• Aerospace Engineering and Technology
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• v.5 no.2
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• pp.227-239
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• 2006

Reliability prediction provides a rational basis for design decisions such as the choice between alternative concepts, choice of part quality levels, derating factors to be applied, use of proven versus state-of-the-art techniques, and other factors. For this reasons, reliability prediction is essential functions in developing space systems. The worth of the quantitative expression lies in the information conveyed with the numerical value and the use which is made of that information and reliability prediction should be initiated early in the configuration definition stage to aid in the evaluation of the design and to provide a basis for item reliability allocation (apportionment) and establishing corrective action priorities. Reliability models and predictions are updated when there is a significant change in the item design availability of design details, environmental requirements, stress data, failure rate data, or service use profile. In this paper, the procedure, selection of reliability data and methods for space system reliability prediction is presented.

• Korean Journal of Construction Engineering and Management
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• v.8 no.3
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• pp.125-133
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• 2007

If project time extension were required in the construction projects, either liquidated damages or extension costs should be applied according to causation of the schedule delays. However, in actual cases it is not applied so far according to the contract conditions. The reason why this situation happened Is that function of the present planning and scheduling is not working feasibly. The CPM schedule could not provide a proper solution for apportioning responsibility for the schedule delays. This situation could be considered as breach of contract and will cause potential disputes for schedule delay. Therefore, in this research process based contract administration model for construction delay claim is proposed to prevent schedule delay and solve the claims. The model is based on pro-active management for causation of delay to provide apportionment of responsibility and written evidences.

• Journal of Environmental Science International
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• v.16 no.3
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• pp.299-310
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• 2007

It will be necessary to make proper management plans to preserve the air quality in good level for the public. In order to make these plans, source information and detail emission inventories of the city and near industrial areas should be given. However, lack of the source measurements data makes us more difficult to complete the source inventory. VOC source Inventory could be utilized for the feasibility study to estimate the contribution of VOC sources presenting to the receptor such as residential area. It may give policy maker an idea how to control the air quality, and improve their social environment in the area. This study shows data that measured VOCs concentrations from the local industrial areas in Jeonju during from May 2005 to January 2006. The samples were collected from the near sources in 7 major factories in the industrial park as well as 5 general sources in near city Jeonju area to elucidate the abundances of speciated VOCs and their spacial and temporal distributions depending on source bases. Industrial sources are as follows; chemical, food, paper, wood, metal, non-metal (glass), and painting (coating) industries. The 5 general sources are sampled from tunnel, gasoline gas station, dry cleaning shop, printing (copy) shop, and road pavement working place in urban area. To understand the near source effect at receptor, samples from the 2 receptor sites (one is at center of the industrial complex and the other site is at distance residential area downwind from the center) were collected and analyzed for the comparison to source concentration. The mass contributions of the speciated VOC to total mass of VOCs measured from the different sources and ambient (2 receptors) were presented and discussed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.1020-1026
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• 2011

Seasonal characteristics of water soluble ions in atmospheric particles in Cheonan were studied between 2008 and 2009. $Na^+$, $NH_4^+$ and $NO_3^-$, $SO_4^{2-}$ were the principle cations and anions in both coarse and fine particles. Water soluble ions occupied 24.4%(spring), 33.2%(summer), 40.7%(fall), and 39.6%(winter) of the total mass of coarse particles. In fine particles, 43.0%(spring), 59.7%(summer), 55.4%(fall), and 53.2%(winter) of mass were occupied by water soluble ions. From the factor analysis, 2 and 4 factors were extracted for water soluble ions in coarse and fine particles, respectively. 70.33% of water ions in the coarse particles were estimated from the natural source, but 66.01% in the fine particles were from the anthropogenic source.

• 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.

