• Journal of the Korean earth science society
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• v.34 no.7
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• pp.693-710
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• 2013

This study investigates the distribution of suspended particulates during the Asian dust period in Busan, Korea in the spring of 2009. Weather map and automatic weather system (AWS) data were used to analyze the synoptic weather conditions during the period. Particulate matter 10, laser particle counter data, satellite images and a backward trajectories model were used to analyze the aerosol particles distribution and their origins. In Case 1 (20 February 2009), when the $PM_{10}$ concentration increased, the aerosol volume distribution of small ($0.3-1.0{\mu}m$) particles decreased, while the concentration of large ($1.0-10.0{\mu}m$) particles increased. When the $PM_{10}$ concentration decreased, the aerosol volume distribution was observed to decrease as well. The prevailing winds changed from weak northerly winds to strong southwesterly winds when the concentration of the large particles increased. The correlation coefficient between the $PM_{10}$ concentration and aerosol volume distribution of large particles showed a high positive value of over 0.9. The results from the trajectory model show that the Asian dust originated in the Gobi desert and the Nei Mongol plateau. In Case 2 (25 April 2009), when the $PM_{10}$ concentration increased, the aerosol volume concentration of small ($0.3-0.5{\mu}m$) particles decreased, but the concentration of large ($0.5-10.0{\mu}m$) particles increased. The opposite was observed when the $PM_{10}$ concentration decreased. The prevailing winds changed from northeasterly winds to southwesterly and northeasterly winds. The correlation coefficient between the $PM_{10}$ concentration and aerosol volume distribution of large particles ($1.0-10.0{\mu}m$) showed a high positive value of about 0.9. The results from the trajectory model show that the Asian dust originated in Manchuria and the eastern coast of China.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.259-267
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• 2020

This study measured the Particulate Matter (PM) concentration according to altitude (30 m, 60 m, 90 m, 120 m, and 150 m) in three different environments: a construction site, natural environment (arboretum), and residential area. PM_2.5 and PM₁₀ values at 30 m above the construction site were 18.63 ㎍/㎥ and 24.23 ㎍/㎥ while values at 150 m were 10.89 ㎍/㎥ and 10.61 ㎍/㎥, respectively, indicating the average concentration decreased as altitude increased. PM_2.5 and PM₁₀ values at 30 m above the natural environment were 9.03 ㎍/㎥ and 11.21 ㎍/㎥ while those at 150 m were 3.42 ㎍/㎥ and 3.57 ㎍/㎥, respectively, showing lower average concentrations as altitude increased. PM_2.5 and PM₁₀ values at 30 m above the residential area were 10.65 ㎍/㎥ and 12.06 ㎍/㎥ while those at 150 m were 4.24 ㎍/㎥ and 5.17 ㎍/㎥, also demonstrating lower PM concentrations as altitude increased. The PM concentrations decreased as altitude increased at all tested sites and also decreased between environments in the following order: construction site, residential area, and natural environment. The results of this study are significant because PM concentrations were measured at various altitudes at different land-use sites. The results are expected to serve as basic data for decision-making in both regional and urban planning.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.635-646
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• 2018

This study sought to identify the characteristics of seasonal concentration differences of particulate matter influenced by land cover types associated with particulate matter emission and reductions, namely forest and urbanized regions. PM10 and PM2.5 was measured with quantitative concentration in 2016 on 23 urban air monitoring stations in Seoul, classified the stations into 3 groups based on the ratio of urbanized and forest land covers within a range of 3km around station, and analysed the differences in particulate matter concentration by season. The center values for the urbanized and forest land covers by group were 53.4% and 34.6% in Group A, 61.8% and 16.5% in Group B, and 76.3% and 6.7% in Group C. The group-specific concentration of PM10 and PM2.5 by season indicated that the concentration of Group A, with high ratio of forests, was the lowest in all seasons, and the concentration of Group C, with high ratio of urbanized regions, had the highest concentration from spring to autumn. These inter-group differences were statistically significant. The concentration of Group C was lower than Group B in the winter; however, the differences between Groups B to C in the winter were not statistically significant. Group A concentration compared to the high-concentration groups by season was lower by 8.5%, 11.2%, 8.0%, 6.8% for PM10 in the order of spring, summer, autumn and winter, and 3.5%, 10.0%, 4.1% and 3.3% for PM2.5. The inter-group concentration differences for both PM10 and PM2.5 were the highest in the summer and grew smaller in the winter, this was thought to be because the forests' ability to reduce particulate matter emissions was the most pronounced during the summer and the least pronounced during the winter. The influence of urbanized areas on particulate matter concentration was lower compared to the influence of forests. This study provided evidence that the particulate matter concentration was lower for regions with higher ratios of forests, and subsequent studies are required to identify the role of green space to manage particulate matter concentration in cities.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.1
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• pp.20-28
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• 2014

