• 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.

• 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)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2005.11a
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• pp.83-84
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• 2005

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.11a
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• pp.124-125
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• 2004

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.04a
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• pp.237-238
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• 2006

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2009.10a
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• pp.430-431
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• 2009

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.1
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• pp.85-97
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• 2006

The PAMS data collected at four sites in Seoul metropolitan area in 2004 were analyzed using the positive matrix factorization (PMF) technique, in order to identify the possible sources and estimate their contributions to ambient VOCs. Ten sources were then resolved at Jeongdong, Bulgwang, Yangpyeong, and Seokmo, including vehicle exhaust, LPG vehicle, petroleum evaporation, coating, solvent, asphalt, LNG, Industry & heating, open burning, and biogenic source. The PMF analysis results showed that vehicle exhaust commonly contributed the largest portion of the predicted total VOCs mass concentration, more than $30\%$ at four sites. The contribution of other resolved sources were significantly different according to the characteristics of site location. In the case of Jeongdong and bulgwang located in urban area, various anthropogenic sources such as coating, solvent, asphalt, residual LPG, and petroleum evaporation contributed about $40\%$ of total VOCs mass. On the other hand, at yangpyeong and Seokmo located in rural and remote area, the portion of these anthropogenic sources was reduced to less than $30\%$ and the contribution of natural sources including open burning and biogenic source clearly observed. These results were considerably corresponding to the emission inventory investigated in this region.

• 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.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.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.04a
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• pp.291-293
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• 2000

대기 연구자들은 대기오염의 일반적인 현황과 대기오염 유발의 근본 원인 파악, 저감 대책 등에 대한 연구를 활발히 수행하고 있다 하지만 이러한 연구들 중에서 대기오염의 근본 원인을 파악하기 위한 오염원 (Source) 추정 연구는 국내외적으로 매우 미진하다. 대기질의 평가와 예측은 분산모델과 수용방법론을 이용하는데, 분산모델에 내재되어 있는 한계성과 제약점 때문에 수용체에서 오염물질의 특성을 분석한 후, 오염원의 기여도를 평가하는 수용방법론이 지속적으로 개발되고 있다. (중략)