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Hybrid receptor models overestimate the contribution of background areas (no specific emission sources), like the Yellow Sea in Korea. This study aimed to improve model performances using Advanced Concentration Emission and Retention Time Weighted Trajectory (ACERWT). ACERWT was combined with a positive matrix factorization (PMF), back trajectory, and Regional Emission Inventory in Asia (REAS). The PMF receptor model used one year of data from Korea's Central Air Environment Research Center. In the PMF receptor model, eight sources (dust/soil, secondary nitrate, biomass burning, vehicles, secondary sulfate, industry, coal combustion and sea salt) influenced PM2.5 pollution at the receptor site (Daejeon, Korea). Secondary sulfate was the most dominant source, followed by secondary nitrate and vehicle sources. ACERWT results showed high contributions from China, Japan, and Korean regions, while the contribution from the Yellow Sea was significantly lower. Several regions, such as the eastern and south-eastern areas of China, the southern area of Taiwan, the western area of Tokyo, and the central area of Korea, showed high contributions due to large-scale emission facilities and industrial complexes. In this study, the ACERWT model significantly improved its performance regarding regional contributions to PM2.5 pollution at the receptor site. [ABSTRACT FROM AUTHOR]

Air pollution is a critical problem that is of major concern worldwide. South Korea is one of the countries most affected by air pollution. Rapid urbanization and industrialization in South Korea have induced air pollution in multiple forms, such as smoke from factories and exhaust from vehicles. In this paper, we perform a comparative analysis of predictive models for fine particulate matter in Daejeon, the fifth largest city in South Korea. This study is conducted for three purposes. The first purpose is to determine the factors that may cause air pollution. Two main factors are considered: meteorological and traffic. The second purpose is to find an optimal predictive model for air pollutant concentration. We apply machine learning and deep learning models to the collected dataset to predict hourly air pollutant concentrations. The accuracy of the deep learning models is better than that of the machine learning models. The third purpose is to analyze the influence of road conditions on predicting air pollutant concentration. Experimental results demonstrate that considering wind direction and wind speed could significantly decrease the error rate of the predictive models. [ABSTRACT FROM AUTHOR]

This paper presents the development of the global to mesoscale air quality forecast and analysis system (GMAF) and its application to particulate matter under 2.5 μm (PM2.5) forecast in Korea. The GMAF combined a mesoscale model with a global data assimilation system by the grid nudging based four-dimensional data assimilation (FDDA). The grid nudging based FDDA developed for weather forecast and analysis was extended to air quality forecast and analysis for the first time as an alternative to data assimilation of surface monitoring data. The below cloud scavenging module and the secondary organic formation module of the community multiscale air quality model (CMAQ) were modified and subsequently verified by comparing with the PM speciation observation from the PM supersite. The observation data collected from the criteria air pollutant monitoring networks in Korea were used to evaluate forecast performance of GMAF for the year of 2016. The GMAF showed good performance in forecasting the daily mean PM2.5 concentrations at Seoul; the correlation coefficient between the observed and forecasted PM2.5 concentrations was 0.78; the normalized mean error was 25%; the probability of detection for the events exceeding the national PM2.5 standard was 0.81 whereas the false alarm rate was only 0.38. Both the hybrid bias correction technique and the Kalman filter bias adjustment technique were implemented into the GMAF as postprocessors. For the continuous and the categorical performance metrics examined, the Kalman filter bias adjustment technique performed better than the hybrid bias correction technique. [ABSTRACT FROM AUTHOR]

