• 한국대기환경학회지
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• 제26권1호
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• pp.21-33
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• 2010

The objective of this study was to estimate the $PM_{2.5}$ source apportionment at the Pinnacles National Monument IMPROVE site in western coastal USA. The PMF was applied to identify the existing sources and apportion the $PM_{2.5}$ mass to each source. To analyze local source impacts from various wind directions, the NPR analysis was performed using source contribution results with the wind direction values measured at the site. Also, PSCF was applied to identify the locations by point sources relative to the back trajectories. A total of 1,634 samples were collected from March 1988 to May 2004 by IMPROVE sampler and 32 chemical species were analyzed by PIXE, PESA, XRF, IC, and TOR methods. The PMF modeling identified seven sources and the average mass was apportioned to gasoline vehicle, secondary sulfate, aged sea salt, secondary nitrate, wood/field burning, diesel emission, and soil, respectively. In this study, the average mass was apportioned to gasoline vehicle (33.0%), secondary sulfate (25.7%), aged sea salt (17.8%), and secondary nitrate (10.1%). Also, this study suggests the possible role for source apportionment study of $PM_{2.5}$ at similar areas such as wildness, national park, and coastal areas in Korea.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2003년도 International Symposium on Clean Environment
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• pp.216-225
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• 2003

Fine particles with aerodynamic diameter less than 2.5 ${\mu}m$ (PM2.5) were collected from three sites in Beijing during April, August, and November 2000 and January 2001. After chemical components in samples are analyzed, a chemical mass balance (CMB) receptor model using PARs as tracers is applied to quantify the source contributions to PM2.5 in Beijing. The results show that the major sources are coal combustion, fugitive dust, vehicle exhaust, secondary sulfate and nitrate, and organic matter while biomass burning and construction dust contribute only a small fraction. In addition, source inventory in Beijing is used to determine the primary source contributions. The two methods result in comparable results. Source apportionment at three sampling sites presents similar contributions to PM2.5 although the sites are far away from each other. However, distinct seasonal pattern is presented for the source contributions from coal combustion, fugitive dust, biomass burning, secondary sulfate and nitrate.

• 한국대기환경학회지
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• 제8권2호
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• pp.112-120
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• 1992

Twenty four metal, nonmetal elements and 4 major anions in total suspended particulates (TSP) collected at two sites in Daejon city from october to december in 1991 by a Hi-vol sampler were thoroughly analyzed by Inductively Coupled Plasma/ Atomic Emission Spectrometry (ICP/AES) and Ion Chromatography (IC). These analyzed data were used to perform a receptor modeling using the Chemical Mass Balance (CMB) for the source apportionment of TSP sample. Approximately 60% TSP weight in industrial complex area was influenced by potential industrial sources and 25% was by heating fuels and automobile emissions, whereas a half of TSP in residential area was influenced by surrounding environment and more than 35% of TSP was influenced by heating fuels. The CMB model provided source apportionment results reasonably and scientifically with a minor limitation.

• 한국환경과학회지
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• 제17권4호
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• pp.359-364
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• 2008

PM10 samples were collected from July 2007 to Oct. 2007 at Gwaebopdong(inland area) and Dongsamdong(coastal area), in Busan. This paper investigates the contribution of emission sources to PM10 mass in Busan. Source apportionment results derived from the chemical mass balance(CMB) method. A source profiles applied in this study is organized to minimize the collinearity among sources type via statistical method. Source profiles applied in this study utilized a measured value of fine particle directly sampled from metropolitan area such as Seoul and Incheon, After a CMB modeling, sulfate and nitrate related sources among those contributing to PM10 in Busan showed high contribution by 36.53% in Gwaebopdong and 42.02% in Dongsamdong.

