• Journal of Korean Society for Atmospheric Environment
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• v.31 no.6
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• pp.508-519
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• 2015

The objective of this study was to quantitatively estimate $PM_{2.5}$ source contribution in Gyeongsan. Ambient $PM_{2.5}$ samples have been collected on zefluor, quartz and nylasorb filter by $PM_{2.5}$ samplers of cyclone method from September 2010 to December 2012. Collected samples were analyzed for determining 17 inorganic elements, 8 ions, and 8 carbon components after pretreatment. Based on these chemical information, the PMF model was applied to estimate the quantitative contribution of air pollution sources. The results of the PMF modeling showed that the sources were apportioned by biomass burning source (15.5%), secondary sulfate source (16.0%), industry source (10.4%), soil source (7.0%), gasoline source (9.1%), incinerator source (10.4%), diesel emission source (11.0%), and secondary nitrate source (20.6%), respectively. To analyze local source impacts from various wind directions, the CPF analysis were performed using source contribution results with the wind direction values measured at the site.

• 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.19 no.6
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• pp.701-717
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• 2003

The objective of this study was to extensively estimate the air quality trends of the study area by surveying con-centration trends in months or seasons, after analyzing the mass concentration of PM-10 samples and the inorganic lements, ion, and total carbon in PM-10. Also, the study introduced to apply the PMF (Positive Matrix Factoriza-tion) model that is useful when absence of the source profile. Thus the model was thought to be suitable in Korea that often has few information about pollution sources. After obtaining results from the PMF modeling, the existing sources at the study area were qualitatively identified The PM-10 particles collected on quartz fiber filters by a PM-10 high-vol air sampler for 3 years (Mar. 1999∼Dec.2001) in Kyung Hee University. The 25 chemical species (Al, Mn, Ti, V, Cr, Fe, Ni, Cu, Zn, As, Se, Cd, Ba, Ce, Pb, Si, N $a^{#}$, N $H_4$$^{+}$, $K^{+}$, $Mg^{2+}$, $Ca^{2+}$, C $l^{[-10]}$ , N $O_3$$^{[-10]}$ , S $O_4$$^{2-}$, TC) were analyzed by ICP-AES, IC, and EA after executing proper pre - treatments of each sample filter. 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 source profiles (scaled F matrix). So, 11 sources were qualitatively identified, such as secondary aerosol related source, soil related source, waste incineration source, field burning source, fossil fuel combustion source, industry related source, motor vehicle source, oil/coal combustion source, non-ferrous metal source, and aged sea- salt source, respectively.ively.y.

• Particle and aerosol research
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• v.12 no.2
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• pp.27-36
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• 2016

The PMF result was compared with the result from the Chemical Mass Balance (CMB) modelling (Lee and Kim, 2007) to estimate major source of PAHs observed at Seoul from August 2002 to December 2003. Five major sources were estimated from PMF and CMB modellings respectively. Among them three major sources (coal combustion for residential, coke oven and biomass burning) were identified at both models.

• 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.30 no.3
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• pp.219-234
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• 2021

Industrial emissions, mainly from industrial complexes, are important sources of ambient Volatile Organic Compounds (VOCs). Identification of the significant VOC sources from industrial complexes has practical significance for emission reduction. VOC samples were collected from July 2019 to June 2020. A Positive Matrix Factorization (PMF) receptor model was used to evaluate the VOC sources in the area. Four sources were identified by PMF analysis, including coating-1, coating-2, printing, and vehicle exhaust. The coating-1 source was revealed to have the highest contribution (41.5%), followed by coating-2 (23.9%), printing (23.1%), and vehicle exhaust (11.6%). The source showing the highest contribution was coating emissions, originating from the northwest to southwest of the sample site. It also relates to facilities that produce auto parts. The major components of VOC emissions from the coating facilities were toluene, m,p-xylene, ethylbenzene, o-xylene, and butyl acetate. Industrial emissions should be the top priority to meet the relevant control criteria, followed by vehicular emissions. This study provides a strategy for VOC source apportionment from an industrial complex, which is helpful in the development of targeted control strategies.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.11a
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• pp.206-207
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• 2004

• Journal of Korean Society of Environmental Engineers
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• v.33 no.9
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• pp.644-655
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• 2011

VOCs (Volatile Organic Compounds) have adverse effects on human health and have caused serious global air pollution problems such as ozone depletion and cimate changes. The total of 56 target VOCs were selected to be monitored in this study for 4 years (2006~2009). The VOCs were measured every hour. The concentration of BTEX was higher than the other target compounds. Generally, the levels of VOCs measured in this study were higher than those measured by the other studies because Gamjeon and Jangrim monitering sites are located in industrial areas. The seasonal variations showed that the VOCs were the highest in winter. The temporal variations showed that the VOCs were high during commuting time on weekday. PMF model was used to resolve source types and source contributions of VOCs in this study. Identified sources and quantified contributions resolved by PMF were vehicle exhaust (15.22%), thinning solvent (29.83%), surface coating (17.13%), industries (13.95%), LPG vehicle (15.22%), combustion boiler (7.11%) and biogenic source (6.61%). Thinning solvent and Surface coating were the most contributed sources possibly due to manufactures and automobile garages in Gamjeon and solvent and paint manufactures in Sasang-Gu.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2003.05b
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• pp.62-63
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• 2003

대기 중 입자상 및 가스상 오염물질에 대한 오염원의 영향을 확인하고 기여도를 정량화하기 위하여 수용방법론 (receptor methods)이 이용되고 있다. 수용방법론은 각종 응용통계학을 기반으로 한 계량화 학적 분석기술로서, 일반대기 중 수용체에서 가스상ㆍ입자상 오염물질의 물리ㆍ화학적 특성을 분석한 후, 대기질에 영향을 미치는 오염원을 확인하고 기여도를 정량적으로 파악하여 대기오염 관리를 합리적으로 수행할 수 있는 통계적 방법이다. 또한 수용방법론은 입자상 및 가스상 오염물질의 분석에 다각도로 응용할 수 있으며, 합리적인 대기오염 관리를 유도하는 기초기술이라 할 수 있다(황인조 등, 2001). (중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.11a
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• pp.189-190
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• 2002

PMF(Positive Matrix Factorization) 모텔은 기존의 인자분석 모델이 갖는 인자부하량의 음수 문제를 해결하기 위해 인자부하량과 공통인자를 양수로 제한하여 결과 해석에 명확성을 주었다. 또한 환경연구에서 많이 나타나는 outlier와 log-normal분포모형을 선택사항으로 도입하고 있어 현재 환경관련 연구에 응용성이 높다. 본 연구에서는 대전 1, 2 공단 지역의 PM 10 중 미량금속과 이온성분의 농도를 분석하고 PMF를 이용하여 오염원을 확인하고자 한다. (중략)