Search

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

Efforts to reduce greenhouse gas emissions are ongoing in many East Asian countries; however, their influences on future drought remain unknown. We analyzed future changes in drought intensity over East Asia using the Representative Concentration Pathway (RCP) 8.5 and the new two limiting local greenhouse gas emission scenarios in East Asia: National Institute of Environmental Research (NIER)-H scenario for high emission and NIER-L scenario for low emission. The two scenarios were made by a fully coupled global climate model with prescribed gas boundaries of high and low greenhouse gas emissions from an atmospheric chemistry model reflecting the up-to-date environmental policies of South Korea, China, and Japan. The application of a clustering analysis to the calculated drought index time series identified four zonally distributed drought regions in the present-day period (1951 - 2016). Among these regions, higher latitude regions showed a more rapidly increasing trend in drought intensity than other regions for the present-day period. All future scenarios projected the continuation of this meridional intensification trend of drought until 2100; however, the intensification rates in the NIER-L were much smaller than those in the RCP8.5 and NIER-H scenarios. Our results suggest that reducing greenhouse gas emissions is critical for East Asian countries to alleviate the potential damages of future droughts.

Atmospheric ammonia is a significant pollutant throughout the year, necessitating standardized measurement and identification of emission factors. We performed a quantized evaluation of ammonia concentrations at various locations in and around Seoul, South Korea. The established testing methods of the Radiello Passive Sampler were used for ammonia sampling, and the method was validated using annular denuder sampling. Urban and suburban areas were studied to gain a deeper understanding of the factors responsible for ammonia pollution. This study aimed to establish the fluctuations in concentration over one year, by analyzing the seasonal and regional variation in ammonia concentration. Livestock and agricultural areas recorded the highest concentration of ammonia among all sites, with the highest concentration recorded in autumn. However, at most of the other studied sites, the highest and lowest ammonia concentrations were recorded during summer and winter, respectively. This study attempted to establish a correlation between ammonia concentration and temperature, as well as ammonia concentration and altitude.

We use the CAMx (Comprehensive Air Quality Model with Extensions) chemical transport model (CTM) with 4-km horizontal resolution over the Korean Peninsula to investigate source contributions to PM2.5 in Korea from domestic and upwind sources. We modeled 2015 and 2016 to account for meteorological variation with Korean emissions from the Clean Air Policy Supporting System (CAPSS), meteorology from WRF (Weather, Research, and Forecasting) model, and regional boundary concentrations from the GEOS-Chem global CTM. The CAMx particulate source apportionment technology (PSAT) provided PM2.5 source contributions from 5 source sectors and 6 geographic regions within Korea, international sources, and boundary concentrations. PM2.5 contributions from outside Korea are important with boundary concentrations plus the “other” emissions sector (includes marine shipping, agricultural ammonia, and international emissions from North Korea and Japan within the CAMx domain) contributing 67% of annual average PM2.5 in Seoul in 2016 and 71% in 2015. The boundary concentrations contributed between 30% and 50% of PM2.5 at different Korean cities with contributions generally lower in 2016 than in 2015. For Korean sources, PM2.5 contributions from Electric Generating Unit (EGU) emissions were smaller than contributions from mobile and industrial emissions sources although there is considerable day-to-day variation in contributions. On an annual basis in 2016, the “other” category contributed 25% followed by mobile sources at 23%, industrial sources at 6%, and EGU sources at 3%. For 2015, the contributions were similar. Focusing on March when PM2.5 concentrations were higher than other months, the contributions from other, mobile, industrial, and EGUs were 21%, 18%, 4%, and 4%, respectively in 2016. For 2015, contributions from these four categories were 18%, 15%, 3%, and 3%, respectively.

The air quality in Republic of Korea, especially in cities such as Seoul, has been a serious public health concern over the years. The key pollutant in the atmosphere leading to poor air quality in Korea is fine particulate matter (PM2.5). Here, we use a 3-D global chemistry model (GEOS-Chem) to conduct source attribution to PM2.5 in Korea from international and domestic emissions. The modeling was done for 2015 and 2016 to account for different meteorological conditions. We ran the GEOS-Chem model for both years, conducted model evaluation using ground and aloft observations, and then conducted sensitivity simulations without domestic anthropogenic emissions and Chinese anthropogenic emissions, respectively. Results show that the Chinese influence on PM2.5 in Korea varies from month to month with the highest contribution during spring when observed concentrations are also the highest. Chinese contributions to PM2.5 concentrations in South Korea reach a maximum of up to ~60% in January and February and gradually decrease until August when they reach a minimum at about 20%. On an annual basis, our analysis estimated that in 2016, Chinese anthropogenic emissions contributed 45% to PM2.5 in South Korea. The 2016 contribution from China was generally 3–5% lower than in 2015 because of emissions reductions in China. Compared to the Chinese contribution, the rest of the world contributions (which also include contributions from natural emissions worldwide) were minor except for summer in the South Sea.

The Comprehensive Automobile Research System (CARS) is an open-source python-based automobile emissions inventory model designed to efficiently estimate high quality emissions from motor-vehicle emission sources. It can estimate the criteria air pollutants, greenhouse gases, and air toxics in various temporal resolutions at the national, state, county, and any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road link-level network Geographic Information System (GIS) information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's (EEA) onroad automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from onroad automobile sources. It can optionally utilize road link-specific average speed distribution (ASD) inputs to reflect more realistic vehicle speed variations by road type than a road-specific single averaged speed approach. Also, utilizing high-resolution road GIS data allows the CARS to estimate the road link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a moderate increase of VOC (33 %), CO (52 %), and fine particulate matter (PM2.5) (15 %) emissions while NOx and SOx are reduced by 24 % and 17 % in the CARS estimates. The main differences are driven by the usage of different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but wasn’t implemented in Korea’s CAPSS mobile emissions inventory. While 52% of vehicles use gasoline fuel and 35 % use diesel, gasoline vehicles only contribute 7.7 % of total NOx emissions while diesel vehicles contribute 85.3 %. But for VOC emissions, gasoline vehicles contribute 52.1 % while diesel vehicles are limited to 23 %. While diesel buses are only 0.3 % of vehicles, each vehicle has the largest contribution to NOx emissions (8.51 % of NOx total) due to its longest daily VKT. For VOC, CNG buses are the largest contributor with 19.5 % of total VOC emissions. It indicates that the CNG bus is better for the rural area while the diesel bus is better applicable for the urban area for a better ozone control strategy because the rural area is usually NOx limited for ozone formation and urban area is VOC limited region. For primary PM2.5, more than 98.5 % is from diesel vehicles. The CARS model's in-depth analysis feature can assist government policymakers and stakeholders develop the best emission abatement strategies.

