• Economic and Environmental Geology
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• v.42 no.6
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• pp.599-608
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• 2009

This study was conducted to examine chemical characteristics and correlations among seepage water, subsurface waters and inland groundwater in and around a coastal underground LPG cavern using factor and cluster analyses. The study area is located in western coast of Incheon metropolitan city and is about 8 km off the coast. The LPG cavern storing propane and butane was built beneath artificially reclaimed island. Mean bathymetry is 8.5 m and maximum sea level change is 10 m. Water sampling was conducted in May and August, 2006 from 22 sampling points. Correlation analysis showed strong correlations among $Fe^{2+}$ and $Mn^{2+}$ (r=0.83~0.99), and Na and Cl (r=0.70~0.97), which indicated reductive dissolution of iron and manganese bearing minerals and seawater ingression effect, respectively. According to factor analysis, Factors 1 (May) and I (August) showed high loadings for parameters representing seawater ingression into the cavern and effect of submarine groundwater discharge, respectively while Factors 2 and IV showed high loadings for those representing oxidation condition (DO and ORP). Factors 4 and II have large positive loadings for $Fe^{2+}$ and $Mn^{2+}$. The increase of $Fe^{2+}$ and $Mn^{2+}$ was related to decomposition of organic matter and subsequent their dissolution under reduced condition. Cluster analysis showed the resulting 6 groups for May and 5 groups for August, which mainly included groups of inland groundwater, cavern seepage water, sea water and subsurface water in the LPG storage cavern. Subsurface water (Group 2 and Group III) around the underground storage cavern showed high EC and major ions contents, which represents the seawater effect. Cavern seepage water (Group 5 and Group II) showed a reduced condition (low DO and negative ORP) and higher levels of $Fe^{2+}$ and $Mn^{2+}$.

• Korean Journal of Soil Science and Fertilizer
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• v.29 no.1
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• pp.74-80
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• 1996

This experiment was conducted to obtain the basis of degradation of remaining agricultural chemicals accumulated in upland soils of Honam district in Korea. The population. relative growth rate(RGR). chlorothalonil(TPN)-degradation ability and bacterialogical characteristics of TPN resistant strains were investigated in TPN levels of 0, 25, 50, 100 and $500{\mu}l/ml$ compared with Mancozeb. A number of TPN-resistant bacteria were differ in the area of examined and were decreased with higher levels of TPN. The resistance of bacteria was stronger in TPN than Mancozeb but the resistance of fungi was vise versa. RGR of bacteria in the culture was the highest at the level of $50{\mu}l/ml$ and the lowest in $500{\mu}l/ml$ of TPN. TPN-degradation ability of bacteda isolated in various TPN levels was varied : only 8 percentage of bacteria showed 75 percentage or more degradation ability. The higher the concentration in TPN resistance, the larger the number of strains carried great ability to decompose pesticide residues. The strains having higher decomposition ability was rod-shapes cells and senstive to heat. Analyses of the indol production, methyl red, and V-P test have given similar results, with negative reaction in all these strain, while the other biochemical characteristics were differ in the strains. Based on these, these strains might be classified into Pseudomonas sp., Corynebacterium sp., Acinetobacter sp. and Moraxcella sp.

• Environmental and Resource Economics Review
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• v.14 no.1
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• pp.167-199
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• 2005

The following policy implications can be drawn from this study: 1) The Air Pollution Emission Amount Report published by the Ministry of Environment since 1991 classifies industries into 4 sectors, i. e., heating, manufacturing, transportation and power generation. Currently, the usability of report is very low and extra efforts should be given to refine the current statistics and to improve the industrial classification. 2) Big pollution industries are as follows - s7, s17 and s20. The current air pollution control policy for these sectors compared to other sectors are found to be inefficient. This finding should be noted in the implementation of future air pollution policy. 3) s10 and s17 are found to be a big polluting industrial sector and its pollution reduction effect is also significant. 4) The effect of emission coefficient (${\Delta}f$) has the biggest impact on the reduction of emission amount change and the effect of economic growth coefficient (${\Delta}y$) has the biggest impact on the increase of emission volume. The effect of production technology factor (${\Delta}D$) and the effect of the change of the final demand structure (${\Delta}u$) are insignificant in terms of the change of emission volume. 5) Further studies on emission estimation techniques on each industry sector and the economic analysis are required to promote effective enforcement of the total volume control system of air pollutants, the differential management of pollution causing industrial sectors and the integration of environment and economy. 6) Korea's economic growth in 1990 is not pollution-driven in terms of the Barry Commoner's hypothesis, even though the overall industrial structure and the demand structure are not environmentally friendly. It indicates that environmental policies for the improvement of air quality depend mainly on the government initiatives and systematic national level consideration of industrial structures and the development of green technologies are not fully incorporated.

• The Korean Journal of Pesticide Science
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• v.16 no.3
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• pp.217-221
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• 2012

This study was carried out to evaluate residual characteristics of tolclofos-methyl in ginseng and elucidate the reason for its high detection rate from fresh ginseng selling at markets. Seeds of ginseng were sowed after seed dressing with tolclofos-methyl and after a year of growth, the young seedlings were transplanted to field. They were then harvested annually until three-years of growth and the pesticide residue was analyzed in them. LOD and LOQ of the pesticide were 0.001 and 0.003 mg/kg, respectively. Recovery test was carried out to validate the analytical method for tolclofos-methyl in ginseng. The ginseng seedlings were fortified with the test pesticide at the level of LOQ, ten times of LOQ and maximum residue concentration of tolclofos-methyl. Its recovery ranged from 77.37 to 100.16%. Residual concentration of tolclofos-methyl in ginseng seedlings just before transplanting and two-year-old ginseng were from 7.58 to 8.05 and from 6.46 to 6.79 mg/kg, respectively. In case of three-year-old ginseng, it was found to be from 4.18 to 4.35 mg/kg. As a result of annual pesticide residue analysis, concentration of the pesticide was found to decrease time-coursely in ginseng. This may be due to decomposition and increasing of fresh weight of the ginseng during the cultivation periods of three years.

