• 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.

• Asian Journal of Atmospheric Environment
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• v.5 no.2
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• pp.86-96
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• 2011

The positive matrix factorization (PMF2) and multilinear engine (ME2) models have been shown to be powerful environmental analysis techniques and have been successfully applied to the assessment of ambient particulate matter (PM) source contributions. Because these models are difficult to apply practically, the US EPA developed a more user-friendly version of the PMF. The initial version of the EPA PMF model does not provide any rotational capabilities; for this reason, the model was upgraded to include rotational functions in the EPA PMF ver. 2.0. In this study, PMF and EPA PMF modeling identified ten particulate matter sources including secondary sulfate I, vehicle gasoline, secondary sulfate II, secondary nitrate, secondary sulfate III, incinerators, aged sea salt, airborne soil particles, oil combustion, and diesel emissions. All of the source profiles determined by the two models showed excellent agreement. The calculated average concentrations of $PM_{2.5}$ were consistent between the PMF2 and EPA PMF ($17.94{\pm}0.30{\mu}g/m^3$ and $17.94{\pm}0.30\;{\mu}g/m^3$, respectively). Also, each set of estimated source contributions of the PMF2 and EPA PMF showed good agreement. The results from the new EPA PMF version applying rotational functions were consistent with those of PMF2. Therefore, the updated version of EPA PMF with rotational capabilities will provide more reasonable solutions compared with those of PMF2 and can be more widely applied to air quality management.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.E4
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• pp.203-213
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• 2002

Receptor modeling is one of statistical methods to achieve reasonable air pollution strategies. 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 main purpose of the study was to survey seasonal trends of inorganic elements in the coarse and fine particles. Second, this study has attempted emission sources qualitatively by a receptor method, the PMF mo-del. After that. both PMF (positive matrix factorization) model and TTFA (target transformation factor analysis) model were applied to compare and to estimate mass contribution of coarse and fine particle sources at the receptor. A total of 138 sets of samples was collected from 1989 to 1996 by a low volume cascade impactor with 9 size fraction stages at Kyung Hee University in Korea. Sixteen chemical species (Si, Ca, Fe, K, Pb, Na, Zn, Mg, Ba, Ni, V, Mn, Cr, Br, Cu. Co) were characterized by XRF. The study result showed that the weighted arithmetic mean of coarse and fine particles were 51.3 and 54.4 $\mu\textrm{g}$/㎥, respectively. Contribution of both particle fractions were esti-mated using TTFA and PMF models. The number of estimated sources was seven according to TTFA model and 8 according to PMF model. Comparison of TTFA and PMF revealed that both methodologies exhibited similar trends in their contribution pattern. However, large differences between contributions were observed in some sour-ces. The results of this study may help to suggest control strategies in local countries where known source profiles do not exist.

• 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.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.1
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• pp.100-107
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• 2008

The objective of this study was to quantitatively estimate PM-10 source contribution in Gumi City, Korea. Ambient PM-10 samples were collected by a high volume air sampler, which operated for 84 different days with a 24-h sampling basis, from June 14,2001 though May 19, 2003. The filter samples were analyzed for determining 13 inorganic elements, 3 anions, and a total carbon. The study has intensively applied a receptor model, the PMF (Positive Matrix Factorization) model. The results from PMF modeling indicated that a total of seven sources were independently identified and each source was contributed to the ambient Gumi City from secondary sulfate (34%), motor vehicle (26%), soil relation (5%), field burning (3%), industrial relation (3%), secondary nitrate (22%), and incinration (7%) in terms of PM-10 mass, respectively.

• 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.

• Asian Journal of Atmospheric Environment
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• v.8 no.3
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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.

• Journal of Korean Society for Atmospheric Environment
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• v.17 no.2
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• pp.133-145
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• 2001

The receptor modeling is one of the statistical methods to achieve reasonable air pollution strategies. The pur-pose of this study was to survey the concentration variability oi inorganic elements and ionic species in the PM-10 particles, to qualitatively characterize emission sources by an advanced algorithm called positive matrix factoriza-tion(PMF) as a receptor model that can strictly provide results in every loading matrix. A total of 254 samples was collected by a PM-10 high volume air sampler from Mar. 1997 to Feb. 1998 in Kyung Hee University at Suwon Campus. Fourteen chemical species(Zn, Cu, Fe, Pb, Al, Mn, $Na^{+}$, NH$_4$+, $K^{+}$, $Mg^{2+}$, $Ca^{2+}$, $SO_4^{2-}$, $NO_{3}^{-}$, and $Cl^{-}$) were determined by AAS and IC methods. The study results showed that the average monthly concentration of PM-10 particles were 86.3$\mu\textrm{g}$/$\textrm{m}^3$ in March (maximum) and 28.5$\mu\textrm{g}$/$\textrm{m}^3$ in August(minimum), respectively. The concentrations of Na+, NH$_4$+, $K^{+}$ and $Cl^{-}$ in winter, $Mg^{2+}$, $Ca^{2+}$ and $NO_{3}^{-}$, in spring, and $SO_4^{2-}$ in summer showed the largest peak concentration for the respective season. Through and app-lication of a PMF program of Pm-10 concentration data of Suwon, 9 sources were qualitatively identified , such as incineration source, oil burning source, soil related source, open burning source automobile source, coal burning sources, secondary sulfate related source, and secondary nitrate related source.

• 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 Korean Society for Atmospheric Environment
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• v.26 no.1
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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.