Geostationary satellite sensors over East Asia (GOCI and AHI) are now providing continuous mapping of aerosol optical depth (AOD) at 550 nm to improve monitoring of fine particulate matter (PM2.5) air quality. Here we evaluate our understanding of the physical relationships between AOD and PM2.5 over East Asia by using the GEOS-Chem atmospheric chemistry model to simulate observations from multiple sources: 1) the joint NASA-NIER Korea – United States Air Quality aircraft campaign over South Korea (KORUS-AQ; May–June 2016); 2) AODs from the AERONET ground-based network; 3) AOD from a new GOCI/AHI fused product; and 4) surface PM2.5 networks in South Korea and China. 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 the high hygroscopicity of the PBL aerosols. Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. A broader analysis of the GOCI/AHI AOD data over East Asia in different seasons shows agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. 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 PM2.5 over South Korea is much weaker, reflecting weaker variation of PBL depth and lack of residential coal emissions. Physical interpretation of the satellite AOD data in terms of surface PM2.5 is sensitive to accurate information on aerosol size distributions, PBL depths, RH, the role of coarse particles, and diurnal variation of PM2.5.

The emission of pollution from Chinese point sources, and its migration to Korea, downwind of China, is an important issue in terms of Korean air quality management. Therefore, this study intended to improve the Asian Emission Inventory, NIER/KU-CREATE, by improving information about the point source emissions, their location, and discharge conditions, in China. By using the Global dataset, we can establish point source location information for Coal Power Plants and Non-Coal Power Plants in the Chinese power sector, and calculate and allocate emissions in the inventory. In addition, the emission inventory of NH3, as a by-product of NOx abatement processing, was regarded as being insufficiently characterized in the previous emission inventory, and has been re-estimated. As a result, 3798 LPSs in China were identified, and NH3 emissions from those power plants have increased significantly. In addition, the vertical distribution of emissions through the allocation stack height parameters showed atmospheric injection heights of between 250 m and 1000 m. Using this information, HY-SPLIT trajectory modeling found that during periods of active westerly from China to Korea, emissions from the higher injection height were more prone to move towards Korea, than those from the lower height.

High concentrations of PM2.5 have become a serious environmental issue in South Korea, which ranked 1st or 2nd among OECD countries in terms of population exposure to PM2.5. Quantitative understanding of PM2.5 source attribution is thus crucial for developing efficient air quality mitigation strategies. Here we use a suite of extensive observations of PM2.5 and its precursors concentrations during the international KORea-US cooperative Air Quality field study in Korea (KORUS-AQ) in May–June 2016 to investigate source contributions to PM2.5 in South Korea under various meteorological conditions. For the quantitative analysis, we updated a 3-D chemical transport model, GEOS-Chem, and its adjoint with the latest regional emission inventory and other recent findings. The updated model is evaluated by comparing against observed daily PM2.5 and its component concentrations from six ground sites (Bangnyung, Bulkwang, Olympic park, Gwangju, Ulsan, and Jeju). Overall, simulated concentrations of daily PM2.5 and its components are in a good agreement with observations over the peninsula. We conduct an adjoint sensitivity analysis for simulated surface level PM2.5 concentrations at five ground sites (except for Bangnyung because of its small population) under four different meteorological conditions: dynamic weather, stagnant, extreme pollution, and blocking periods. Source contributions by regions vary greatly depending on synoptic meteorological conditions. Chinese contribution accounts for almost 68% of PM2.5 in surface air in South Korea during the extreme pollution period of the campaign, whereas an enhanced contribution from domestic sources (57%) occurs for the blocking period. Results from our sensitivity analysis suggest that the reduction of domestic anthropogenic NH3 emissions could be most effective in reducing population exposure to PM2.5 in South Korea (effectiveness = 14%) followed by anthropogenic SO2 emissions from Shandong region (effectiveness = 11%), domestic anthropogenic NOx emissions (effectiveness = 10%), anthropogenic NH3 emissions from Shandong region (effectiveness = 8%), anthropogenic NOx emissions from Shandong region (effectiveness = 7%), domestic anthropogenic OC emissions (effectiveness = 7%), and domestic anthropogenic BC emissions (effectiveness = 5%).

In this paper we forecast energy demand at the local level using a new two-dimensional downscaling methodology that respects regional and subnational variation and, as well as consistency at the aggregate level. This methodology combines the one-dimensional statistical downscaling and the Iterative Proportional Fitting (IPF) procedure. Previous projection studies based on downscaling methods have focused on allocating one-dimensional quantity indicators such as GDP, population, and emissions, into geographically or administratively lower levels. In case of energy demand projections, however, downscaling involves not only regional allocation of energy demand quantities but also energy mixes of the demands in the regions. Our novel two-dimensional downscaling methodology allows us to downscale national energy projections in both demand quantities and their energy mix to regional levels by combining the statistical downscaling and the IPF procedure. We illustrate the methodology by deriving energy demand projections for provinces and metropolitan cities in Korea from national scenarios. In addition, we provide two more simple applications of the IPF to show utilization potentials of the IPF in energy demand forecasting and statistics.

In this work, we investigate the NOx 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. NOx 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 NOx emissions rate of 227±94 kt yr −1 , while the bottom-up inventory itself within a 40 km radius of Seoul yields a NOx emissions rate of 198 kt yr −1 . Using the top-down approach on the regional OMI NO2 product, we derive the NOx emissions rate from Seoul to be 484±201 kt yr −1 , and a 353±146 kt yr −1 NOx 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 NOy simulated by WRF-Chem to observations of the same quantity acquired by aircraft and find a model underestimate. When NOx 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 NOx emissions estimates.