Thermal power plants (TPPs) and national industrial complexes (NICs) are widely known as being among the major causes of changes in the concentrations of fine particulate matter (PM2.5). However, little is known about the changes in PM2.5 concentration caused by the operation of these facilities in South Korea and the health burden attributable to them, including early death. There were two purposes to this study. The first was to quantitatively evaluate the changes in PM2.5 concentration caused by TPPs and NICs in Korea. The second was to estimate the number of early deaths as a health burden attributable to such changes in PM2.5 concentration. The changes in PM2.5 concentration caused by the operation of TPPs and NICs were investigated within TPPs in 2013 and within NICs in 2015. The number of early deaths in 2015 caused by changes in PM2.5 concentration was estimated using the Environmental Benefits Mapping and Analysis Program (BenMAP). Nationwide, the annual average concentration of PM2.5 caused by the operation of TPPs and NICs was estimated to increase by 0.611 μg/m3 and 1.245 μg/m3, respectively, suggesting that NICs contributed about twice as much to this concentration as TPPs. The same trend was also observed regarding the number of early deaths, with TPPs and NICs accounting for 1017 and 2091 early deaths per year, respectively, indicating that the operation of NICs causes a health burden about twice as high as that caused by TPPs. However, the changes in PM2.5 concentration were found to be high near TPPs and NICs, while the health burden caused by exposure to PM2.5 varied according to the level of population distribution and mortality in each air (quality) control zone (ACZ) to which one is exposed. The findings of this study are expected to be utilized as reference data when setting goals to strengthen air quality management (AQM) in each ACZ in Korea. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) is the most dangerous type of air pollutant and is harmful to human health. Plants can be used as a biofilter to remove PM from the atmosphere and improve air quality. In this study, we used the air pollution tolerance index and four leaf traits of five different plant species commonly used in landscaping in Korea to determine which plants are best suited to remove PM from the atmosphere in roadside areas in spring, summer, and autumn. We found that the PM concentrations in the atmosphere impacted the amount of PM accumulated in the plants, with increased PM accumulation during periods of increased environmental PM levels on the roadside. Euonymus japonicus, and Euonymus alatus accumulated the highest amount of PM and had the highest tolerance levels to air pollution. Thus, these species could be suitable for use in areas with high PM concentrations to improve air quality. We also found that shrubs were more effective in accumulating PM than trees and recommend that shrubs and trees be used together to further increase the amount of PM removed from the atmosphere in urban areas. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) is the most dangerous form of air pollution and is known to cause severe health problems to humans. Plants as biological filters can reduce PM in urban areas by accumulating PM on the surface and epicuticular wax of leaves. The present study determined the amount of PM (large PM (10–100 µm) and coarse PM (2.5–10 µm)) collected on 10 plant species from two sites (urban forest and roadside) of Cheongju City, South Korea. Selected leaf traits (chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), carotenoid, relative leaf water content (RWC), specific leaf area (SLA), and pH of these plant species at the two sites were concurrently determined to find about the correlation between the leaf straits and PM accumulation on leaf. Study results showed that the amount of accumulated large PM (10–100 µm) and coarse PM (2.5–10 µm) were different depending on the plant species and the collection site. Plants from the roadside tended to have higher amounts of PM accumulation as compared to the same plant species from the urban forest. In addition, the amount of PM accumulated on the leaf surface was higher than that of the epicuticular wax. PM accumulation on the leaf surface was positively correlated with the amount of PM accumulated on the epicuticular wax. Among the 10 plant species selected, P. strobus, P. densiflora, M. denudata, and S. vulgaris were the most effective plant for PM accumulation, while M. glyptostroboides was the least effective plant ones. Chl a, Chl b, TChl, and carotenoid contents were higher in plants collected along the roadside than in those collected from the urban forest, whereas RWC was higher in plants from the urban forest. No distinct tendency was noted regarding the pH. Coarse PM (2.5–10 µm) was negatively correlated with leaf traits of plants along the roadside. The tolerance of plants to pollution might be due to an increase in chlorophyll content. Features of the leaf were also essential in increasing PM accumulation on the leaf surface. [ABSTRACT FROM AUTHOR]

Various studies are currently underway on PM (Particulate Matter) monitoring in view of the importance of air quality in public health management. Spatial interpolation has been used to estimate PM concentrations due to that it can overcome the shortcomings of station-based PM monitoring and provide spatially continuous information. However, PM is affected by a combination of several factors, and interpolation that only considers the spatial relationship between monitoring stations is limited in ensuring accuracy. Additionally, relatively accurate results may be obtained in the case of interpolation by using external drifts, but the methods have a disadvantage in that they require additional data and preprocessing. This study proposes a modified IDW (Inverse Distance Weighting) that allows more accurate estimations of PM based on the sole use of measurements. The proposed method improves the accuracy of the PM estimation based on weight correction according to the importance of each known point. Use of the proposed method on PM10 and PM2.5 in the Seoul-Gyeonggi region in South Korea led to an improved accuracy compared with IDW, kriging, and linear triangular interpolation. In particular, the proposed method showed relatively high accuracy compared to conventional methods in the case of a relatively large PM estimation error. [ABSTRACT FROM AUTHOR]

Road and industrial origin particulate matters (PM) are a significant source of potentially toxic elements (PTEs), with health risks to the surrounding residents. In Korea for 60 years, although industries, roads and automobiles have increased aggressively, there are still few PTEs data in PM in road-deposited sediment (RDS) of industrial complexes (ICs). Therefore, this study aimed to investigate the PTE composition of on-road PM10 from nine major ICs and its pollution degree in Korea and evaluate its human health risks. The geo-accumulation index (Igeo) and pollution load index (PLI) elucidated that on-road PM10 were severely polluted by Sb, Zn, Ag and Pb. A combination of principal component analysis (PCA) and chemical tracers was used to define the PTEs sources. The results showed that non-exhaust emission from vehicles' activity is the primary source of PTEs in on-road PM10, and industrial emissions are the secondary source. The riskiest pathway on carcinogenic and non-carcinogenic by on-road PM10 with PTEs was in-gestion. Traffic origin PTEs including Pb, As, Sb and Cd had a more significant impact on carcinogenic and non-carcinogenic health than those of industrial origins. These results could help mitigate public health risks arising from on-road PM10 and improve air quality in ICs. [ABSTRACT FROM AUTHOR]

