• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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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.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.1
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• pp.75-89
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• 2009

The purpose of this study was to identify $PM_{2.5}$ sources and to estimate their contributions to the border of Yongin-Suwon area, based on the analysis of the $PM_{2.5}$ mass concentration and the associated inorganic elements, ions and carbon components. The contribution of $PM_{2.5}$ sources were estimated by using a positive matrix factorization (PMF) model to identify air emission sources. For this study, $PM_{2.5}$ samples were collected from May, 2007 to April, 2008. The inorganic elements were analyzed by an ICP-AES. The ionic components in $PM_{2.5}$ were analyzed by an Ie. The carbon components were also analyzed by DRI/OGC analyzer. After performing PMF modeling, a total of 12 sources were identified and their contributions were quantitatively estimated. The contributions from each emission source were as follows: 11.3% from oil combustion source, 3.4% from bus/highway source, 5.8% from diesel vehicle source, 4.7% from gasoline vehicle source, 8.8% from biomass burning source, 15.1 % from secondary sulfate, 5.2% from secondary nitrate source, 13.4% from industrial related source, 4.1% from Cl-rich source, 19.6% from soil related source, 1.0% from aged sea salt, and 7.4% from coal combustion source, respectively. This study provides basic information on the major sources affecting air quality, and then it will help to effectively control $PM_{2.5}$ in this study area.

• Journal of Environmental Science International
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• v.3 no.4
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• pp.393-402
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• 1994

It is necessary to examine the source contributions and the relationship between a receptor and sources for the resonable controlling of air pollution level of suspended particulate matters. Therefore, in this study, profiles of sources contributing to the concentration of suspended particulate matters, were developed and CMB model was applied to obtain information of source contributions and feasibility of CMB model application. According to the propose of this study, twenty-seven chemical species such as the elements, anions, and total carbon of thirty-six PMl0 and TSP data sets sampled at the Pomch'on receptor site in Pusan for a 24-hr period from May to Aug. 1992, were analyzed and three (transportation, soil, marine) different source profiles were developed through the field measurement. Applying CMB model to transportation, soil, marine, the results of source contribution by CMB model showed that the case with TSP was more suitable for CMB model than that with PMl0. And the average contribution of transportation source to TSP and PMlo concentration at Pomch'on receptor was calculated as 90.66 ㎍/m3(64%) and 23.27 ㎍/m3(39%), resfiectively, which showed that the contribution by transportation was dominant. The validation of CMB model was performed by means of the results of contributions from marine source for the wind direction sectors.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.5
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• pp.477-485
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• 2000

The data set was collected on fifty-eight different days with a 24-h sampling period from October 27, 1995 through August 25, 1996. From the chemical mass balance (CMB) analysis of $PM_{2.5}$ in the Chongju area, the contributions from soil, gasoline, diesel, light and heavy oil combustion were 2.6%, 15.4%, 9.0%, 28.8% and 1.5%, respectively. Residual $NO_{3}^{-}$), residual $SO_{4}^{2-}$ and residual OC, possibly formed in the atmosphere. represented additional 8.0, 10.2, and 1.6% of the $PM_{2.5}$, respectively. Other unidentified sources constituted the remaining 22.9%. From the CMB analysis, the $PM_{2.5}$ source contribution for fall, winter, spring and summer were 92, 76.8, 77.5 and 59.2%, respectively.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.789-792
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• 2005

The dominant noise sources of Diesel Multiple Units are powerpack, which is composed of engine, transmission and cooling system, noise and wheel-rail rolling noise. The interior noise of a running vehicle is determined by structure-borne noise and air-borne noise from these noise sources. The contributions of interior noise from each noise source are calculated by air-borne transfer functions and structure-borne transfer functions of noise sources. In this paper, source separation technique is proposed to determine these transfer functions from the results of stationary and running tests of existing vehicle. With this technique, it is possible to get hold of contributions of interior noise from .noise sources of running vehicle. This source separation technique makes it possible to take efficient measures for reduction of interior noise at the early car-development stage.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.1
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• pp.122-127
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• 2008

The purpose of this study is to calculate atmospheric environmental capacity for $PM_{10}$, $NO_2$ and $SO_2$ using a rollback model in Chungju. From the results of this study, the source contributions for $PM_{10}$ and $NO_2$ was the following order: line source > area source > point source. However, the source contributions for $SO_2$ was the following: point source > area source > line source. While the atmospheric environmental capacity of $NO_2$ and $SO_2$ was enough to meet the regional air quality target, $PM_{10}$ emission needs to be reduced by 5%.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.E1
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• pp.32-43
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• 2008

