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This study investigated the potential of fine nitrate (NO3− in PM2.5) formation in Seoul Metropolitan Area (SMA) by nighttime dinitrogen pentoxide (N2O5) heterogeneous chemistry during March 16–18, 2016, relatively dry and stagnant early spring days, by intervening N2O5 uptake coefficients (reactive uptake probability, γN2O5) in modeling with WRF-CMAQ. Simulations of a base case and two sensitivity tests with default (Davis et al., 2008), zero and decupled (tenfold) γN2O5 showed that impacts of γN2O5 on NO3− and PM2.5 are sensitive to relative humidity (RH) and sulfate-nitrate-ammonium (SNA) conditions. The base case simulation generally underestimated NO3− and PM2.5 levels in comparison to observations. Even with decupled γN2O5, modeled NO3− and PM2.5 concentrations showed relatively small increases under conditions that RH is relatively low in the range of 20 to 40% and SNA levels are severely underestimated (e.g., lower by one third) in the base case simulation. Comparisons of NO3− and PM2.5 concentrations in SMA between simulations with differently specified γN2O5 indicated that N2O5 heterogeneous chemistry has potential to (1) form additional nitric acid (HNO3), (2) further react with ammonia (NH3) emitted from various sources including agricultural sources outside of SMA urban-core areas, and (3) contribute to NO3− and PM2.5 formation in SMA. Additional modeling and observational studies on heterogeneous N2O5 chemistry are needed to improve our understanding of NO3− and PM2.5 formation and better forecast PM2.5 pollution levels over SMA or other urban areas with abundant nitrogen oxides emissions and ammonia emissions such as agricultural emissions from surrounding areas.

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%).

The typical spatio-temporal evolutions of large-scale weather patterns before and after high-concentration events of particulate matter with an aerodynamic diameter ≤ 10 μm (PM10) (High-PM10) over the Korean Peninsula were investigated. Using the K-means clustering method, five significant large-scale weather patterns associated with High-PM10 events were identified. Among the 91 High-PM10 events that occurred during winter/spring of 2007–2018, approximately 41% (38 cases) belonged to Cluster 1 and 5, characterized by upper-tropospheric downstream amplification of pressure anomaly originating from the Barents/Kara Sea of the Arctic Ocean. We refer to this pattern as the wave-train type. Concurrently with this pattern, southwesterly wind anomalies from southeastern China and significant descending air occurred over the Korean Peninsula, causing PM10 accumulation. Despite its dominance of occurrence, the wave-train type exhibited a relatively shorter mean duration of 1–2 days owing to its rapid development and traveling characteristics. Cluster 2 corresponded to approximately 29% (27 cases) of the total cases, and was characterized by a stagnant anticyclone over the northeastern Eurasia region. In the lower atmosphere, southerly winds dominated the Korean Peninsula. Vertical diffusion of PM10 was restricted by a vertically stable condition in the lower-to-mid-troposphere and a notably weak wind speed. Cluster 2 had the longest duration of 3 days and the highest mean PM10 concentration compared to the other clusters. Cluster 3 corresponded to approximately 13% (12 cases) of the total cases and exhibited a stagnant anticyclone centered at the Kamchatka region. In this case, weakened westerly is observed resulting in the characteristics of a circulation pattern that easily accumulates pollutants. Cluster 4 accounted for approximately 15% (14 cases) of all cases, and is a pattern in which air of PM10 inflows following the back of the cyclone located in the northern part of the Korean Peninsula. We emphasize that most of High-PM10 events occurring over the Korean Peninsula are linked to the preceding upper-tropospheric large-scale circulation patterns. They control the low-level transports and accumulation of pollutants.