• Journal of Environmental Health Sciences
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• v.49 no.2
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• pp.66-77
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• 2023

Background: Along with the increase in consumer interest in and consumption of tattoo products, the controversy over harmful heavy metals associated with the use of tattoo cosmetics is also increasing. Therefore, investigation of hazardous metals in these tattoo products is needed. Objectives: This study was performed to provide useful data for establishing reasonable standards to securely manage tattoo cosmetics, tattoo stickers, and tattoo inks distributed in the market. Methods: Thirteen kinds of hazardous metal contents (Pb, As, Cd, Sb, Ni, Co, Cu, Cr, Se, Ba, Zn, Sn, and Hg) were analyzed for 23 tattoo cosmetics, ten tattoo stickers, and 16 tattoo inks. Hg was measured through the combustion-gold amalgamation method, and other hazardous metals were measured by inductively coupled plasma-mass spectrometry (ICP-MS) after acidic decomposition using a microwave apparatus. Results: The detected ranges of Pb, As, Cd, Sb, Ni, and Hg in tattoo cosmetics were 0.07~1.18, 0.06~0.41, ND~0.07, 0.01~3.44, 0.12~2.75, and ND~0.01 ㎍/g, respectively. All of the hazardous metals detected were below the recommended maximum standards of the Ministry of Food and Drug Safety. The mean amount of Pb detected in tattoo stickers for children was 0.24 ㎍/kg and Cd was not detected, meaning both metals met the recommended criteria. There was no statistically significant difference in all measured metals between children's tattoo stickers and adults' tattoo stickers. In the results of the study on the hazardous metal content of tattoo inks, four products (25%) for Pb, one product (6%) for As, 13 products (81%) for Ni, four products (25%) for Cu, and five products (31%) for Zn exceeded the recommended standards approved by the government. The highest predicted exposure amount for hazardous metals exceeding the recommended level of tattoo inks in a single tattooing was 5.69 ㎍/kg for Ni, 8.51 ㎍/kg for Zn, 0.44 ㎍/kg for Pb, 8.07 ㎍/kg for Cu, 0.44 ㎍/kg for As, and 71.36 ㎍/kg for Ba. Conclusions: It is necessary to prepare criteria for content limitation for the management of Co, Cr, Ba and Se tattoo cosmetics, and tattoo inks require thorough quality control.

• Clean Technology
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• v.30 no.1
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• pp.37-46
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• 2024

Environmental problems caused by plastic waste have been continuously growing around the world, and plastic waste is increasing even faster after COVID-19. In particular, PP and PE account for more than half of all plastic production, and the amount of waste from these two materials is at a serious level. As a result, researchers are searching for an alternative method to plastic recycling, and plastic pyrolysis is one such alternative. In this paper, a numerical study was conducted on the pyrolysis behavior of non-condensable gas to predict the chemical reaction behavior of the pyrolysis gas. Based on gas products estimated from preceding literature, the behavior of non-condensable gas was analyzed according to temperature and residence time. Numerical analysis showed that as the temperature and residence time increased, the production of H₂ and heavy hydrocarbons increased through the conversion of the non-condensable gas, and at the same time, the CH₄ and C₆H₆ species decreased by participating in the reaction. In addition, analysis of the production rate showed that the decomposition reaction of C₂H₄ was the dominant reaction for H₂ generation. Also, it was found that more H₂ was produced by PE with higher C₂H₄ contents. As a future work, an experiment is needed to confirm how to increase the conversion rate of H₂ and carbon in plastics through the various operating conditions derived from this study's numerical analysis results.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.260-276
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• 2024

The purpose of this study is to explore whether pre-service teachers majoring in earth science improve their perception of computational thinking through STEAM classes focused on engineering-based wave power plants. The STEAM class involved designing the most efficient wave power plant model. The survey on computational thinking practices, developed from previous research, was administered to 15 Earth science pre-service teachers to gauge their understanding of computational thinking. Each group developed an efficient wave power plant model based on the scientific principal of turbine operation using waves. The activities included problem recognition (problem solving), coding (coding and programming), creating a wave power plant model using a 3D printer (design and create model), and evaluating the output to correct errors (debugging). The pre-service teachers showed a high level of recognition of computational thinking practices, particularly in "logical thinking," with the top five practices out of 14 averaging five points each. However, participants lacked a clear understanding of certain computational thinking practices such as abstraction, problem decomposition, and using bid data, with their comprehension of these decreasing after the STEAM lesson. Although there was a significant reduction in the misconception that computational thinking is "playing online games" (from 4.06 to 0.86), some participants still equated it with "thinking like a computer" and "using a computer to do calculations". The study found slight improvements in "problem solving" (3.73 to 4.33), "pattern recognition" (3.53 to 3.66), and "best tool selection" (4.26 to 4.66). To enhance computational thinking skills, a practice-oriented curriculum should be offered. Additional STEAM classes on diverse topics could lead to a significant improvement in computational thinking practices. Therefore, establishing an educational curriculum for multisituational learning is essential.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.