The three air quality monitoring sites, analysed simultaneously PM-10 and PM-2.5, ie. Ihyeondong in industrial area, Manchondong in residential area, Pyeongnidong in streetside, among 13 air quality monitoring sites in Daegu area, were investigated the concentration distribution characteristics of PM-2.5 and PM-10 in the last 2 years (2011~2012). PM-10 concentrations exceeded annual average reference value ($50{\mu}g/m^3$) in Ihyeondong ($52.5{\mu}g/m^3$) and Pyeongnidong ($60.9{\mu}g/m^3$) but satisfied in Manchondong ($44.9{\mu}g/m^3$). All PM-2.5 concentrations exceeded EPA annual standard value of the United States ($15{\mu}g/m^3$) in three points, but also exceeded new control annual standard value ($25{\mu}g/m^3$) coming into effect in 2015. Seasonal concentration of PM-10 appeared the order of spring > winter > fall > summer, and in the case of PM-2.5, the order was winter > spring > fall > summer. Monthly concentrations of PM-10 and PM-2.5 were highest in February and lowest in September. Diurnal concentrations of PM-10 and PM-2.5 increased from 7:00 AM, and recorded the highest concentration between 10:00 AM and 11:00 AM. And after 6:00 PM it lowered continuously and tended to show fixed concentrations from evening until early morning. In addition, the concentration of fine particles during the week was higher than the weekend. The fluctuation in industrial area was larger than the residential area. At the PM-2.5/PM-10 ratio, summer was generally high, spring was the lowest. And, when yellow sand occurred, it was 0.32 to 0.42. It was very low compared to 0.54 to 0.64 during non-yellow sand times. This paper for the state and the characteristics of Daegu' fine particles (PM-10, PM-2.5) will be valuable to future researches of fine particles and air pollution management.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.509-520
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• 2018

The impact of meteorological condition on surface $PM_{10}$ concentrations in South Korea was quantitatively simulated from 2010 to 2014 using WRF (ver.3.8.1) and CMAQ (5.0.2) model. The result showed that seasonal standard deviations of PM10 induced by change of weather conditions were $4.8{\mu}g/m^3$, $1.7{\mu}g/m^3$, $1.7{\mu}g/m^3$, $4.2{\mu}g/m^3$ for spring, summer, autumn and winter compared to 2010, respectively, with the annual mean standard deviation of about $2.6{\mu}g/m^3$. The results of 18 regions in South Korea showed standard deviation of more than $1{\mu}g/m^3$ in all regions and more than $2{\mu}g/m^3$ in Seoul, Northern Gyeonggi, Southern Southern Gyeonggi, Western Gangwon and Northern Chungcheong in South Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.61-69
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• 2014

In this study spatial analysis of PM10 was performed to Particulate Materials(PM) less than $10{\mu}m$ in diameter in Seoul city. Because PM10 are responsible for the increasing mortality rate of lung cancer and cardiovascular diseases, spatial distribution of PM10 are special interest in air pollution of Seoul. In this study, spatial analysis of Particulate Materials were monitored by monthly averaged PM10 concentration of 2010, 2011. The monthly spatial patterns of PM10 showed the west area of Seoul(Youngdungpo) higher PM10 concentration than northern part of Seoul in early spring and winter seasons. In the comparison of PM10 concentration distribution patterns in 2010 and 2011, the PM10 concentration of 2011 at Gangnam and Songpa-gu were more increased than yearly averaged patterns of 2010. The distribution patterns of PM10 in Seoul city showed the high concentration PM10 of several areas with Youngdungpo-gu, Gangnam-gu and Cheongnyangni. Therefore we need to establish PM10 management strategy for these area.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.423-433
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• 2018