A combination of mesoporous silica with an LaCuS2-graphene composite has been prepared by a self-assembly method in support of the CTAB surfactant as the structure creator and basic medium. The characterization was investigated using XRD, SEM, TEM, HR-TEM, EDS, XPS, Raman, and DRS analysis. The pore size of the as-fabricated composite was calculated to be 5.83 nm by the nitrogen adsorption/desorption isotherms. In addition, the photocatalytic properties revealed that the mesoporous SiO2/LaCuS2-graphene catalyst presented a good result for the photocatalytic activity of organic dyes and gallic acid under visible light irradiation. Moreover, the as-prepared mesoporous SiO2/LaCuS2-graphene nanocomposite exhibited a good photodegradation rate for a cationic organic dye group under a pH medium of 11. The photocatalytic performance of the phenol compound representation presented a good result, and the optimum amount of the catalytic for the gallic acid photocatalytic activity is 0.05 g. In addition, the mesoporous SiO2/LaCuS2-graphene catalyst can be exploited for the application of solar energy conversion with high hydrogen production capacity.

The Korea-United States Air Quality (KORUS-AQ) field study was conducted during May–June 2016 to understand the factors controlling air quality in South Korea. Extensive aircraft and ground network observations from the campaign offer an opportunity to address issues in current air quality models and reduce model-observation disagreements. This study examines these issues using model evaluation against the KORUS-AQ observations and intercomparisons between models. Six regional and two global chemistry transport models using identical anthropogenic emissions participated in the model intercomparison study and were used to conduct air quality simulations focusing on ozone (O3), aerosols, and their precursors for the campaign. Using the KORUSv5 emissions inventory, which has been updated from KORUSv1, the models successfully reproduced observed nitrogen oxides (NOx) and volatile organic compounds mixing ratios in surface air, especially in the Seoul Metropolitan Area, but showed systematic low biases for carbon monoxide (CO), implying possible missing CO sources in the inventory in East Asia. Although the DC-8 aircraft-observed O3 precursor mixing ratios were well captured by the models, simulated O3 levels were lower than the observations in the free troposphere in part due to too low stratospheric O3 influxes, especially in regional models. During the campaign, the synoptic meteorology played an important role in determining the observed variability of PM2.5 (PM diameter ≤ 2.5 μm) concentrations in South Korea. The models successfully simulated the observed PM2.5 variability with significant inorganic sulfate-nitrate-ammonium aerosols contribution, but failed to reproduce that of organic aerosols, causing a large inter-model variability. From the model evaluation, we find that an ensemble of model results, incorporating individual models with differing strengths and weaknesses, performs better than most individual models at representing observed atmospheric compositions for the campaign. Ongoing model development and evaluation, in close collaboration with emissions inventory development, are needed to improve air quality forecasting.

The Korea–United States Air Quality (KORUS-AQ) field study was conducted during May–June 2016. The effort was jointly sponsored by the National Institute of Environmental Research of South Korea and the National Aeronautics and Space Administration of the United States. KORUS-AQ offered an unprecedented, multi-perspective view of air quality conditions in South Korea by employing observations from three aircraft, an extensive ground-based network, and three ships along with an array of air quality forecast models. Information gathered during the study is contributing to an improved understanding of the factors controlling air quality in South Korea. The study also provided a valuable test bed for future air quality–observing strategies involving geostationary satellite instruments being launched by both countries to examine air quality throughout the day over Asia and North America. This article presents details on the KORUS-AQ observational assets, study execution, data products, and air quality conditions observed during the study. High-level findings from companion papers in this special issue are also summarized and discussed in relation to the factors controlling fine particle and ozone pollution, current emissions and source apportionment, and expectations for the role of satellite observations in the future. Resulting policy recommendations and advice regarding plans going forward are summarized. These results provide an important update to early feedback previously provided in a Rapid Science Synthesis Report produced for South Korean policy makers in 2017 and form the basis for the Final Science Synthesis Report delivered in 2020.

Nonmethane volatile organic compounds (NMVOCs) result in ozone and aerosol production that adversely affects the environment and human health. For modeling purposes, anthropogenic NMVOC emissions have been typically compiled using the “bottom-up” approach. To minimize uncertainties of the bottom-up emission inventory, “top-down” NMVOC emissions can be estimated using formaldehyde (HCHO) observations. In this study, HCHO vertical column densities (VCDs) obtained from the Geostationary Trace gas and Aerosol Sensor Optimization spectrometer during the Korea–United States Air Quality campaign were used to constrain anthropogenic volatile organic compound (AVOC) emissions in South Korea. Estimated top-down AVOC emissions differed from those of the up-to-date bottom-up inventory over major anthropogenic source regions by factors of 1.0 ± 0.4 to 6.9 ± 3.9. Our evaluation using a 3D chemical transport model indicates that simulated HCHO mixing ratios using the top-down estimates were in better agreement with observations onboard the DC-8 aircraft during the campaign relative to those with the bottom-up emission, showing a decrease in model bias from –25% to –13%. The top-down analysis used in this study, however, has some limitations related to the use of HCHO yields, background HCHO columns, and AVOC speciation in the bottom-up inventory, resulting in uncertainties in the AVOC emission estimates. Our attempt to constrain diurnal variations of the AVOC emissions using the aircraft HCHO VCDs was compromised by infrequent aircraft observations over the same source regions. These limitations can be overcome with geostationary satellite observations by providing hourly HCHO VCDs.

