• The Journal of Engineering Geology
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• v.25 no.4
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• pp.577-585
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• 2015

Contamination of soil due to an oil spill influences its subsequent behavior. An investigation was conducted to study the effect of oil viscosity on compaction characteristics, coefficient of permeability, and shear strength. Water permeability was also determined by using Kerosene, Engine oil, and Crude-oil as contaminants. The test results indicate that the compaction characteristics are influenced by oil contamination. Direct shear test was conducted to investigate the effect of oil in the pore space in sandy ground. angle of internal friction of sand (based on total stress condition) decreases due to presence of oil within the pore spaces in sand.

• Korean Journal of Microbiology
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• v.34 no.3
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• pp.101-107
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• 1998

Biological treatment of Arabian light crude oil-contaminated pebble was investigated in laboratory microcosms after supplementation with inorganic nutrients and oil-degrading microorganisms. Glass columns ($10cm{\times}20cm$) were used as microcosms and each microcosm was filled with pebbles of diameter less than 40 mm. After initial oil contamination of 2.4% (w/v), Inipol EAP-22 or slow release fertilizer (SRF) was added as inorganic nutrients and microorganisms were sprayed over pebbles. When $C_{17}$/pristane and $C_{18}$/phytane ratios were used as a marker for oil biodegradation, both ratios for microcosm supplemented with SRF and microorganisms were the lowest (below detectable range) after 92 days. Elimination of oil by abiotic processes, however, were minimal with decrease of $C_{17}$/pristane and $C_{18}$/phytane ratios from 3.55 and 2.41 to 3.06 and 1.50, respectively. The numbers of heterotrophic and oil-degrading microorganisms, and biological activity (dehydrogenase activity) corresponded to the course of biodegradation activities in all microcosms. During the whole experimental period, there was no significant nutrient deficiency only in the microcosm with SRF and microorganisms. It seemed that a continuous supply of inorganic nutrients using SRF was the most important factor for the successful performance of biological treatment in oil-contaminated pebbles.

• Microbiology and Biotechnology Letters
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• v.39 no.3
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• pp.301-307
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• 2011

Oil pollution was world-wide prevalent treat to the environment, and the physic-chemical remediation technology of the TPH (total petroleum hydrocarbon) contaminated soil had the weakness that its rate was very slow and not economical. Bioremediation of the contaminated soil is a useful method if the concentrations are moderate and non-biological techniques are not economical. The aim of this research is to investigate the influence of additives on TPH degradation in a diesel contaminated soil environment. Six experimental conditions were conduced; (i) diesel contaminated soil, (ii) diesel contaminated soil treated with microbial additives, (iii) diesel contaminated soil treated with microbial additives and the mixture was titrated to the end point of pH 7 with NaOH, (iv) diesel contaminated soil treated with microbial additives and accelerating agents and (v) diesel contaminated soil treated with microbial additives and accelerating agents, and the mixture was titrated to the end point of pH 7 with NaOH. After 10 days, significant TPH degradation (67%) was observed in the DSP-1 soil sample. The removal of TPH in the soil sample where microbial additives were supplemented was 38% higher than the control soil sample during the first ten days. The microbial additives were effective in both the initial removal rate and relative removal efficiency of TPH compared with the control group. However, various environmental factors, such as pH and temperature, also affected the activities of microbes lived in the additives, so the pH calibration of the oil-contaminated soil would help the initial reduction efficiency in the early periods.

• Journal of Korea Soil Environment Society
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• v.1 no.2
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• pp.3-13
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• 1996

The efficiency of bioremedation can be measured by the enumeration of microorganism, respiration rate and decomposition rate. The side-effect can be measured by using Daphnia, oyster larvae and rainbow trout. Oxygen transfer could be a problem in the on-site treatment. For these, hydrogen peroxide can be used for solvents such as benzenes. Oleophilic nitrogen and phosphorus can be added for the treatment of oil pollution. Mixed microbial population or pure culture can be used for the inoculum. The pure culture used is Pseudomonas and Phanerochate. Sometimes enzymes are added and Photodegadation is coupled to increase the efficiency. For the treatment of oil pollution residue on soil such as waste lubrication oil and machine oil sludges, top soil of 15cm∼20cm depth is plowed and oil residue with approximately 5% concentration is applied. The optimum pH range is 7∼8, the ratio of phosphorus to hydrocarbon is 1:800. Appropriate drainage is necessary. For the treatment of marine oil pollution residue, addition of oleophilic fertilizer is effective. Air pollutiant such as oder can be treated by bioremediation. In this case, biofilters or biosrubbers are used for the reactor.

• 한국지구물리탐사학회:학술대회논문집
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• 2001.09a
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• pp.94-103
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• 2001

An experiment was conducted using a sand and gravel-filled tank model, to investigate the influence on the GPR response of vadose zone gasoline vapor phase effects and residual gasoline distributed by a fluctuating water table. After background GPR measurements were made with only water in the tank, gasoline was injected into the bottom of the model tank to simulate a subsurface discharge from a leaking pipe or tank. Results from the experiment show the sensitivity of GPR to the changes in the moisture content and its effectiveness for monitoring minor fluctuation of the water table. The results also demonstrate a potential of GPR for detecting possible vapor phase effects of volatile hydrocarbons in the vadose zone as a function of time, and for detecting the effects of residual phase of hydrocarbons in the water saturated system. In addition, the results provide the basis for a strategy that has the potential to successfully detect and delineate LNAPL contamination at field sites where zones of residual LNAPL in the water saturated system are present in the subsurface.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.06a
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• pp.267-269
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• 2009

This study investigated & analyzed the scheme for Ship Management for the Activation of world-wide Oil business. Shipping safety & marine pollution accidents occurred a huge property loss not only a shipping companies but also a oil companies, and marine accident may affect to a shipping company to go out of existence. On that score, oil companies have executed the evaluation by periods on each shipping companies and ships, and SIRE is inter-sharing by internet homepage on OCIMF & CDI, and its most important data when oil cargo transport. Therefore this study is provided a practical method to PIC of tanker shipping business & Designated Person, and it will be a basic of Ship Management for the activation of the Oil business.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.02a
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• pp.3-9
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• 2004

• 한국석유지질학회:학술대회논문집
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• 2005.09a
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• pp.48-53
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• 2005

• Proceedings of the Zoological Society Korea Conference
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• 1996.10a
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• pp.98.2-98
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• 1996