In this study, domestic and foreign contributions to a severe PM2.5 episode in South Korea, in which &ldquo, emergency reduction measures against particulate matter&rdquo, were issued, were analyzed. During the period between 27 February and 7 March in 2019 when high PM2.5 concentrations occurred, the PM2.5 concentration in the Seoul metropolitan area (SMA) in South Korea was approximately 87.3 &mu, g/m3 on average, and a severe PM2.5 concentration level of approximately 113.4 &mu, g/m3 was observed between 3 March and 5 March. The results of the analysis conducted using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model and meteorological observation data showed that northwesterly wind or westerly winds were formed during the P1 and P3 periods when the PM2.5 concentration markedly increased. When the PM2.5 concentrations in East Asia were simulated using the Community Multiscale Air Quality (CMAQ), it was found that the high PM2.5 concentrations that occurred in the SMA of South Korea were mostly affected by PM2.5 transported over long distances and following atmospheric stagnation. When the domestic and foreign contributions were evaluated using the brute-force method (BFM), the foreign and domestic contribution concentrations were found to be 62.8 and 16.8 &mu, g/m3, respectively, during the target period of this study. It was also found that the foreign contribution was 78.8%, while the domestic contribution was 21.2%.

Ozone concentrations in East Asia were simulated using the Community Multi-scale Air Quality (CMAQ) model, and its source contributions were estimated by multiple source–receptor modeling techniques. To study relationships between ozone concentrations and precursor emission sources, three approaches were applied to four months (January, April, July, and October 2009) to represent seasonal characteristics and compare results, with a particular focus on South Korea. Brute force (BF) is a traditional sensitivity analysis method used to estimate model output response to an input change. The high-order decoupled direct method (HDDM), a computational method, is an efficient and accurate alternative to the BF method for sensitivity. The Ozone and Particulate Precursor Tagging Methodology (OPTM) provides contribution information quantified by tracking emissions from selected sources throughout the simulation period. The approaches generally show that most of the receptor regions were substantially influenced by emissions from central China, which is the largest anthropogenic emissions source region in East Asia. Local emissions were still major contributors, especially South Korea and Japan during July 2009. On the other hand, a case study of maximum 8-h ozone concentrations derived from CMAQ–OPTM on April 9 in South Korea shows that the NOx and VOCs emissions from China contributed approximately 82% and 91%, respectively, to maximum 8-h ozone in Region 4 (South Korea) without boundary inflow, which indicates that Chinese emissions are the dominant contributor in this episode. A comparison study of the three approaches shows that HDDM tends to estimate biogenic source contributions lower than that from OPTM in China but similar to OPTM in South Korea and Japan. When comparing the BF method and HDDM, the sensitivity results show a reasonably good agreement during a given period. The location- and time-dependent maximum 8-h ozone isopleths over South Korea as a receptor region created by HDDM suggest that most ozone was being transported from central China, whereas almost no ozone was formed locally during April 2009, and local conditions were heavily VOC limited. On the other hand, local emissions were the dominant contributor during July 2009, and every source region showed a NOx-limited regime, which indicates that ozone concentrations in South Korea strongly depend on NOx emissions during this month.

