• Proceedings of the KSEEG Conference
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• 2007.04a
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• pp.478-480
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• 2007

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• Proceedings of the KSEEG Conference
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• 2007.04a
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• pp.405-406
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• 2007

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• Journal of Soil and Groundwater Environment
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• v.22 no.5
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• pp.89-97
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• 2017

In this study, we evaluated effect of particle size on arsenic solid-state speciation and bioaccessibility in soils highly contaminated with arsenic from smelting and mining. Soils were partitioned into six particle size fractions ($2000-500{\mu}m$, $500-250{\mu}m$, $250-150{\mu}m$, $150-75{\mu}m$, $75-38{\mu}m$, <$38{\mu}m$), and arsenic solid-state speciation and bioaccessibility were characterized in each particle size fraction. Arsenic solid-state speciation was characterized via sequential extraction and XRD analysis, and arsenic bioaccessibility was evaluated by SBRC (Solubility Bioaccessibility Research Consortium) method. In smelter site soil, arsenic was mainly present as arsenic bound to amorphous iron oxides. Fine particle size fractions showed higher arsenic concentration, but lower arsenic bioaccessibility. On the other hand, arsenic in mine site soil showed highest concentration in largest particle size fraction ($2000-500{\mu}m$), while higher bioaccessibility was observed in smaller particle size fractions. Arsenic in mine site soil was mainly present as arsenolite ($As_2O_3$) phase, which seemed to affect the distribution of arsenic and arsenic bioaccessibility in different particle size fractions of the mine soil.

• Analytical Science and Technology
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• v.8 no.4
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• pp.405-410
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• 1995

A study on elemental speciation of trace metals in lake water (Lake Biwa in Japan) has been carried out by a size exclusion chromatography (SEC) / inductively coupled plasma mass spectrometry (ICP-MS) system. Before analysis, the water sample was preconcentrated with a ultrafiltration technique, where the large molecules with molecular weight larger than 10,000 were concentrated. Then the preconcentrated water samples (500-1000 fold) were analyzed by a SEC/ICP-MS system. Most trace metals were found at the UV absorption peaks corresponding to the molecular weights of ca. 300,000 and 10,000-50,000, where trace metals were on-line detected by ICP-MS. The results suggest that many of trace metals exist as the large organic molecules-metal complexes in natural water.

• Bulletin of Food Technology
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• v.22 no.1
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• pp.21-28
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• 2009

• Journal of Korean Society of Environmental Engineers
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• v.30 no.6
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• pp.621-627
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• 2008

In this study, a technique of speciation and determination of the trace inorganic arsenic(As(III) and As(V)) in water sample using HPLC-DRC-ICP-MS has been developed. Isocratic mobile phase of 10 mM ammonium nitrate and 10 mM ammonium phosphate monobasic was used and methanol(5 v/v%) was used as flushing solvent. Selection of the best flow rate of reaction gas, O$_2$, and optimization of the parameters such as pH and flow rate of mobile phase, and injection volume of sample for the separation and detection of arsenic species were carried out. The oxygen flow rate of 0.5 mL/min, pH of 9.4 and flow rate of 1.5 mL/min of mobile phase, and injection volume of sample of 100 $\mu$L were found to be the best parameters for the speciation and determination of arsenic species. The analytical features of the method were detection limit 0.10 and 0.08 $\mu$g/L, precision(RSD) 4.3% and 3.6%, and recovery 95.2% and 96.4% for As(III) and As(V), respectively. Analysis time was 4 minutes per sample. Linear calibration graphs with r$^2$ = 0.998 were obtained for both As(III) and As(V). Speciation analysis of arsenic species in the raw water samples collected from the tributary streams to Han River and main stream of Paldnag were performed by the proposed method. The concentrations of As(III) ranged from 0.10 to 0.22 $\mu$g/L and As(V) concentrations ranged from 0.44 to 1.19 $\mu$g/L, and 93.5% of total arsenic was found to be As(V).