The U.S. National Aeronautics and Space Administration in partnership with Korea’s National Institute of Environmental Research embarked on the Korea-United States Air Quality (KORUS-AQ) study to address air quality issues over the Korean peninsula. Underestimation of volatile organic compound (VOC) emissions from various large facilities on South Korea’s northwest coast may contribute to this problem, and this study focuses on quantifying top-down emissions of formaldehyde (CH2O) and VOCs from the largest of these facilities, the Daesan petrochemical complex, and comparisons with the latest emission inventories. To accomplish this and additional goals discussed herein, this study employed a number of measurements acquired during KORUS-AQ onboard the NASA DC-8 aircraft during three Daesan overflights on June 2, 3, and 5, 2016, in conjunction with a mass balance approach. The measurements included fast airborne measurements of CH2O and ethane from an infrared spectrometer, additional fast measurements from other instruments, and a suite of 33 VOC measurements acquired by the whole air sampler. The mass balance approach resulted in consistent top-down yearly Daesan VOC emission flux estimates, which averaged (61 ± 14) × 103 MT/year for the 33 VOC compounds, a factor of 2.9 ± 0.6 (±1.0) higher than the bottom-up inventory value. The top-down Daesan emission estimate for CH2O and its four primary precursors averaged a factor of 4.3 ± 1.5 (± 1.9) times higher than the bottom-up inventory value. The uncertainty values in parentheses reflect upper limits for total uncertainty estimates. The resulting averaged top-down Daesan emission estimate for sulfur dioxide (SO2) yielded a ratio of 0.81–1.0 times the bottom-up SO2 inventory, and this provides an important cross-check on the accuracy of our mass balance analysis.

We present trace gas vertical profiles observed by instruments on the NASA DC-8 and at a ground site during the Korea-US air quality study (KORUS) field campaign in May to June 2016. We focus on the region near the Seoul metropolitan area and its surroundings where both anthropogenic and natural emission sources play an important role in local photochemistry. Integrating ground and airborne observations is the major research goal of many atmospheric chemistry field campaigns. Although airborne platforms typically aim to sample from near surface to the free troposphere, it is difficult to fly very close to the surface especially in environments with complex terrain or a populated area. A detailed analysis integrating ground and airborne observations associated with specific concentration footprints indicates that reactive trace gases are quickly oxidized below an altitude of 700 m. The total OH reactivity profile has a rapid decay in the lower part of troposphere from surface to the lowest altitude (700 m) sampled by the NASA DC-8. The decay rate is close to that of very reactive biogenic volatile organic compounds such as monoterpenes. Therefore, we argue that photochemical processes in the bottom of the boundary layer, below the typical altitude of aircraft sampling, should be thoroughly investigated to properly assess ozone and secondary aerosol formation.

With global anthropogenic black carbon (BC) emissions increasing, automobiles are significantly contributing as the major source of emissions. However, the appropriate regulations of BC emissions from vehicles are not in place. This study examined BC emissions following fuel types (gasoline, liquefied petroleum gas (LPG), and diesel) and engine combustion (gasoline direct injection (GDI) and multi-port injection (MPI) for gasoline vehicles) with emission regulations. To this end, chassis dynamometer and aethalometer (AE33) were used. Driving modes created by the National Institute of Environmental Research (NIER) and emission certification modes (CVS-75 and NEDC) for vehicles in Korea were used to determine BC emissions for various vehicle speeds. In addition, the contributions of biomass and coal combustion to the data of AE33 were analyzed to determine the possibility of tracking the BC sources. MPI, LPG, and EURO 6 with diesel particulate filter (DPF) vehicles emitted the lowest BC emissions in NIER modes. Among gasoline vehicles, MPI vehicles showed the lower BC content in PM emissions. Also, older vehicles in MPI vehicles emitted the high PM and BC emissions. The BC emissions of EURO 3 vehicles without DPF were the highest as the results of previous studies, and it was found that as emissions regulations were tightened, the level of BC results of diesel vehicles became similar with MPI vehicles. The average absorption &Aring, ngstr&ouml, m exponent (AAE) from difference emissions sources were biomass combustion (oak wood) &gt, coal combustion (the power plant stack) &gt, automobile emissions (gasoline, LPG, diesel).

Substantial mitigation of air pollutants emissions has been performed since 2013 around Beijing, and changes in the atmospheric characteristics have been expected over the downstream area of Beijing. In this study, both WRF-Chem simulation and on-site measurements were utilized for the Baengnyeong (island) supersite, one of the representative regional background sites located in the Yellow Sea, the entrance area of the long-range transport process in Korea. The changes in the chemical compositions of inorganic aerosols were examined for spring-time during the Chinese emission mitigation period from 2014 to 2016. The measured ratio of ionic species to PM2.5 at the Baengnyeong supersite showed changes in aerosol inorganic chemical compositions from sulfate in 2014 to nitrate in 2015–2016. The modeling results also showed that nitrate was low in 2014 and significantly increased in 2015 and 2016, and the acidic aerosol condition had also changed toward a more neutralized status in both the simulation and the observations. The WRF-Chem modeling study further indicated that the sulfur was not neutralized in 2014. However, in 2015 and 2016, SO2 was more sufficiently neutralized as sulfur emissions were substantially reduced in China, while at the same time nitrate had begun to increase in such a ‘SO2–poor’ condition in Beijing area in China, and thus approaching more enhanced neutralization over the Yellow Sea area. The causes of the higher nitrate based on the modeled characteristics of the ammonia-sulfate-nitrate aerosol formation in response to the SO2 emissions reduction in China are also discussed in this paper.

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.

Biogenic volatile organic compound (BVOCs) emissions are the largest VOC emission source globally, and are precursors to ozone and secondary organic aerosols, both of which are strong, short-lived climate pollutants. BVOC emissions are usually estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN), which requires Plant Functional Types (PFTs) and Leaf Area Indexes (LAIs) as inputs. Herein, the effects of refined input data on regional BVOC emission estimates are analyzed. For LAIs, lower resolution MODerate-resolution Imaging Spectroradiometer (MODIS), and higher spatio-temporal resolution Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) LAI were generated. For PFTs, local land cover maps were developed, in addition to MODIS PFT. In South Korea, annual emissions of isoprene and monoterpenes in 2015 were estimated as 384 and 160 Gg/year, respectively, using STARFM LAI and Local PFT (Case 4). For North Korea, 340 Gg/year isoprene and 72 Gg/year monoterpenes emissions were estimated using STARFM LAI and MODIS PFT. These estimates were 14&ndash, 110% higher than when using MODIS LAI and MODIS PFT (Case 1). Inter-comparison with satellite-based inverse isoprene emission estimates from GlobEmission shows 32% (North Korea) to 34% (South Korea) overestimation in bottom-up data. Our new vegetation inputs improve MEGAN performance and resulting BVOC emission estimations. Performance of Weather Research and Forecasting (WRF) meteorological modeling requires improvement, especially for solar radiation, to avoid overestimation of isoprene emissions.