• Journal of Environmental Science International
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• v.32 no.4
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• pp.243-257
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• 2023

This study was conducted to investigate the distribution characteristics, source identification, and health risk of polycyclic aromatic hydrocarbons (PAHs) present in particulate matter 10 (PM-10), in Gwangju. PM-10 samples were collected from September 2021 to August 2022 from three sampling sites, one located in each of the following areas: green, residential, and industrial. The average concentrations of PAHs were found to be higher in the industrial area (9.75±6.51 ng/㎥) than in the green (6.90±2.41 ng/㎥) and residential (6.74±2.38 ng/㎥) areas. Throughout the year and across all sites, five-ring PAHs accounted for the largest proportion (29.8-34.5%) of all PAHs. The concentrations of PAHs showed distinct seasonal variations, with the highest concentration observed in winter, followed by autumn, spring, and summer. Source apportionment analyses were performed using diagnostic ratios and principal component analyses, which indicated that coal/biomass combustion and vehicle emissions were the primary sources of PAHs in PM-10. The incremental lifetime cancer risk was estimated for all age groups at all sampling sites, and the results revealed a much lower risk level than the standard acceptable risk level (1×10^-6).

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.5
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• pp.538-555
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• 2012

The purpose of this study was to understand $PM_{2.5}$ chemical characteristics on the Suwon/Yongin area and further to quantitatively estimate $PM_{2.5}$ source contributions. The $PM_{2.5}$ sampling was carried out by a high-volume air sampler at the Kyung Hee University-Global Campus from November, 2010 to October, 2011. The 40 chemical species were then analyzed by using ICP-AES(Ag, Ba, Cr, Cu, Fe, Mn, Ni, Pb, Si, Ti, V and Zn), IC ($Na^+$, $K^+$, $NH_4{^+}$, $Mg^{2+}$, $Ca^{2+}$, $NO_3{^-}$, ${SO_4}^{2-}$ and $Cl^-$), DRI/OGC (OC1, OC2, OC3, OC4, OP, EC1, EC2 and EC3) and GC-FID (acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benzo[a]anthracene, benzo[b]fluoranthene, benzo[a] pyrene, indeno[1,2,3-cd] pyrene, benzo[g,h,i]perylene and dibenzo[a,h,]anthracene). When applying PMF model after performing proper data treatment, a total of 10 sources was identified and their contributions were quantitatively estimated. The average contribution to $PM_{2.5}$ emitted from each source was determined as follows; 26.3% from secondary aerosol source, 15.5% from soil and road dust emission, 15.3% from vehicle emission, 15.3% from illegal biomass burning, 12.2% from incineration, 7.2% from oil combustion source, 4.9% from industrial related source, and finally 3.2% from coal combustion source. In this study we used the ratios of PAHs concentration as markers to double check whether the sources were reasonably classified or not. Finally we provided basic information on the major $PM_{2.5}$ sources in order to improve the air quality in the study area.

• Journal of Korean Society on Water Environment
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• v.35 no.6
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• pp.510-519
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• 2019

The 'Stable Isotope Analysis in R' (SIAR), one of the Bayesian mixing models for stable isotopes, has been proven to be useful for source apportionment of nitrates in rivers. In this study, the contribution ratios of nitrate sources were quantified by using the SIAR based on nitrogen and oxygen stable isotope measurements in the Yeongsan River. From the measurements, it was found that the values of δ¹⁵N-NO₃ and δ¹⁸O-NO₃ ranged from -8.2 ‰ to +13.4 ‰ and from +2.2 ‰ to +9.8 ‰, respectively. We further analyzed the contribution ratios of the five nitrate sources by using the SIAR. From the modeling results, the main nitrate source was found to be soil N (29.3 %), followed by sewage (26.7 %), manure (19.6 %), chemical fertilizer (17.9 %) and precipitation (6.3 %). From the results, it was found that the anthropogenic sources, i.e., sewage, manure and chemical fertilizer contribute 64.2% of the total nitrate inflow from the watershed. Due to the significant correlation of δ¹⁵N-NO₃ and lnNO₃^- in this study, the fractionation factors reflecting the biogeochemical processes of stable isotope ratios could be directly obtained. This may make the contribution ratios obtained in this study more precise. The fractionation factors were identified as +3.64 ± 0.91 ‰ for δ¹⁵N-NO₃ (p<0.01) and -5.67 ± 1.73 ‰ for δ¹⁸O-NO₃(p<0.01), respectively, and were applied in using the SIAR. The study showed that the stable isotope method using the SIAR could be applied to quantitatively calculate the contribution ratios of nitrate sources in the Yeongsan River.