• Journal of the Korean Chemical Society
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• v.60 no.6
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• pp.452-456
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• 2016

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.1
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• pp.11-39
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• 2018

Tritium and $^{14}C$ are two most important radionuclides released from nuclear facilities to the environment, and $^{14}C$ contributes dominant radiation dose to the population around nuclear power plants. This paper presents an overview of the production, pathway, species and levels of tritium and $^{14}C$ in nuclear facilities, mainly nuclear power plants. The methods for sampling and collection of different species of tritium and $^{14}C$ in the discharge gas from the stack in the nuclear facilities, atmosphere of the nuclear facilities and environment are presented, and the features of different methods are reviewed. The on-line monitoring methods of gaseous tritium and $^{14}C$ in air and laboratory measurement methods for sensitive determination of tritium and $^{14}C$ in collected samples, water and environmental solid samples are also discussed in detailed. Meanwhile, the challenges in the determination and speciation analysis of tritium and $^{14}C$ are also highlighted.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.186-191
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• 2004

Sorption kinetics of 2-chlorophenol(2-ChP), 2,4-dichlorophenol(2,4-DChP) and 2,4,5-trichlorophenol (2,4,5-TChP), onto montmorillonite modified with hexadecyltrimethyl ammonium cations(HDTMA-mont) were investigated. One-site mass transfer model(OSMTM) and two compartment first-order kinetic model(TCFOKM) were used to analyze kinetics. As expected from the number of model parameters involved, the three-parameter TCFOKM was better than the two-parameter OSMTM in describing sorption and desorption kinetics of chlorophenols in HDTMA-mont. For all chlorophenols, the results of OSMTM analysis indicate that the predominant deprotonated speciation(at pH 9.15) exhibited higher mass transfer coefficient( $k_{s}$ ) than the protonated speciation(at pH 4.85). This is because the deprotonated speciation has stronger hydrophobic interaction than protonated speciation. Most sorption completes in three hours. The fraction of the fast sorption and the first-order sorption rate constants for the fast and slow compartments in TCFOKM were determined by fitting experimental data to the TCFOKM. The results of kinetics reveal that the fraction of the fast sorption( $f_1$) and the sorption rate constants in the fast compartments( $k_1$) were in the order 2,4,5-TChP > 2,4-DChP > 2-ChP, which agrees with the magnitude of the $K_{ow}$ . The first-order sorption rate constants in the fast compartment(10$^{0.8}$ - 10$^{1.22}$ h $r^{-1}$ ) were much larger than those in the slow compartment(10$^{-1}$.74/ - 10$^{-2}$.622/ h $r^{-1}$ ).> ).).

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.3
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• pp.284-299
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• 2008

Emission input modules was developed to produce emission input data and change some profiles for Sparse Matrix Operator Kernel Emissions (SMOKE) using Clean Air Policy Support System (CAPSS)'s activities and previous studies. Specially, this study was focused to improve chemical speciation and temporal allocation profiles of SMOKE. At first, SCC cord mapping was done. 579 SCC cords of CAPSS were matched with EPA's one. Temporal allocation profiles were changed using CAPSS monthly activities. And Chemical speciation profiles were substituted using Kang et al. (2000) and Lee et al. (2005) studies and Kim et al. (2005) study. Simulation in Seoul Metropolitan Area (Seoul, Incheon, Gyeonggi) using MM5, SMOKE and CMAQ modeling system was done for effect analysis of changed input modules of SMOKE. Emission model results adjusted with new input modules were slightly changed as compared to using EPA's default modules. SMOKE outputs shows that aldehyde emissions were decreased 4.78% after changing chemical profiles, increased 0.85% after implementing new temporal profiles. Toluene emissions were decreased 18.56% by changing chemical speciation profiles, increased 0.67% by replacing temporal profiles as well. Simulated results of air quality were also slightly elevated by using new input modules. Continuous accumulation of domestic data and studies to develop input system for air quality modeling would produce more improved results of air quality prediction.