For the purpose of improving PM prediction skills in East Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations that accounts for the uncertainties in anthropogenic emissions. In contrast to the conventional method to estimate the BEC that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over East Asia using Community Multi-scale Air Quality (CMAQ) initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1,535 sites in China were assimilated every six hours during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–14 June 2016). Data assimilation with our new BEC showed better agreement with the surface PM2.5 observations than that with the conventional method. Our method also showed closer agreement with the observations in 24-hour PM2.5 predictions with ~ 44 % fewer negative biases than the conventional method. We conclude that increased standard deviations, together with horizontal and vertical length scales in the new BEC, tend to improve the data assimilation and short-term predictions for the surface PM2.5. This paper also suggests further research efforts devoted to estimating the BEC to improve PM2.5 predictions.

The absence of up-to-date emissions has been a major impediment to accurately simulate aspects of atmospheric chemistry, and to precisely quantify the impact of changes of emissions on air pollution. Hence, a non-linear joint analytical inversion (Gauss–Newton method) of both volatile organic compounds (VOC) and nitrogen oxides (NOx) emissions is made by exploiting the Smithsonian Astrophysical Observatory (SAO) Ozone Mapping and Profile Suite Nadir Mapper (OMPS-NM) formaldehyde (HCHO) and the National Aeronautics and Space Administration (NASA) Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide (NO2) retrievals during the Korea-United States Air Quality (KORUS-AQ) campaign over East Asia in May–June 2016. Effects of the chemical feedback of NOx and VOCs on both NO2 and HCHO are implicitly included through iteratively optimizing the inversion. Emissions estimates are greatly improved (averaging kernels > 0.8) over medium- to high-emitting areas such as cities and dense vegetation. The amount of total NOx emissions is mainly dictated by values reported in the MIX-Asia 2010 inventory. After the inversion we conclude a decline in the emissions (before, after, change) for China (87.94 ± 44.09 Gg/day, 68.00 ± 15.94 Gg/day, −23 %), North China Plain (NCP) (27.96 ± 13.49 Gg/day, 19.05 ± 2.50 Gg/day, −32 %), Pearl River Delta (PRD) (4.23 ± 1.78 Gg/day, 2.70 ± 0.32 Gg/day, −36 %), Yangtze River Delta (YRD) (9.84 ± 4.68 Gg/day, 5.77 ± 0.51 Gg/day, −41 %), Taiwan (1.26 ± 0.57 Gg/day, 0.97 ± 0.33 Gg/day, −23 %), and Malaysia (2.89 ± 2.77 Gg/day, 2.25 ± 1.34 Gg/day, −22 %), all of which have effectively implemented various stringent regulations. In contrast, South Korea (2.71 ± 1.34 Gg/day, 2.95 ± 0.58 Gg/day, +9 %) and Japan (3.53 ± 1.71 Gg/day, 3.96 ± 1.04 Gg/day, +12 %) experience an increase in NOx emissions potentially due to risen number of diesel vehicles and new thermal power plants. We revisit the well-documented positive bias of the model in terms of biogenic VOC emissions in the tropics. The inversion, however, suggests a larger growth of VOC (mainly anthropogenic) over NCP (25 %) than previously reported (6 %) relative to 2010. The spatial variation in both magnitude and sign of NOx and VOC emissions results in non-linear responses of ozone production/loss. Due to simultaneous decrease/increase of NOx/VOC over NCP and YRD, we observe an ~ 53 % reduction in the ratio of the chemical loss of NOx (LNOx) to the chemical loss of ROx (RO2 + HO2) transitioning toward NOx-sensitive regimes, which in turn, reduces/increases the afternoon chemical loss/production of ozone through NO2 + OH (−0.42 ppbv hr−1)/HO2 (and RO2) + NO (+0.31 ppbv hr−1). Conversely, a combined decrease in NOx and VOC emissions in Taiwan, Malaysia, and the southern China suppresses the formation of ozone. Ultimately, model simulations indicate enhancements of maximum daily 8-hour average (MDA8) surface ozone over China (0.62 ppbv), NCP (4.56 ppbv), and YRD (5.25 ppbv) due to the non-linear ozone chemistry, suggesting that emissions standards should be extended to regulate VOCs to be able to curb ozone production rates. Taiwan, Malaysia, and PRD stand out as the regions undergoing lower MDA8 ozone levels resulting from the NOx reductions occurring predominantly in NOx-sensitive regimes.

Concentration of air pollutants such as tropospheric ozone and aerosols are mainly affected by meteorological variables and emissions. East Asia has large amount of anthropogenic and natural air pollutant emissions and has been putting lots of efforts to improve air quality. In order to seek effective ways to mitigate future air pollution, it is essential to understand the current emissions and their impacts on air quality. Emission inventory is one of the key datasets required to understand air quality and find ways to improve it. Amounts and spatial-temporal distributions of emissions are, however, not easy to estimate due to their complicate nature, therefore introduce significant uncertainties.In this study, we had developed an updated version of our Asian emissions inventory, named NIER/KU-CREATE (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment) in support of climate-air quality study. We first inter-compare multiple bottom-up inventories to understand discrepancies among the dataset(sectoral, spatial). We then inter-compare those bottom-up emissions to the satellite-based top-down emission estimates to understand uncertainties of the databases. The bottom-up emission inventories used for this study are: CREATE, MEIC(Multiresolution Emission Inventory for China), REAS (Regional Emission inventory in ASia), and ECLIPSE(Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants). The satellite-derived top-down emission inventory had been acquired from the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm data from the GlobEmissions website.The analysis showed that some discrepancies, in terms of emission amounts, sectoral shares and spatial distribution patterns, exist among the datasets. We analyzed further to find out which parameters could affect more on those discrepancies. Co-analysis of top-down and bottom-up emissions inventory help us to evaluate emissions amount and spatial distribution. These analysis are helpful for the development of more consistent and reliable inventories with the aim of reducing the uncertainties in air quality study. More results of evaluation of emissions will be presented on site. Acknowledgements : This work was supported by National Institute of Environment Research (NIER-2019-03-02-005), Korea Environment Industry & Technology Institute(KEITI) through Public Technology Program based on Environmental Policy Program, funded by Korea Ministry of Environment(MOE)(2019000160007). This research was supported by the National Strategic Project-Fine particle of the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT(MSIT), the Ministry of Environment(ME), and the Ministry of Health and Welfare(MOHW) (NRF-2017M3D8A1092022).