High concentration of particulate matter (PM) threatens public health and the environment. Increasing traffic in the city is one of the main factors for increased PM in the air. Urban green spaces play an important role in reducing PM. In this study, the leaf surface and in-wax PM (sPM and wPM) accumulation were compared for 11 plant species widely used for landscaping in South Korea. In addition, biochemical characteristics of leaves (ascorbic acid chlorophyll content, leaf pH, and relative water content) were analyzed to determine air pollution tolerance. Plant species suitable for air quality improvement were selected based on their air pollution tolerance index (APTI) and anticipated performance index (API). Results showed a significant difference according to the accumulation of sPM and wPM and the plant species. PM accumulation and APTI showed a positive correlation. Pinus strobus showed the highest PM accumulation and APTI values, while Cercis chinensis showed the lowest. In 11 plants, API was divided into five groups. Pinus densiflora was classified as the best group, while Cornus officinalis and Ligustrum obtusifolium were classified as not recommended. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rapid industrialization of Korea's economy has brought with it environmental pollution that threatens human health. Among various other pollutants, ambient fine particulate matter known to endanger human health often exceeds air quality standards in Seoul, South Korea's capital. The goal of this research is to find the impact of meteorological extremes and particle levels on human health. The analysis was conducted using hourly air pollutant concentrations, meteorological variables, and the daily mortality from cerebrovascular disease. Results show that the effect of fine particulate matter on mortality from cerebrovascular disease was more noticeable during meteorological extremes. The linkage between extreme weather conditions and mortality was more apparent in winter than in summer. Comprehensive studies of various causes of diseases should be continued to more accurately analyze the effects of fine particulate matter on human health and meteorological extremes, and to further minimize the public health impact of air pollution and meteorological conditions. [ABSTRACT FROM AUTHOR]

This study analyzes the relationship between various atmospheric fields and the observed PM10 concentrations in the Seoul metropolitan area, South Korea, during the winters of the 2001–2014 period to find suitable atmospheric indices for predicting high PM10 episodes in the region. The analysis shows that PM10 concentration in the metropolitan area is mainly affected by the intensity of horizontal ventilation and the 500 hPa high-pressure system over the Korean peninsula. The modified Korea particulate matter index (MKPI) is proposed based on a 10 m wind speed for surface ventilation and 500 hPa zonal wind for the intensity of a 500 hPa high-pressure system over the Korean peninsula. It is found that a positive MKPI value is closely correlated with the occurrence of high PM10 concentration episodes, and hence, can be used as a predictor for high PM10 episodes in the area. A future projection of the MKPI using two three-member general circulation model (GCM) ensembles with four shared socioeconomic pathway (SSP) scenarios in Coupled Model Intercomparison Project Phase 6 (CMIP6) shows that positive MKPI events and high PM10 episodes are expected to increase by 5.4−16.4% depending on the SSP scenarios in the 2081−2100 period from the present-day period of 1995−2014. [ABSTRACT FROM AUTHOR]

High particulate matter (PM2.5) concentrations have been considered a serious environmental issue in South Korea. Recent studies have focused mostly on metropolitan and industrial cities; however, high PM2.5 episodes have also been frequently observed even in small– and middle-sized cities. Thus, in this study, PM2.5 and its major chemical components were measured in a small residential city with low anthropogenic emissions for 2 years to identify the factors affecting the PM2.5 concentrations. Overall, the average PM2.5 concentration was 29.4 μg m−3: about two times higher than the annual ambient air quality standard value. In winter, when the PM2.5 concentrations were generally higher, relative humidity (RH) was significantly correlated with both PM2.5 mass and the PM2.5/PM10 ratio, suggesting that high RH promoted the formation of secondary PM2.5. In addition, SO42− and NO3− were found to be correlated with both NH4+ and K+ in winter, indicating that biomass burning was an important source in this city. Water-soluble organic carbon (WSOC) was also highly correlated with elemental carbon (EC) and K+ in fall and winter, when the burning of agricultural residues actively occurred. During high concentration episodes, NO3− exhibited the highest increase; nevertheless, other components (e.g., K+ and organic carbon) also significantly increased. [ABSTRACT FROM AUTHOR]

This study identified the key chemical components based on an analysis of the seasonal variations of ground level PM2.5 concentrations and its major chemical constituents (sulfate, nitrate, ammonium, organic carbon, and elemental carbon) in the Seoul Metropolitan Area (SMA), over a period of five years, ranging from 2012 to 2016. It was found that the mean PM2.5 concentration in the SMA was 33.7 μg/m3, while inorganic ions accounted for 53% of the total mass concentration. The component ratio of inorganic ions increased by up to 61%–63% as the daily mean PM2.5 concentration increased. In spring, nitrate was the dominant component of PM2.5, accounting for 17%–32% of the monthly mean PM2.5 concentrations. In order to quantify the impact of long-range transport on the SMA PM2.5, a set of sensitivity simulations with the community multiscale air-quality model was performed. Results show that the annual averaged impact of Chinese emissions on SMA PM2.5 concentrations ranged from 41% to 44% during the five years. Chinese emissions' impact on SMA nitrate ranged from 50% (winter) to 67% (spring). This result exhibits that reductions in SO2 and NOX emissions are crucial to alleviate the PM2.5 concentration. It is expected that NOX emission reduction efforts in China will help decrease PM2.5 concentrations in the SMA. [ABSTRACT FROM AUTHOR]