• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.1
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• pp.206-217
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• 2001

The purpose of this study was to estimate that the relations of weathering speed and shear strength of granite soil by tracing the weathering depth of granite soil from the very moment of its cutting. The results obtained this follows ; 1) The relationships among Nc, Li and CEC, Li>6%, CEC>14 corresponds to Nc=2~30, and 4%${\phi}$)increases at a standard pressure. 3) And Nc=0~50 corresponds to $27{\sim}50^{\circ}$ of internal fiction angle and to 12~49kPa of cohesion. That is to say, internal friction angle(${\phi}$)corresponds better than cohesion(c). In conclusion, this study suggests that in simplified dynamic cone penetration test a penetration boundary line of 5 centimeters is decided at around Li=4%, CEC=3(meq/100g) which is classified as a completely weathering soil. It also appears that CEC increases as Li increases while Nc decreases.

• Journal of the Korean Geotechnical Society
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• v.15 no.1
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• pp.127-140
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• 1999

In this study, decomposed granite soil samples were taken from 16 different regions and their weathering properties were investigated by testing their physical and chemical properties. According to the results, the Chemical Weathering Index (CWI) ranges from 15.5 to 30 in general and 12 out of the 16 regions show over 20 in CWI. The 12 regions are categorized into complete granite soil areas by Sueoka's classification. CWI, Li. $SiO_2,\; \DeltaS/S$ and SAR are all correlated significantly and used as indicators by which the degree of weathering can be judged. The degree of weathering can be measured especially through Li or $SiO_2$ which is considered as an easy approach of geotechnical classification. Also, the relationship between the value of N and Li is regarded as a good indicator in estimating the degree of weathering.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.333-338
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• 2016

The purpose of this study was to understand the pollution level of the surface sediment of Sihwa Lake by assessing its geochemical characteristics and investigating the spatial distribution of trace metals and organic matter. In the surface sediment of Sihwa lake, the mean grain size was between 2.94 and 6.35 Ø and the main type of sediment was sandy silt. The concentrations of As, Co, Cr, Ni, V and Li among the metal elements in the surface sediment were correlated with the mean crust concentration (p<0.05). Based on the strong correlation between the metals (Cd, Cu, Pb and Zn) and organic matter (Ignition Loss), the concentrations of these metal elements seem to be controlled by the organic matter dilution effect. The trace metal pollution level, determined by applying the Republic of Korea Marine environmental standard and the US National Oceanic and Atmospheric Administration's sediment quality guidelines, showed the pollution level of As to be either close to or in excess of the above-mentioned standards at almost all levels. The enrichment factor and geoaccumulation index of As showed that there was an incremental increase of pollution by elements other than V, Cr, Co, Fe, Al and Mn. Moreover, the nearby industrial area and dike were more polluted than the other areas, so the surface sediments in Sihwa lake should be monitored by taking into consideration the geological variations.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.1
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• pp.1-13
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• 2012

We measured various geochemical parameters, including the grain size, loss on ignition(LOI), total organic carbon(TOC), total nitrogen(TN), total sulfur(TS) and metallic elements, in surface sediment collected from 19 stations in Gamak Bay in April 2010 in order to understand the sedimentary types, the origin of organic matters, and the distribution patterns of alkali(Li, Na, K, Rb) and alkaline earth(Be, Mg, Ca, Sr, Ba) elements. The surface sediments were mainly composed of mud. The concentrations of Chlorophyll-a, TOC, TN, TS and LOI in sediment were the highest at the cultivation areas of fish and shellfish in the northern and southern parts of the bay. The redox potential(or oxidation-reduction potential) showed the positive value in the middle part of the bay, indicating that the surface sediment is under oxidized condition. The organic materials in sediment at almost all of stations were characterized by the autochthonous origin. Based on the overall distributions of metallic elements, it appears that the concentrations of alkali and alkaline earth elements except Ba in sediment are mainly influenced by the dilution effect of quartz. The concentrations of Sr and Ba are also dependent on the secondary factors such as the effect of calcium carbonate and the redox potential.

• Analytical Science and Technology
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• v.27 no.1
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• pp.11-21
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• 2014

An investigation of grain size, organic compounds and metal distribution in 23 sediment samples of the Geum-River basin (Korea) was conducted in two seasons of 2012 (dry season and rainy season). The samples of sediment were collected from the basin and investigated for concentrations of some metal and general indexes containing grain size. Concentrations of Pb, Zn, Cu, Cr, Ni, As, Cd, Hg, Al and Li have been determined by inductively coupled plasma spectrometer (ICP) and the sediments organic matter content was determined by the loss on ignition, and sediments were fractionated with three different nylon sieves. Correlation analysis was made for grain size, organic material and metal concentrations, and the Pearson correlation coefficients between their concentrations were determined. As a result, the higher metal concentrations were found in the period of the dry season than in another season. The metal concentrations showed high correlation with that of organic material (COD and TOC). Thereby, the high distribution of metal concentrations in sediment containing high organic compound is suggesting an interaction with organic matter.

• The Korean Journal of Malacology
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• v.28 no.3
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• pp.225-232
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• 2012

In order to evaluate the relationship of between the contents of inorganic elements in shellfish and surface sediments, we measured the concentration of inorganic elements (As, Ca, Cd, Cr, Fe, Hg, Li, Mn, Ni, P, Pb, Zn) in various shellfish and surface sediments of research area. The enrichment factor (EF) and the index of accumulation rate (Igeo) of the metals showed that the research areas can be classified as moderately polluted, or unpolluted. And also we measured the geochemical characteristics of surface sediments(grain size, chemical oxygen demand (COD), ignition loss (IL) and acid volatile sulfide (AVS). The grain sizes for research areas of surface sediment were similar the ratio of silt and clay in comparison with other sites. The COD and IL in surface sediment ranged from 5.41 to 14.06 mg/g. d.w. (mean $8.78{\pm}3.16$ mg/g d.w.) and from 0.92 to 3.17% (mean $2.08{\pm}0.86%$), respectively. Siginificant differences in metal concentrations also were determinated in the shellfish tissue among the different sites. However, except for metals(Mn, Zn), which showed some elevation of concentrations, the variations in the shellfish tissue were not related to variations in the sediment.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.