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.

The Korea-United States Air Quality Study (KORUS-AQ) took place in spring 2016 to better understand air pollution in Korea. In support of KORUS-AQ, 2554 whole air samples (WAS) were collected aboard the NASA DC-8 research aircraft and analyzed for 82 C1–C10 volatile organic compounds (VOCs) using multi-column gas chromatography. Together with fast-response measurements from other groups, the air samples were used to characterize the VOC composition in Seoul and surrounding regions, determine which VOCs are major ozone precursors in Seoul, and identify the sources of these reactive VOCs. (1) The WAS VOCs showed distinct signatures depending on their source origins. Air collected over Seoul had abundant ethane, propane, toluene and n-butane while plumes from the Daesan petrochemical complex were rich in ethene, C2–C6 alkanes and benzene. Carbonyl sulfide (COS), CFC-113, CFC-114, carbon tetrachloride (CCl4) and 1,2-dichloroethane were good tracers of air originating from China. CFC-11 was also elevated in air from China but was surprisingly more elevated in air over Seoul. (2) Methanol, isoprene, toluene, xylenes and ethene were strong individual contributors to OH reactivity in Seoul. However methanol contributed less to ozone formation based on photochemical box modeling, which better accounts for radical chemistry. (3) Positive Matrix Factorization (PMF) and other techniques indicated a mix of VOC source influences in Seoul, including solvents, traffic, biogenic, and long-range transport. The solvent and traffic sources were roughly equal using PMF, and the solvents source was stronger in the KORUS-AQ emission inventory. Based on PMF, ethene and propene were primarily associated with traffic, and toluene, ethylbenzene and xylenes with solvents, especially non-paint solvents for toluene and paint solvents for ethylbenzene and xylenes. This suggests that VOC control strategies in Seoul could continue to target vehicle exhaust and paint solvents, with additional regulations to limit the VOC content in a variety of non-paint solvents.

Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM_2.5) air quality, but this requires better understanding of the AOD–PM_2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM_2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM_2.5 composition, PM₁₀, and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µm with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM_2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM_2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM_2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM_2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM_2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions.

In this work, we investigate the NO_x emissions inventory in Seoul, South Korea, using a regional ozone monitoring instrument (OMI) NO₂ product derived from the standard NASA product. We first develop a regional OMI NO₂ product by recalculating the air mass factors using a high-resolution (4 km × 4 km) WRF-Chem model simulation, which better captures the NO₂ profile shapes in urban regions. We then apply a model-derived spatial averaging kernel to further downscale the retrieval and account for the subpixel variability. These two modifications yield OMI NO₂ values in the regional product that are 1.37 times larger in the Seoul metropolitan region and >2 times larger near substantial point sources. These two modifications also yield an OMI NO₂ product that is in better agreement with the Pandora NO₂ spectrometer measurements acquired during the South Korea–United States Air Quality (KORUS-AQ) field campaign. NO_x emissions are then derived for the Seoul metropolitan area during the KORUS-AQ field campaign using a top-down approach with the standard and regional NASA OMI NO₂ products. We first apply the top-down approach to a model simulation to ensure that the method is appropriate: the WRF-Chem simulation utilizing the bottom-up emissions inventory yields a NO_x emissions rate of 227±94 kt yr⁻¹, while the bottom-up inventory itself within a 40 km radius of Seoul yields a NO_x emissions rate of 198 kt yr⁻¹. Using the top-down approach on the regional OMI NO₂ product, we derive the NO_x emissions rate from Seoul to be 484±201 kt yr⁻¹, and a 353±146 kt yr⁻¹ NO_x emissions rate using the standard NASA OMI NO₂ product. This suggests an underestimate of 53 % and 36 % in the bottom-up inventory using the regional and standard NASA OMI NO₂ products respectively. To supplement this finding, we compare the NO₂ and NO_y simulated by WRF-Chem to observations of the same quantity acquired by aircraft and find a model underestimate. When NO_x emissions in the WRF-Chem model are increased by a factor of 2.13 in the Seoul metropolitan area, there is better agreement with KORUS-AQ aircraft observations and the recalculated OMI NO₂ tropospheric columns. Finally, we show that by using a WRF-Chem simulation with an updated emissions inventory to recalculate the air mass factor (AMF), there are small differences (∼8 %) in OMI NO₂ compared to using the original WRF-Chem simulation to derive the AMF. This suggests that changes in model resolution have a larger effect on the AMF calculation than modifications to the South Korean emissions inventory. Although the current work is focused on South Korea using OMI, the methodology developed in this work can be applied to other world regions using TROPOMI and future satellite datasets (e.g., GEMS and TEMPO) to produce high-quality region-specific top-down NO_x emissions estimates.

High levels of fine particulate matter (PM_2.5) pollution in East Asia often exceed local air quality standards. Observations from the Korea–United States Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM_2.5. Atmospheric models often have difficulty simulating PM_2.5 chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM_2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. At the surface, the model underestimates sulfate by −64 % but overestimates nitrate by +36 %. The largest underestimate in sulfate occurs during the pollution event, for which models typically struggle to generate elevated sulfate concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100 % against aircraft observations. We hypothesize that this is due to a large missing sink, which we implement here as a factor of 5 increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape, resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH₃ could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO₂ hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N₂O₅ hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model's inability to simulate the buildup of PM_2.5 during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO₂ is added to the model, which previously only considered aqueous production of sulfate from SO₂ in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO₂ simulation, implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM_2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO₂ decreases the fraction of PM_2.5 attributable to long-range transport from 66 % to 54 %. Locally produced sulfate increased from 1 % to 25 % of locally produced PM_2.5, implying that local emissions controls could have a larger effect than previously thought. However, this additional uptake of SO₂ is coupled to the model nitrate prediction, which affects the aerosol liquid water abundance and chemistry driving sulfate–nitrate–ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO₂ to aerosol and simple improvements to the model nitrate simulation results in 30 % less sulfate due to 40 % less nitrate and aerosol water, and this results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and nighttime boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate + nitrate + ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM_2.5 in South Korea to ensure continued air quality improvements.