• 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 The Korean Society of Agricultural Engineers
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• v.60 no.2
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• pp.1-11
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• 2018

Probable Maximum Flood (PMF) is mostly applied for the designs of large-scale hydraulic structures and it is estimated by computing the runoff hydrograph where Probable Maximum Precipitation (PMP) is inserted as design rainfall. The existing PMP is estimated by transferring the heavy rainfall from all watersheds of korea to the design watershed, however, in this study, PMP was analyzed by selecting only rainfall events occurred in the design watershed. And then, Catchment-scale Soil Erosion Model (CSEM) was used to estimate the PMF and sediment-runoff yield according to the watershed-based estimated PMP. Although the PMF estimated in this study was lower than the existing estimated PMF in the Yongdam-dam basin, it was estimated to be higher than the 200-year frequency design flood discharge. In addition, sediment-runoff yield was estimated with a 0.05 cm of the maximum erosion and a 0.06 cm of the maximum deposition, and a total sediment-runoff yield of 168,391 tons according to 24-hour PMP duration.

• 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.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 Industrial Technology
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• v.43 no.1
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• pp.57-62
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• 2023

In this review, we introduce the non-parametric Bayesian filtering algorithm known as the point-mass filter (PMF) and discuss recent studies related to it. PMF realizes Bayesian filtering by placing a deterministic grid on the state space and calculating the probability density at each grid point. PMF is known for its robustness and high accuracy compared to other nonparametric Bayesian filtering algorithms due to its uniform sampling. However, a drawback of PMF is its inherently high computational complexity in the prediction phase. In this review, we aim to understand the principles of the PMF algorithm and the reasons for the high computational complexity, and summarize recent research efforts to overcome this challenge. We hope that this review contributes to encouraging the consideration of PMF applications for various systems.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2005.11a
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• pp.466-467
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• 2005

• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.905-914
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• 2019

In this study, The PMF model was used to identify pollutant sources and their contribution to pollution sources of $PM_{2.5}$. The contribution of A city to each source was 19.8% for Secondary Sulfate, followed by Mobile 19.5%, Industry 16.0%, Biomass Buring 14.1%, Secondary Nitrate 14.1%, Oil Combustion 11.6%, Aged Sea Salt 2.6%, Soil 2.5% and so on. Sulfate and Ammonium concentrations were the highest contributing sources in the source profile, which was analyzed to be Secondary Aerosols produced by Photochemical Reactions of gaseous precursors (SOx and ammonia gas) in the atmosphere.

• 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.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.