) system—it can be used to support various climate and AQ integrated modeling studies, both now and in the future.

We report the results of year-long PM_2.5 (particulate matter less than 2.5 µm in diameter) simulations over Northeast Asia for the base year of 2013 under the framework of the Long-range Transboundary Air Pollutants in Northeast Asia (LTP) project. LTP is a tripartite project launched by China, Japan, and Korea for cooperative monitoring and modeling of the long-range transport (LRT) of air pollutants. In the modeling aspect in the LTP project, each country’s modeling group employs its own original air quality model and options. The three regional air quality models employed by the modeling groups are WRF-CAMx, NHM-RAQM2, and WRF-CMAQ. PM_2.5 concentrations were simulated in remote exit-and-entrance areas associated with the LRT process over China, Japan, and Korea. The results showed apparent bias that remains unexplored due to a series of uncertainties from emission estimates and inherent model limitations. The simulated PM₁₀ levels at seven remote exit-and-entrance sites were underestimated with the normalized mean bias of 0.4 ± 0.2. Among the four chemical components of PM_2.5 (SO₄²⁻, NO₃⁻, organic carbon (OC), and elemental carbon (EC)), the largest inter-model variability was in OC, with the second largest discrepancy in NO₃⁻. Our simulation results also indicated that under considerable SO₄²⁻ levels, favorable environments for ammonium nitrate formation were found in exit-and-entrance areas between China and Korea, and gas-aerosol partitioning for semi-volatile species of ammonium nitrate could be fully achieved prior to arrival at the entrance areas. Other chemical characteristics, including NO₃⁻/SO₄²⁻ and OC/EC ratios, are discussed to diagnose the LRT characteristics of PM_2.5 in exit-and-entrance areas associated with transboundary transport over China, Japan, and Korea.

In this study, we investigated the possible teleconnection between PM₁₀ concentrations in South Korea and Arctic Sea ice concentrations at inter-annual time scales using observed PM₁₀ data from South Korea, NCEP R2 data, and NOAA Sea Ice Concentration (SIC) data from 2001 to 2018. From the empirical orthogonal function (EOF) analysis, we found that the first mode (TC1) was a large-scale mode for PM₁₀ in South Korea and explained about 27.4% of the total variability. Interestingly, the TC1 is more dominantly influenced by the horizontal ventilation effect than the vertical atmospheric stability effect. The pollution potential index (PPI), which is defined by the weighted average of the two ventilation effects, is highly correlated with the TC1 of PM₁₀ at a correlation coefficient of 0.75, indicating that the PPI is a good measure for PM₁₀ in South Korea at inter-annual time scales. Regression maps show that the decrease of SIC over the Barents Sea is significantly correlated with weakening of high pressure over the Ural mountain range region, the anomalous high pressure at 500 hPa over the Korean peninsula, and the weakening of the Siberian High and Aleutian low. Moreover, these patterns are similar to the correlation pattern with the PPI, suggesting that the variability of SIC over the Barents Sea may play an important role in modulating the variability of PM₁₀ in South Korea through teleconnection from the Barents Sea to the Korean peninsula via Eurasia.

Studies on the characteristics of hazardous air pollutants (HAPs) in the emissions of medium-duty diesel trucks are significantly insufficient compared to those on heavy-duty trucks. This study investigated the characteristics of regulated pollutants and HAPs, such as volatile organic compounds (VOCs), aldehydes, and polycyclic aromatic hydrocarbons (PAHs), and estimated non-methane hydrocarbon (NMHC) speciation in the emissions of medium-duty diesel trucks. Ten medium-duty diesel trucks conforming to Euros 5 and 6 were tested for four various driving cycles (WLTC, NEDC, CVS-75, and NIER-9) using a chassis dynamometer. In an urban area such as Seoul, CO and NMHC emissions were increased because of its longer low-speed driving time. NOx emissions were the highest in the high-speed phase owing to the influence of thermal NOx. PM emissions were almost not emitted because of the DPF installation. Alkanes dominated non-methane volatile organic compound (NMVOC) emissions, 36–63% of which resulted from the low reaction of the diesel oxidation catalyst. Formaldehyde emissions were the highest for 35–53% among aldehydes irrespective of driving cycles. By sampling the particle-phase of PAHs, we detected benzo(k)fluoranthene and benzo(a)pyrene and estimated the concentrations of the gas-phase PAHs with models to obtain the total PAH concentrations. In the particle portion, benzo(k)fluoranthene and benzo(a)pyrene were over 69% and over 91%, respectively. The toxic equivalency quantities of benzo(k)fluoranthene and benzo(a)pyrene from NIER-9 (cold) for both Euro 5 and Euro 6 vehicles were more than five times higher than those of NIER (hot) and NEDC. In the case of NMHC speciation, formaldehyde emissions were the highest for 10–45% in all the driving cycles. Formaldehyde and benzene must be controlled in the emissions of medium-duty diesel trucks to reduce their health threats. The results of this study will aid in establishing a national emission inventory system for HAPs of mobile sources in Korea.