Atmospheric nitrogen deposition in China has attracted huge public attention in recently years due to the increasing anthropogenic emission of reactive nitrogen (Nr) and its impacts on the terrestrial and aquatic ecosystems. However, limited long-term and multi-site measurements have restrained the understanding on the mechanism of the Nr deposition as well as the chemical transport model (CTM) improvement. In this study, the performance of the simulated wet and dry deposition for different Nr species, i.e., particulate NO3− and NH4+, gaseous NOx, HNO3 and NH3, have been conducted using the framework of Model Inter-Comparison Study for Asia (MICS-Asia) phase III. Nine Models, including 5 WRF-CAMQ models, 2 self-developed regional models, a global model and a RAMS-CMAQ model, have been selected for the comparison. For wet depositions, observation data from 83 measurement sites of EANET, CREN, CAUDN, NADMN and DEE of China have been collected and normalized to compare with model results. In general, most models showed the consistent spatial and temporal variation of both oxidized N (Nox) and reduced N (Nrd) wet depositions in China with the NME around at 50 %, which is lower than the value of 70 % based on EANET observation over Asia. Both the ratio of wet or dry deposition to the total inorganic N deposition (TIN) and the ratios of TIN to their emissions have shown the consistent results with the NNDMN estimations. The performance of ensemble results (ENM) was further assessed with the measurement from satellite. In different regions of China, the results showed that the simulated Nox wet deposition was overestimated in North East China (NE) but underestimated in south of China (SE+SW), while the Nrd wet deposition was underpredicted in all regions by all models. The deposition of Nox have large uncertainties than the Nrd especially in North China (NC), indicating chemical reaction process is one of the most importance factors that affecting the model performance. Compared to Critical load (CL) value, the Nr deposition in NC, SE and SW reached or exceeded the reported CL value and exerted serious ecological impacts. The control of Nrd in NC and SW and Nox in SE would be effective to mitigate the TIN deposition in these regions. More interestingly, the Nr deposition in Tibet plateau with the high ratio of TIN/emission (~3.0), indicating a significant import from outside should be focused in the future due to its climatical influence to the sensitive ecosystem in whole China.

We examine O3 production and its sensitivity to precursor gases and boundary layer mixing in Korea by using a 3-D global chemistry transport model and extensive observations during the KORea-US cooperative Air Quality field study in Korea, which occurred in May–June 2016. During the campaign, observed aromatic species onboard the NASA DC-8 aircraft, especially toluene, showed high mixing ratios of up to 10 ppbv, emphasizing the importance of aromatic chemistry in O3 production. To examine the role of VOCs and NOx in O3 chemistry, we first implement a detailed aromatic chemistry scheme in the model, which reduces the normalized mean bias of simulated O3 mixing ratios from –26% to –13%. Aromatic chemistry also increases the average net O3 production in Korea by 37%. Corrections of daytime PBL heights, which are overestimated in the model compared to lidar observations, increase the net O3 production rate by ~10%. In addition, increasing NOx emissions by 50% in the model shows best performance in reproducing O3 production characteristics, which implies that NOx emissions are underestimated in the current emissions inventory. Sensitivity tests show that a 30% decrease in anthropogenic NOx emissions in Korea increases the O3 production efficiency throughout the country, making rural regions ~2 times more efficient in producing O3 per NOx consumed. Simulated O3 levels overall decrease in the peninsula except for urban and other industrial areas, with the largest increase (~6 ppbv) in the Seoul Metropolitan Area (SMA). However, with simultaneous reductions in both NOx and VOCs emissions by 30%, O3 decreases in most of the country, including the SMA. This implies the importance of concurrent emission reductions for both NOx and VOCs in order to effectively reduce O3 levels in Korea.

Accurate and consistent monitoring of anthropogenic combustion is imperative because of its significant health and environmental impacts, especially at city-to-regional scale. Here, we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) global prediction system using measurements from aircraft, ground sites, and ships during the Korea-United States Air Quality (KORUS-AQ) field study in May to June 2016. Our evaluation focuses on CAMS CO and CO2 analyses as well as two higher-resolution forecasts (16 and 9 km horizontal resolution) to assess their capability in predicting combustion signatures over east Asia. Our results show a slight overestimation of CAMS CO2 with a mean bias against airborne CO2 measurements of 2.2, 0.7, and 0.3 ppmv for 16 and 9 km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the 16 km forecast appears to be consistent across the vertical profile of the measurements. In contrast, we find a moderate underestimation of CAMS CO with an overall bias against airborne CO measurements of − 19.2 (16 km), − 16.7 (9 km), and − 20.7 ppbv (analysis). This negative CO mean bias is mostly seen below 750 hPa for all three forecast/analysis configurations. Despite these biases, CAMS shows a remarkable agreement with observed enhancement ratios of CO with CO2 over the Seoul metropolitan area and over the West (Yellow) Sea, where east Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul ( d CO / d CO 2 = 9 ppbv ppmv −1 ) compared to the West Sea ( d CO / d CO 2 = 28 ppbv ppmv −1 ). This “combustion signature contrast” is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8–12 ppbv ppmv −1 , the West Sea: ∼30 ppbv ppmv −1 ) regardless of forecast/analysis configurations. The correlation of CAMS CO bias with CO2 bias is relatively high over these two regions (Seoul: 0.64–0.90, the West Sea: ∼0.80 ) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO2 variability. Along with measurements at ground sites over the Korean Peninsula, CAMS produces too high CO and CO2 concentrations at the surface with steeper vertical gradients ( ∼0.4 ppmv hPa −1 for CO2 and 3.5 ppbv hPa −1 for CO) in the morning samples than observed ( ∼0.25 ppmv hPa −1 for CO2 and 1.7 ppbv hPa −1 for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9 km vs. 16 km) generally produces better forecasts.