• 한국대기환경학회지
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• 제24권1호
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• pp.30-42
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• 2008

In this study, the chemical compositions of $PM_{2.5}$ samples collected at the Redwood National Park IMPROVE site in California from March 1988 to May 2004 were analyzed to provide source identification and apportionment. A total of 1,640 samples were collected and 33 chemical species were analyzed by particle induced X-ray emission, proton elastic scattering analysis, photon induced X-ray fluorescence, ion chromatography, and thermal optical reflectance methods. Positive matrix factorization (PMF) was used to develop source profiles and to estimate their mass contributions. The PMF modeling identified five sources and the average mass was apportioned to motor vehicle (35.8%, $1.58\;{\mu}g/m^3$), aged sea salt (23.2%, $1.02\;{\mu}g/m^3$), fresh sea salt (21.4%, $0.94\;{\mu}g/m^3$), wood/field burning (16.1%, $0.71\;{\mu}g/m^3$), and airborne soil (3.5%, $0.15\;{\mu}g/m^3$), respectively. To analyze local source impacts from various wind directions, the CPF and NPR analyses were performed using source contribution results with the wind direction values measured at the site. These results suggested that sources of $PM_{2.5}$ are also sources of visibility degradation and then source apportionment studies derived for $PM_{2.5}$ are also used for understanding visibility problem.

• 한국대기환경학회:학술대회논문집
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• 한국대기환경학회 2008년도 추계학술대회 논문집
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• pp.53-56
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• 2008

• 한국대기환경학회지
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• 제34권4호
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• pp.517-533
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• 2018

We utilize the CAMx (Comprehensive Air Quality Model with eXtensions) system and the PSAT (Particulate Source Apportionment Technology) diagnostic tool to determine the $PM_{2.5}$ concentration and to perform its source apportionment in the southeastern region of South Korea. For a year-long simulation, eight local authorities in the region such as Pohang, Daegu, Gyeongju, Ulsan, Busan-Gimhae, Gosung-Changwon, Hadong, and all remaining areas in Gyeongsangnam-do, are selected as source areas based on the emission rates of $NO_x$, $SO_x$, VOC, and primary PM in CAPSS (Clean Air Policy Support System) 2013 emissions inventory. The CAMx-PSAT simulation shows that Pohang has the highest $PM_{2.5}$ self-contribution rate (25%), followed by Hadong (15%) and Busan-Gimhae (14%). With the exception of Pohang, which has intense fugitive dust emissions, other authorities are strongly affected by emissions from their neighboring areas. This may be measured as much as 1 to 2 times higher than that of the self-contribution rate. Based on these estimations, we conclude that the efficiency of emission reduction measures to mitigate $PM_{2.5}$ concentrations in the southeastern region of South Korea can be maximized when the efforts of local or regional emission controls are combined with those from neighboring regions. A comprehensive control policy planning based on the collaboration between neighboring jurisdictional boundaries is required.

• Asian Journal of Atmospheric Environment
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• 제8권3호
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• pp.115-125
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• 2014

In this study, particle composition data for $PM_{10}$ samples were collected every 3 days at Seoul, Korea from August 2006 to November 2007, and were analyzed to provide source identification and apportionment. A total of 164 samples were collected and 21 species (15 inorganic species, 4 ionic species, OC, and EC) were analyzed by particle-induced x-ray emission, ion chromatography, and thermal optical transmittance methods. Positive matrix factorization (PMF) was used to develop source profiles and to estimate their mass contributions. The PMF modeling identified nine sources and the average mass was apportioned to secondary nitrate (9.3%), motor vehicle (16.6%), road salt (5.8%), industry (4.9%), airborne soil (17.2 %), aged sea salt (6.2%), field burning (6.0%), secondary sulfate (16.2%), and road dust (17.7%), respectively. The nonparametric regression (NPR) analysis was used to help identify local source in the vicinity of the sampling area. These results suggest the possible strategy to maintain and manage the ambient air quality of Seoul.

• 한국대기환경학회:학술대회논문집
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• 한국대기환경학회 2005년도 추계학술대회 논문집
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• pp.83-84
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• 2005

• 한국대기환경학회:학술대회논문집
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• 한국대기환경학회 2005년도 추계학술대회 논문집
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• pp.301-302
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• 2005