Plant functional type (PFT) distributions affect the results of biogenic emission modeling as well as O3 and particulate matter (PM) simulations using chemistry-transport models (CTMs). This paper analyzes the variations of both surface biogenic volatile organic compound (BVOC) emissions and O3 concentrations due to changes in the PFT distributions in the Seoul Metropolitan Areas, Korea. The Fifth-Generation NCAR/Pennsylvania State Meso-scale Model (MM5)/the Model of Emissions of Gases and Aerosols from Nature (MEGAN)/the Sparse Matrix Operator Kernel Emissions (SMOKE)/the Community Multiscale Air Quality (CMAQ) model simulations were implemented over the Seoul Metropolitan Areas in Korea to predict surface O3 concentrations for the period of 1 May to 31 June 2008. Starting from a performance check of CTM predictions, we consecutively assessed the effects of PFT area deviations on the MEGAN BVOC and CTM O3 predictions, and we further considered the basis of geospatial and statistical analyses. The three PFT data sets considered were (1) the Korean PFT, developed with Korea-specific vegetation database; (2) the CDP PFT, adopted from the community data portal (CDP) of US National Center for Atmospheric Research in the United States (NCAR); (3) MODIS PFT, reclassified from the NASA Terra and Aqua combined land cover products. Although the CMAQ performance check reveals that all of the three different PFT data sets are applicable choices for regulatory modeling practice, noticeable primary data (i.e., PFT and Leaf Area Index (LAI)) was observed to be missing in many geographic locations. Based on the assessed effect of such missing data on CMAQ O3 predictions, we found that this missing data can cause spatially increased bias in CMAQ O3. Thus, it must be resolved in the near future to obtain more accurate biogenic emission and chemistry transport modeling results. Comparisons of MEGAN biogenic emission results with the three different PFT data showed that broadleaf trees (BTs) are the most significant contributor, followed by needleleaf trees (NTs), shrub (SB), and herbaceous plants (HBs) to the total BVOCs. In addition, isoprene from BTs and terpene from NTs were recognized as significant primary and secondary BVOC species in terms of BVOC emissions distributions and O3-forming potentials in the study domain. A geographically weighted regression analysis with locally compensated ridge (LCR-GWR) with the different PFT data (δO3 vs. δPFTs) suggests that addition of BT, SB, and NT areas can contribute to O3 increase, whereas addition of an HB area contributes to O3 decrease in the domain. Assessment results of the simulated spatial and temporal changes of O3 distributions with the different PFT scenarios reveal that hourly and local impacts from the different PFT distributions on occasional inter-deviations of O3 are quite noticeable, reaching up to 13 ppb. The simulated maximum 1 h O3 inter-deviations between different PFT scenarios have an asymmetric diurnal distribution pattern (low in the early morning, rising during the day, peaking at 05:00 p.m., and decreasing during the night) in the study domain. Exponentially diverging hourly BVOC emissions and O3 concentrations with increasing ambient temperature suggest that the use of different PFT distribution data requires much caution when modeling (or forecasting) O3 air quality in complicated urban atmospheric conditions in terms of whether uncertainties in O3 prediction results are expected to be mild or severe.

In this study, we analyzed the chemical composition of fine particulate matter 2.5 μm or less (PM) collected at Incheon, the coastal area in Seoul, Korea every third day from June 2009 to May 2010. Based on the analyzed chemical species in the PM samples, the sources of PM were identified using a positive matrix factorization (PMF). Nine sources of PM were determined from PMF analysis. The major sources of PM were secondary nitrate (25.4%), secondary sulfate (19.0%), motor vehicle 1 (14.8%) with a lesser contribution from industry (8.5%), motor vehicle 2 (8.2%), biomass burning (6.1%), soil (6.1%), combustion and copper production emissions (6.1%), and sea salt (5.9%). From a paired t-test, it was found that yellow sand samples were characterized as having higher contribution from soil sources (p

The objectives of this research were to investigate the accumulation and distribution of Cd and Pb in plant parts and to evaluate the potential problem caused by heavy metals when biofuel is produced by plant seeds cultivated from Cd and Pb contaminated soil. Based on the germination test results, sunflowers were selected as the target plant because of their high germination rate and IC50 compared to other plants. The trends of accumulated amounts and concentrations of Cd and Pb in sunflowers were not identical. The accumulation of Cd in the plant parts was in the order of: seeds > stem > leaves > petals > roots, with the accumulation of Pb: stem > roots > leaves > petals > seeds. Pb did not accumulate in the seeds. Our findings show that there should not be any significant problem producing biofuel from sunflower seeds cultivated in Pb contaminated soil.