Understanding how ozone (O3) and fine particulate matter (PM) formation respond to the precursor concentrations in the presence of biogenic emissions (BEs) and thereby changes in health effects can be a key step to design effective air quality management plans. This is particularly true in the Seoul Metropolitan Area (SMA), where future significant controls of anthropogenic sources of O3 and PM2.5 precursors are expected. In this paper, we investigate the effects of BEs on O3 and fine PM (PM2.5) concentrations during a strong photochemical air pollution season in the SMA in Korea. O3 and PM2.5 levels are modeled with and without BEs in June 2008. Further, we perform the health impact assessments (HIA) of O3 and PM2.5 concentration changes due to BEs to seek useful implications for air quality management by utilizing the adjusted exposure concentration fields for O3 and PM2.5 with an observation fusing (OBF) method. With BEs, daily maximum 8-h average O3 (maximum 8-h O3) and secondary organic aerosol (SOA) concentrations in the SMA increase by 17 and 474%, respectively. These increments are associated with significant consumption of photochemical oxidants (Ox), such as a ~ 60% reduction in OH∙ radicals. The reduction in Ox, conversely, lowers the production of secondary inorganic aerosols (SIOAs) by 2.7%. Adjusted O3 and PM2.5 exposure metrics and the subsequent HIA reveal that large mean increments of O3, about 8.43 ppb, due to BEs are responsible for approximately 62 all-cause premature mortalities in the SMA in June. However, mean increment of PM2.5 due to BEs is approximately 0.3 μg m−3 and results in negligible impacts on the all-cause mortality. Significant correlations of O3 and mortality rates (MR) with the VOC/NOx ratios across the SMA suggest that controlling volatile organic compounds (VOCs) from anthropogenic sources can be a priority to reduce O3 levels and population health risks in the SMA. Specifically, linear relationships of log [O3] and log [MR] to log [VOC/NOx] ensure that a 10% decrease in the VOC/NOx ratios through the VOC abatements would lead to a 1.5% decrease in the O3 levels and a 4.3% decrease in the MR on average across the SMA.

Fast economic growth and urbanization of China have been causing air pollution not only over its domestic but transboundary atmosphere. Recent high fine particle pollution episodes in China made the government move toward more stringent air pollution control policies - which are mostly fuel switching and emissions control. In this research, we tried to understand characteristics of Chinese emissions and their change by analyzing its emissions inventory by year, sector, and region. From the inter-comparison of existing bottom-up emission inventories, we found relatively good agreements (

The characteristics of primary gas/aerosol and secondary aerosol emissions were identified for small passenger vehicles using typical fuel types in South Korea (gasoline, liquefied petroleum gas (LPG), and diesel). The generation of secondary organic aerosol (SOA) was explored using the potential aerosol mass (PAM) oxidation flow reactor. The primary emissions did not vary significantly between fuel types, combustion technologies, or aftertreatment systems, while the amount of NH3 was higher in gasoline and LPG vehicle emissions than that in diesel vehicle emissions. The SOA emission factor was 11.7–66 mg kg-fuel−1 for gasoline vehicles, 2.4–50 mg kg-fuel−1 for non-diesel particulate filter (non-DPF) diesel vehicles (EURO 2–3), 0.4–40 mg kg-fuel−1 for DPF diesel vehicles (EURO 4–6), and 3–11 mg kg-fuel−1 for LPG vehicles (lowest). The carbonaceous aerosols (equivalent black carbon (eBC) + primary organic aerosol + SOA) of diesel vehicles in EURO 4–6 were reduced by up to 95% compared to those in EURO 2–3. The expected SOA yield increased through the hot-condition combustion section of a vehicle, over the SOA range of 0.2–155 μg m−3. These results provide the necessary data to analyze all types of SOA generated by the gas-phase oxidation in vehicle emissions in metropolitan areas.

Climate change is an important issue, with many researches examining not only future climatic conditions, but also the interaction of climate and air quality. In this study, a new version of the emissions processing software tool - Python-based PRocessing Operator for Climate and Emission Scenarios (PROCES) - was developed to support climate and atmospheric chemistry modeling studies. PROCES was designed to cover global and regional scale modeling domains, which correspond to GEOS-Chem and CMAQ/CAMx models, respectively. This tool comprises of one main system and two units of external software. One of the external software units for this processing system was developed using the GIS commercial program, which was used to create spatial allocation profiles as an auxiliary database. The SMOKE-Asia emissions modeling system was linked to the main system as an external software, to create model-ready emissions for regional scale air quality modeling. The main system was coded in Python version 2.7, which includes several functions allowing general emissions processing steps, such as emissions interpolation, spatial allocation and chemical speciation, to create model-ready emissions and auxiliary inputs of SMOKE-Asia, as well as user-friendly functions related to emissions analysis, such as verification and visualization. Due to its flexible software architecture, PROCES can be applied to any pre-gridded emission data, as well as regional inventories. The application results of our new tool for global and regional (East Asia) scale modeling domain under RCP scenario for the years 1995-2006, 2015-2025, and 2040-2055 was quantitatively in good agreement with the reference data of RCPs.

We report the results of year-long PM2.5 (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. PM2.5 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 PM2.5 (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 PM2.5 in exit-and-entrance areas associated with transboundary transport over China, Japan, and Korea.