Particulate matter air pollution is a serious problem affecting human health and visibility. The variations in $PM_{10}$ concentrations are influenced by not only local emission sources, but also atmospheric circulation conditions. In this study, we investigate the temporal features of $PM_{10}$ concentrations in South Korea and the atmospheric circulation patterns associated with high concentration episodes of $PM_{10}$ during winter (December-January-February) 2001-2016. Based on those analyses, a Korea Particulate matter Index (KPI) is developed to represent the large-scale atmospheric pattern associated with high concentration episodes of $PM_{10}$. The atmospheric patterns are characterized by persistent high-pressure anomalies, weakened lower-level north-westerly anomalies, and northward shift of the upper-level meridional wind anomalies near the Korean Peninsula. To evaluate the change in occurrence of high concentration episodes of $PM_{10}$ under a possible future warmer climate, we apply KPI analysis to CMIP5 climate simulations. Here, historical and two representative concentration pathway (RCP) scenarios (RCP 4.5 and RCP 8.5) are used. It is found that the occurrence of atmospheric conditions favorable for high $PM_{10}$ concentration episodes tends to increase over South Korea in response to climate change. This suggests that large-scale atmospheric circulation changes under future warmer climate can contribute to increasing high $PM_{10}$ concentration episodes in South Korea.

• Journal of the Korean earth science society
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• v.31 no.3
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• pp.234-245
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• 2010

Twenty-four hour integrated $PM_{10}$ and $PM_{2.5}$ samples were measured during springtime (March, April, and May) in Busan for three years from 2006 to 2008, and mass concentrations and metallic elements of measurement were analyzed to investigate temporal, spatial, chemical characteristics of the mass concentration and metallic elements in association with meteorological conditions including Asian Dust (AD) vs. non Asian Dust (NAD) seasons, and other air mass transport patterns. The result showed that $PM_{10}$, $PM_{2.5}$ and $PM_{10-2.5}$ concentrations were on average of $126.2{\pm}89.8$, $85.5{\pm}41.6$, and $40.7{\pm}54.9{\mu}g/m^3$, respectively, and the $PM_{2.5}/PM_{10}$ and $PM_{10-2.5}/PM_{2.5}$ ratios were 0.70 and 0.48, respectively. The highest concentrations of PM were observed when air parcels were originated from both northwest sector covering Beijing and west sector including Shanghai areas.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.123-138
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• 2018

To sustainably monitor air pollution in Seoul, the number of Air Pollution Monitoring Station has been gradually increased by Korea's Ministry of Environment. Although particulate matters(PM), one of the pollutants measured at the stations, have an significant influence on human body, the concentration of PM in Korea came in second among 35 OECD member countries. In this study, using the data of PM concentration from the stations, distribution maps of PM10 and PM2.5 concentrations over Seoul were generated, and spatial factors potentially related to PM distribution were investigated. Based on a circumscribed hexagon about a circle in radius of 500 meters created as a basic unit, Seoul was sectionalized and PM concentration map was generated using the interpolation technique of 'inverse distance weighting'. The distributions of PM concentrations were investigated with commuting time by administrative district and the outcome was related with land-use type and volume of traffic. Results from this analysis indicated distribution pattern of PM10 concentration was different from that of PM2.5 by administrative district and time. The distribution of PM concentration was strongly related to not only the size of business and trafficked areas among the land-use type, but also the existence of urban green. Further analysis of the relationship between the PM concentration and detailed land-use and urban green maps can be helpful to identify spatial factors which have an impact on the PM concentration on the regional scale.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.517-523
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• 2019

PM₁₀ concentration is a spatiotemporal phenomenta and capturing data for such continuous phenomena is a difficult task. This study designed a model that enhances spatiotemporal resolution of PM₁₀ concentration levels using satellite imagery, atmospheric and meteorological sensor data, and multiple deep learning models. The designed deep learning model was trained using input data whose factors may affect concentration of PM₁₀ such as meteorological conditions and land-use. Using this model, PM₁₀ images having 15 minute temporal resolution and 30m×30m spatial resolution were produced with only atmospheric and meteorological data.