Source samples were collected to construct source profiles for 9 different source types, including soil, road dust, gasoline/diesel-powered vehicles, a municipal incinerator, industrial sources, agricultural/biomass burning, marine aerosol, and a coal-fired power plant. Seasonal profiles for 'Chinese aerosol', aerosols derived from the urban area of China, were reconstructed from seasonal $PM_{2.5}$ compositions reported in Beijing, China. Ambient $PM_{2.5}$ at a receptor site was also measured during each of the four seasons, from April 2001 to February 2002, in Seoul. The Chemical Mass Balance receptor model was applied to quantify source contributions during the study period using the estimated source profiles. Consequently, motor vehicle exhaust (33.0%), in particular 23.9% for diesel-powered vehicles, was the largest contributor affecting the $PM_{2.5}$ levels in Seoul, followed by agricultural/biomass burning (21.5%) and 'Chinese aerosol' (13.1%), indicating contributions from long-range transport. The largest contributors by season were: for spring, 'Chinese aerosol' (31.7%); for summer, motor vehicle exhaust (66.9%); and for fall and winter, agricultural/biomass burning (31.1% and 40.1%, respectively). These results show different seasonal patterns and sources affecting the $PM_{2.5}$ level in Seoul, than those previously reported for other cities in the world.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.6-14
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• 2014

A new method to determine the total harmonic contributions of several customers and the utility at the point of common coupling is presented. The proposed method can quantify the individual harmonic impact of each suspicious harmonic source at the point of common coupling. The individual harmonic impact index is then used to assess the total harmonic contribution of each harmonic source. This index can be calculated by the results processed from instantaneous harmonic voltage and current phasor values. The results demonstrate the performance of the proposed method in terms of steady-state accuracy and response to time-varying operating conditions. The proposed index can be used for billing purposes to control harmonic distortion levels in power systems.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.6
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• pp.719-731
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• 2003

In order to maintain and manage ambient air quality, it is necessary to identify sources and to apportion its sources for ambient particulate matters. The receptor methods were one of the statistical methods to achieve reasonable air pollution strategies. Also, receptor methods, a field of chemometrics, is based on manifold applied statistics and is a statistical methodology that analyzes the physicochemical properties of gaseous and particulate pollutant on various atmospheric receptors, identifies the sources of air pollutants, and quantifies the apportionment of the sources to the receptors. The objective of this study was 1) after obtaining results from the PMF modeling, the existing sources of air at the study area were qualitatively identified and the contributions of each source were quantitatively estimated as well. 2) finally efficient air pollution management and control strategies of each source were suggested. The PMF model was intensively applied to estimate the quantitative contribution of air pollution sources based on the chemical information (128 samples and 25 chemical species). Through a case study of the PMF modeling for the PM-10 aerosols, the total of 11 factors were determined. The multiple linear regression analysis between the observed PM-10 mass concentration and the estimated G matrix had been performed following the FPEAK test. Finally the regression analysis provided quantitative source contributions (scaled G matrix) and source profiles (scaled F matrix). The results of the PMF modeling showed that the sources were apportioned by secondary aerosol related source 28.8 ％, soil related source 16.8％, waste incineration source 11.5%, field burning source 11.0%, fossil fuel combustion source 10%, industry related source 8.3％, motor vehicle source 7.9%, oil/coal combustion source 4.4％, non-ferrous metal source 0.3％. and aged sea- salt source 0.2％, respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.4
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• pp.275-288
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• 2009

The Korean government strengthened the environmental polices to manage and enhance Metropolitan Area air quality, and also has enforced "Special Act on Seoul Metropolitan Air Quality Improvement (SASMAQI)" issued in Dec. 2004. Recently government expanded the Seoul Metropolitan Air Quality Management District (SMAQMD) to the outskirts satellite cities of Seoul area through the "Revised Law Draft of SASMAQI". The SMAQMD has been alloted the allowable emission loads to the local governments on the basis of the carrying $PM_{10}$ capacity. However, in order to establish the effective air quality control strategy for $PM_{10}$, it is necessary to understand the corresponding sources which have a potential to directly impact ambient $PM_{10}$ concentration. To deal with the situations, many receptor methodologies have been developed to identify the origins of pollutants and to determine the contributions of sources of interests. The objective of this study was to extensively identify $PM_{10}$ sources and to estimate their contributions at the metropolitan area. $PM_{10}$ samples were simultaneously collected at the 3 semi-industrialized local cities in the Seoul metropolitan area such as Hwasung-si, Paju-si, and Icheon-si sites from April 15 to May 31, 2007. The samples collected on the teflon membrane filter by one $PM_{10}$ cyclone sampler were analyzed for trace metals and soluble ions and samples on the quartz fiber filter by another sampler were analyzed for OC and EC. Source apportionment study was then performed by using a positive matrix factorization (PMF) receptor model. A total of 6 sources were identified and their contributions were estimated in each monitoring site. Contribution results on Hwasung, Paju, and Icheon sites were as follows: 33%, 27%, and 27% from soil source, 26%, 26%, and 21% from secondary aerosol source, 11%, 11%, and 12% from biomass burning, 12%, 6%, and 5% from sea salt, 7%, 15%, and 19% from industrial related source, and finally 11%, 15%, and 16% from mobile and oil complex source, respectively. This study provides information on the major sources affecting air quality in the receptor sites and thus it will help to manage the ambient air quality in the metropolitan area by establishing reasonable control strategies, especially for the anthropogenic emission sources.