Recent regulatory policies in East Asia reduce ozone precursors, but these changes are spatially and temporally nonuniform. This study investigates variations in the long-term trends of tropospheric NO2, HCHO, and HCHO/NO2 ratios to diagnose ozone sensitivity to changes in NOx and volatile organic compound using the Ozone Monitoring Instrument (OMI). Using an adaptive-degree polynomial filter, we identify extremums of time series of NO2 to determine when and how NO2 change. Due to the regulations in China, trends which were predominantly upward turned downward. The years undergoing these changes primarily happened in 2011 and 2012. OMI column densities, however, suggest that NOx sources in South Korea, the Pearl River Delta (PRD), Taiwan, and Japan have not consistently decreased. Specifically, as Chinese exports of NO2 started subsiding, increasing trends in NO2 columns over several Korean cities, including Seoul, become evident. To quantify the changes in NOx emissions from summertime 2010 to 2014, we conduct a 3D-Var inverse modeling using a regional model with MIX-Asia inventory and estimate NOx emissions (in 2010 and 2014) for the PRD (1.6 and 1.5 Gg/d), the Yangtze River Delta (3.9 and 3.0 Gg/d), north China (15.6 and 14.3 Gg/d), South Korea (1.6 and 1.5 Gg/d), and Japan (2.7 and 2.6 Gg/d). OMI HCHO shows upward trends in East Asia resulting from anthropogenic effects; however, the magnitudes are negative in the PRD, Japan, North Korea, and Taiwan. OMI HCHO/NO2 ratios reveal that while South Korea, Japan, and the south of China have undergone toward more NOx-sensitive regime, areas around the Bohai Sea have become more NOx saturated.

Enforcement of an air quality standard for PM2.5 in the Seoul metropolitan area (SMA) was enacted in 2015. From May to June of 2016, an international airborne and surface measurement campaign took place to investigate air pollution mechanisms in the SMA. The total and speciated PM2.5 concentrations since 2008 have been measured at an intensive monitoring site for the SMA operated by the National Institute of Environmental Research (NIER). To gain insight on the trends and sources of PM2.5 in the SMA in May, we analyze PM2.5 concentrations from 2009 to 2013 using the measurements and simulations from a 3-dimensional global chemical transport model, GEOS-Chem and its adjoint. The model is updated here with the latest regional emission inventory and diurnally varying NH3 emissions. Monthly average PM2.5 concentration measured by β-ray attenuation ranges from 28 (2010) to 45 (2013) μg/m3, decreased from 2009 to 2010, and then continuously increased until 2013. The model shows good agreement with the measurements for the daily average PM2.5 concentrations (R ≥ 0.5), and reproduces 10 out of 17 measured episodes exceeding the daily air quality standard (50 μg/m3). Using the GEOS-Chem adjoint model, we find that anthropogenic emissions from the Shandong region have the largest modeled influence on PM2.5 in Seoul in May. Average contributions to the high PM2.5 episodes simulated by the model are 39% from the Shandong region, 16% from the Shanghai region, 14% from the Beijing region, and 15% from South Korea. Anthropogenic SO2 emissions from South Korea are negligible with 90% of the total contribution originating from China. Findings from this study may guide interpretation of observations obtained in the KORUS-AQ measurement campaign.

The MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resolution. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We estimate the total Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estimated Asian emissions in 2006 using the same methodology of MIX. The relative change rates of Asian emissions for the period of 2006–2010 are estimated as follows: −8.1 % for SO2, +19.2 % for NOx, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH3, −3.4 % for PM10, −1.6 % for PM2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach. Monthly gridded emissions at a spatial resolution of 0.25° × 0.25° are developed and can be accessed from http://www.meicmodel.org/dataset-mix.

This study examines the effects of different foreign anthropogenic emissions inventories on predicted ozone concentrations in the Seoul Metropolitan Area (SMA), South Korea, and estimates changes in ozone due to emissions reductions. We ran the Community Multi-Scale Air Quality (CMAQ) model using the High-Order Decoupled Direct Method with three inventories of foreign anthropogenic emissions: (1) the Intercontinental Chemical Transport Experiment, Phase B (INTEX-B) 2006; (2) the Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (CREATE) 2010; and (3) the Model Inter-Comparison Study (MICS)-Asia 2010. All three inventories have different spatial distributions of emissions, yielding different modeled ozone concentrations. However, the ozone concentrations modeled for the SMA differ less than those modeled for large, foreign cities in the modeling domain. The simulations using INTEX-B 2006 and CREATE 2010 suggested greater reduction in ozone with NOx control than with VOCs control. All simulations show that (1) simultaneous reduction in NOx and VOCs leads to less ozone reduction than the sum of ozone reductions for individual NOx and VOCs controls and (2) ozone reductions are stronger for high ozone days than for low ozone days. Comparing the modeled reductions in the relative sense yields smaller differences between high and low ozone days than comparing the modeled reductions in the absolute sense. With a 20% reduction in only NOx emissions, the differences in MDA1O3 among the three inventories were between 0.3 and 0.7 ppb. Because air-quality planning often leads to defined tonnage reductions, we examined the model’s response to such a defined emissions reduction. Using the NOx reduction in China estimated by Zhao et al. (2013), we estimated that the differences in MDA1O3 among the three inventories were between 1.50 and 1.78 ppb. Based on these results, we recommend using different foreign anthropogenic emissions inventories to test future scenarios for air-quality control

Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. This paper discusses the estimates of aerosol radiative forcing, aerosol feedbacks, and possible causes for the differences among the models. Over the Beijing-Tianjin-Hebei (BTH) region, the ensemble mean of aerosol direct radiative forcing (ADRF) at the top of atmosphere, inside the atmosphere and at the surface are −1.9, 8.4 and −10.3 W/m2, respectively. Subdivisions of direct and indirect aerosol radiative forcing confirm the dominant roles of direct forcing. During severe haze days (January 17–19, 2010), the averaged reduction in near surface temperature for the BTH region can reach 0.3–3.0 ºC. The responses of wind speeds at 10 m (WS10) inferred from different models show consistent declines in eastern China. For the BTH region, aerosol-radiation feedback induced changes in PM2.5 range from 6.0 to 8.8 µg/m3 (2.5.

For the purpose of providing reliable and robust air quality predictions, an operational air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3, and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multi-scale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate, nitrate, ammonium, organic aerosols (OAs), and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new operational air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors, and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (IOA = 0.60; MB = −13.54) and PM2.5 (IOA = 0.71; MB = −2.43) as compared to the BASE RUN for PM10 (IOA = 0.51; MB = −27.18) and PM2.5 (IOA = 0.67; MB = −9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of −0.27 (BASE RUN) was greatly enhanced to −0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the operational air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3, and PANs in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLH) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free parameter scheme, dust episode prediction, and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the three-dimensional variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean operational air quality forecasting system.