We examined the characteristics, sources, and distribution of PM 2.5 and carbonaceous species in particulate samples collected from June 2009 to May 2010 in Incheon, Korea. The average PM 2.5 concentration (41.9 ± 9.0 μg m −3 ) exceeded the annual level set by the United States' National Ambient Air Quality Standards (15 μg m −3 ). The major fraction of PM 2.5 consisted of ionic species (accounting for 38.9 ± 8.8%), such as NO 3 − , SO 4 2− , and NH 4 + , as well as organic carbon (OC) (accounting for 18.9 ± 5.1%). We also analyzed the seasonal variation in PM 2.5 and secondary aerosols such as NO 3 − and SO 4 2− in PM 2.5 . While SO 4 2− concentrations were higher in spring and summer, the concentration of PM 2.5 and NO 3 − were the highest in winter. SO 4 2− concentrations were higher during the spring and summer, but PM 2.5 and NO 3 − were highest during the winter. As an important aerosol indicator, water-soluble organic carbon (WSOC) (mean 4.7 ± 0.8 μg m −3 , 58.9 ± 10.7% of total OC) showed a strong relationship with NO 3 − , SO 4 2− , and SOC ( R 2 = 0.56, 0.67, and 0.65, respectively), which was indicative of favorable conditions for SOC formation during the sampling period. Among the individual organic aerosols measured, n-alkanes, n-alkanoic acids, levoglucosan, and phthalates were major components, whereas PAHs (polycyclic aromatic hydrocarbons), oxy-PAHs, hopanes, and cholestanes were minor components. The concentration of organic compounds during smoggy periods was higher than during non-event periods. The n-alkane and n-alkanoic acid species during the smoggy periods were 10–14 times higher than during the normal period. Using principal component analysis coupled with multiple linear regression analysis, we identified the primary sources of PM 2.5 to be motor vehicle/sea salt, secondary organic aerosols, combustion, biogenic/meat cooking, and soil sources.

Various types of satellite (AIRS/AMSU, MODIS) and ground measurements are used to analyze temperature trends in the four vertical layers (skin/surface, mid-troposphere, and low stratosphere) around the Korean Peninsula (123–132°E, 33–44°N) during the period from September 2002 to August 2010. The ground-based observations include 72 Surface Meteorological Stations (SMSs), 6 radiosonde stations (RAOBs), 457 Automatic Weather Stations (AWSs) over the land, and 5 buoy stations over the ocean. A strong warming (0.052 K yr−1) at the surface, and a weak warming (0.004∼0.010 K yr−1) in the mid-troposphere and low stratosphere have been found from satellite data, leading to an unstable atmospheric layer. The AIRS/AMSU warming trend over the ocean surface around the Korean Peninsula is about 2.5 times greater than that over the land surface. The ground measurements from both SMS and AWS over the land surface of South Korea also show a warming of 0.043∼0.082 K yr−1, consistent with the satellite observations. The correlation average (r = 0.80) between MODIS skin temperature and ground measurement is significant. The correlations between AMSU and RAOB are very high (0.91∼0.95) in the anomaly time series, calculated from the spatial averages of monthly mean temperature values. However, the warming found in the AMSU data is stronger than that from the RAOB at the surface. The opposite feature is present above the mid-troposphere, indicating that there is a systematic difference. Warming phenomena (0.012∼0.078 K yr−1) are observed from all three data sets (SMS, AWS, MODIS), which have been corroborated by the coincident measurements at five ground stations. However, it should also be noted that the observed trends are subject to large uncertainty as the corresponding 95% confidence intervals tend to be larger than the observed signals due to large thermal variability and the relatively short periods of the satellitebased temperature records. The EOF analysis of monthly mean temperature anomalies indicates that the tropospheric temperature variability near Korea is primarily linked to the Arctic Oscillation (AO), and secondarily to ENSO (El Nino and Southern Oscillation). However, the low stratospheric temperature variability is mainly associated with Southern Oscillation and then additionally with Quasi-Biennial Oscillation (QBO). Uncertainties from the different spatial resolutions between satellite data are discussed in the trends.