• Journal of Environmental Science International
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• v.26 no.11
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• pp.1297-1306
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• 2017

This study identified physical characteristics and aerosol particle sources of $PM_{10}$ and $PM_{2.5}$ in the industrial complex of Busan Metropolitan City, Korea. Samples of $PM_{10}$, $PM_{2.5}$ and also soil, were collected in several areas during the year of 2012 to investigate elemental composition. A URG cyclone sampler was used for collection. The samples were collected according to each experimental condition, and the analysis method of SEM-EDX was used to determine the concentration of each metallic element. The comparative analysis indicated that their mass concentration ranged from 1% to 3%. The elements in the industrial region that were above 10% were Si, Al, Fe, and Ca. Those below 5% were Na, Mg, and S. The remaining elements (1% of total mass) consisted of elements such as Ni, Co, Br and Pb. Finally, a statistical tool was applied to the elemental results to identify each source for the industrial region. From a principal components analysis (SPSS, Ver 20.0) performed to analyze the possible sources of $PM_{10}$ in the industrial region, five main factors were determined. Factor 1 (Si, Al), which accounted for 15.8% of the total variance, was mostly affected by soil and dust from manufacturing facilities nearby, Factors 2 (Cu, Ni), 3 (Zn, Pb), and 4 (Mn, Fe), which also accounted for some of variance, were mainly related to iron, non-ferrous metals, and other industrial manufacturing sources. Also, five factors determined to access possible sources of $PM_{2.5}$, Factor 1 (Na, S), accounted for 13.5% of the total variance and was affected by sea-salt particles and fuel incineration sources, and Factors 2 (Ti, Mn), 3 (Pb, Cl), 4 (K, Al) also explained significant proportions of the variance. Theses factors mean that the $PM_{2.5}$ emission sources may be considered as sources of incineration, and metals, and non-ferrous manufacturing industries.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.4
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• pp.439-454
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• 2008

The purpose of this study was to extensively identify $PM_{10}$ sources and to estimate their contributions to the study area, based on the analysis of the $PM_{10}$ mass concentration and the associated inorganic elements, ions, and total carbon. The contribution of $PM_{10}$ sources was estimated by applying a receptor method because identifying air emission sources were effective way to control the ambient air quality. $PM_{10}$ particles were collected from May to November 2007 in the Yongin-Suwon bordering area. $PM_{10}$ samples were collected on quartz filters by a $PM_{10}$ high-volume air sampler. The inorganic elements (Al, Mn, V, Cr, Fe, Ni, Cu, Zn, Cd, Pb, Si, Ba, Ti and Ag) were analyzed by an ICP-AES after proper pre-treatments of each sample. The ionic components of these $PM_{10}$ samples ($Cl^_$, $NO_3^-$, $SO_4^{2-}$, $Na^+$, $NH_4^+$, $K^+$, $Ca^{2+}$, and $Mg^{2+}$) were analyzed by an IC. The carbon components (OC1, OC2, OC3, OC4, OP, EC1, EC2 and EC3) were also analyzed by DRI/OGC analyzer. Source apportionment of $PM_{10}$ was performed using a positive matrix factorization (PMF) model. After performing PMF modeling, a total of 8 sources were identified and their contribution were estimated. Contributions from each emission source were as follows: 13.8% from oil combustion and industrial related source, 25.4% from soil source, 22.1% from secondary sulfate, 12.3% from secondary nitrate, 17.7% from auto emission including diesel (12.1%) and gasoline (5.6%), 3.1% from waste incineration and 5.6% from Na-rich source. This study provides information on the major sources affecting air quality in the receptor site, and therefore it will help us maintain and manage the ambient air quality in the Yongin-Suwon bordering area by establishing reliable control strategies for the related sources.