Three comprehensive acid deposition models were used to simulate the sulfur concentrations over northeast Asia over the period covering entire year of 2002, and discussed the aggregated uncertainties and discrepancies of the three models. The participating models are from the countries participating in the project of Longrange Transboundary Air Pollutants in Northeast Asia (LTP): China, Japan and Korea. The Eulerian Model-3/CMAQ (by China), Regional Air Quality Model (RAQM, by Japan), and Comprehensive Acid Deposition Model (CADM, by Korea) were employed by each country with common emissions data established by the administrative agencies of China, Japan and Korea. The episodic simulation results between 1 to 15, March 2002 are also presented, during which aircraft measurements were carried out over the Yellow sea. The episodic results show both a wide short-term variability in simulations against measurements, and maximum concentration differences of 3∼5 times among the three models, requiring that further attention before confidence among the three models can be claimed for short-term simulations. However, the year-long cumulative simulations showed almost the same general features, with lower aggregated uncertainties between the three models, produced by the long term integration over northeast Asia.

This study aims to identify the characteristics of soil in Mongolia, one of the major Asian dust sources that influence the Korean Peninsula. Soil particle size was analyzed and the result shows that sand (57.5~97.3%) was identified prominently in most regions, followed by silt (2.5~34.7%) and clay (0.0~7.8%). Soil pH of the covered regions were in the range 7.1~10.1, either weak alkaline or strong alkaline. Analysis of ion species in the soil samples exhibited that (), (), and () were detected more in the soil than other species such as (), (), (), (), and (). As for heavy metal content in the soil, concentrations of soil-borne metals including Fe, Al, Ca, Mg, and K tended to be high, while metals that come from manmade sources Pb, Cd, Cr, V, and Ni were remarkably low. The concentration of organic carbon (OC) was relatively high at , while elemental carbon (EC), directly released in the process of fossil fuel combustion, was not detected at all or found in very small amounts. The result indicates that pollution from manmade sources scarcely occurred. The analysis results from this study may contribute to improving modeling accuracy by providing input data for Asian dust prediction models, and be used as base data for determining the process of physiochemical transformation of Asian dust during long-range transport.

Emission input modules was developed to produce emission input data and change some profiles for Sparse Matrix Operator Kernel Emissions (SMOKE) using Clean Air Policy Support System (CAPSS)`s activities and previous studies. Specially, this study was focused to improve chemical speciation and temporal allocation profiles of SMOKE. At first, SCC cord mapping was done. 579 SCC cords of CAPSS were matched with EPA`s one. Temporal allocation profiles were changed using CAPSS monthly activities. And Chemical speciation profiles were substituted using Kang et al. (2000) and Lee et al. (2005) studies and Kim et al. (2005) study. Simulation in Seoul Metropolitan Area (Seoul, Incheon, Gyeonggi) using MM5, SMOKE and CMAQ modeling system was done for effect analysis of changed input modules of SMOKE. Emission model results adjusted with new input modules were slightly changed as compared to using EPA`s default modules. SMOKE outputs shows that aldehyde emissions were decreased 4.78% after changing chemical profiles, increased 0.85% after implementing new temporal profiles. Toluene emissions were decreased 18.56% by changing chemical speciation profiles, increased 0.67% by replacing temporal profiles as well. Simulated results of air quality were also slightly elevated by using new input modules. Continuous accumulation of domestic data and studies to develop input system for air quality modeling would produce more improved results of air quality prediction.

This study was carried out to identify possible sources and to estimate their contribution to total suspended particle (TSP) organic aerosol (OA) contents. A total of 120 TSP and PM2.5 samples were collected simultaneously every third day over a one-year period in urban area of Incheon, Korea. High concentration in particulate matters (PM) and its components (NO3−, water soluble organic compounds (WSOCs), and n-alkanoic acids) were observed during the winter season. Among the organics, n-alkanes, n-alkanoic acids, levoglucosan, and phthalates were major components. Positive matrix factorization (PMF) analysis identified seven sources of organic aerosols including combustion 1 (low molecular weight (LMW)-polycyclic aromatic hydrocarbons (PAHs)), combustion 2 (high molecular weight (HMW)-PAHs), biomass burning, vegetative detritus (n-alkane), secondary organic aerosol 1 (SOA1), secondary organic aerosol 2 (SOA2), and motor vehicles. Vegetative detritus increased during the summer season through an increase in biogenic/photochemical activity, while most of the organic sources were prominent in the winter season due to the increases in air pollutant emissions and atmospheric stability. The correlation factors were high among the main components of the organic carbon (OC) in the TSP and PM2.5. The results showed that TSP OAs had very similar characteristics to the PM2.5 OAs. SOA, combustion (PAHs), and motor vehicle were found to be important sources of carbonaceous PM in this region. Our results imply that molecular markers (MMs)-PMF model can provide useful information on the source and characteristics of PM.

In this study, we analyzed the chemical composition of fine particulate matter 2.5 μm or less (PM) collected at Incheon, the coastal area in Seoul, Korea every third day from June 2009 to May 2010. Based on the analyzed chemical species in the PM samples, the sources of PM were identified using a positive matrix factorization (PMF). Nine sources of PM were determined from PMF analysis. The major sources of PM were secondary nitrate (25.4%), secondary sulfate (19.0%), motor vehicle 1 (14.8%) with a lesser contribution from industry (8.5%), motor vehicle 2 (8.2%), biomass burning (6.1%), soil (6.1%), combustion and copper production emissions (6.1%), and sea salt (5.9%). From a paired t-test, it was found that yellow sand samples were characterized as having higher contribution from soil sources (p0.05). Furthermore, the likely source areas of PM emissions were determined using the conditional probability function (CPF) and the potential source contribution function (PSCF). CPF analysis identified the likely local sources of PM as motor vehicles and sea salt. PSCF analysis indicated that the likely source areas for secondary particles (sulfate and nitrate) were the major industrial areas in China. Finally, using the source contribution of PM and associated organic composition data, principal component analysis (PCA) was conducted to evaluate the accuracy of the PM source apportionments by PMF. The PCA analysis confirmed eight of the nine PM sources. Our result implies that the chemical composition analysis of PM data and various modeling techniques can effectively identify the potential contributing sources.

In response to increasing trends in sulfur deposition in Northeast Asia, three countries in the region (China, Japan, and Korea) agreed to devise abatement strategies. The concepts of critical loads and source-receptor (S-R) relationships provide guidance for formulating such strategies. Based on the Long-range Transboundary Air Pollutants in Northeast Asia (LTP) project, this study analyzes sulfur deposition data in order to optimize acidic loads over the three countries. The three groups involved in this study carried out a full year (2002) of sulfur deposition modeling over the geographic region spanning the three countries, using three air quality models: MM5-CMAQ, MM5-RAQM, and RAMS-CADM, employed by Chinese, Japanese, and Korean modeling groups, respectively. Each model employed its own meteorological numerical model and model parameters. Only the emission rates for SO(2) and NO(x) obtained from the LTP project were the common parameter used in the three models. Three models revealed some bias from dry to wet deposition, particularly the latter because of the bias in annual precipitation. This finding points to the need for further sensitivity tests of the wet removal rates in association with underlying cloud-precipitation physics and parameterizations. Despite this bias, the annual total (dry plus wet) sulfur deposition predicted by the models were surprisingly very similar. The ensemble average annual total deposition was 7,203.6 ± 370 kt S with a minimal mean fractional error (MFE) of 8.95 ± 5.24 % and a pattern correlation (PC) of 0.89-0.93 between the models. This exercise revealed that despite rather poor error scores in comparison with observations, these consistent total deposition values across the three models, based on LTP group's input data assumptions, suggest a plausible S-R relationship that can be applied to the next task of designing cost-effective emission abatement strategies.