• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.815-825
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• 2017

Visibility of lane marking is impaired at night or in the rain, which thereby threatens traffic safety. Recently, various studies and technologies have been developed to improve lane marking visibility, such as the extension of lane marking life expectancy (up to 1.5 times), improvement of lane marking equipment productivity, improvement of lane marking visibility by applying phosphorescent material mixed paint. Cost-benefit analysis was performed with considering various benefit items that can be expected. About 45% of traffic accidents would be prevented by improving lane marking visibility. Additionally, accident reduction benefit and traffic congestion reduction benefit were calculated as much as 246 billion KRW per year and 12 billion KRW per year, respectively, by reducing repaint cycle due to enhanced durability. 45 billion KRW per year is expected to reduced with improved lane detection performance of autonomous vehicle. Meanwhile, total increased cost when introducing phosphorescent material mixed paint to 91,195km of nationwide road is identified as 1922 billion KRW per year. However, economic feasibility could not be secured with 0.16 of cost-benefit ratio when applied to the road network as a whole. In case of "Accident Hot Spot" analyzing section window (400m), one or more fatality or two or more injured (one or more injured in case of less than 2 lanes per direction) per year were caused by pavement marking related accident, economic feasibility was secured. In detail, 3.91 of cost-benefit ratio is estimated with comparison of the installation cost for 5,697 of accident hot spot and accident reduction benefit. Some limitations and future research agenda have also been discussed.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.10
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• pp.719-723
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• 2014

For effective implementation of total maximum daily load (TMDL), this study presented the improving plans of non-point source pollution management including the classification of non-point source pollution, calculation of non-point source pollution load (generated, discharged), selection of non-point source pollution management regions and management of non-point source pollutant. First of all, the definition of point source pollution and non-point source pollution based on the legal and scientific viewpoint should be precisely classified and managed. Especially, the forest, grassland and river without occurrence of environmental damage by activity of business and human should be separately classified natural background pollutants. The unit for generated and discharged non-point source pollution should be preferentially changed according to actual condition of watershed. The calculation methods of generated and discharged non-point source pollution should be corrected consideration on the amount and duration of rainfall. While the TMDL is implemented, non-point source pollution management regions should be selected in the watersheds exceed the targeted water quality standards by the rainfall. The non-point source pollution management regions should be selected in the minimal regions where have high values of discharged non-point source pollution density in the urban area, farmland and site area except forest, grassland in the whole watershed. The non-point source pollutant treatment facilities, which take into consideration non-point source pollution load per unit area, duration of the excess concentration, realizable possibility of treatment, effectiveness of treatment cost versus point source pollutant, should be established in the regions with a large generated non-point source pollution load and a high concentration of water quality exceed the targeted water quality standards by the rainfall.

• Korean Journal of Ecology and Environment
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• v.39 no.2 s.116
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• pp.187-197
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• 2006

A mathematical modeling program called Hydrological Simulation Program-FORTRAN (HSPF) developed by the United States Environmental Protection Agency(EPA) was applied to the Yongdam Watershed to examine its applicability for loading estimates in watershed scale. It was run under BASINS (Better Assessment Science for Integrating point and Nonpoint Sources) program, and the model was validated using monitoring data of 2002 ${\sim}$ 2003. The model efficiency of runoff was high in comparison between simulated and observed data, while it was relatively low in the water quality parameters. But its reliability and performance were within the expectation considering complexity of the watershed and pollutant sources and land uses intermixed in the watershed. The estimated pollutant load from Yongdam watershed for BOD, T-N and T-P was 1,290,804 kg $yr{-1}$, 3,753,750 kg $yr{-1}$ and 77,404 kg $yr{-1}$,respectively. Non-point source (NPS) contribution was high showing BOD 57.2%, T-N 92.0% and T-P 60.2% of the total annual loading in the study area. The NPS loading during the monsoon rainy season (June to September) was about 55 ${\sim}$ 72% of total NPS loading, and runoff volume was also in a similar rate (69%). However, water quality was not necessarily high during the rainy season, and showed a decreasing trend with increasing water flow. Overall, the BASINS/HSPF was applied to the Yongdam watershed successfully without difficulty, and it was found that the model could be used conveniently to assess watershed characteristics and to estimate pollutant loading in watershed scale.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.904-910
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• 2010

LCA (Life Cycle Assessment) carried out to estimate carbon footprint and to establish of LCI (Life Cycle Inventory) database of pepper production system. Pepper production system was categorized the field cropping (redpepper) and the greenhouse cropping (greenpepper) according to pepper cropping type. The results of collecting data for establishing LCI D/B showed that input of fertilizer for redpepper production was more than that for greenpepper production system. The value of fertilizer input was 2.55E+00 kg $kg^{-1}$ redpepper and 7.74E-01 kg $kg^{-1}$ greenpepper. Amount of pesticide input were 5.38E-03 kg $kg^{-1}$ redpepper and 2.98E-04 kg $kg^{-1}$ greenpepper. The value of field direct emission ($CO_2$, $CH_4$, $N_2O$) were 5.84E-01 kg $kg^{-1}$ redpepper and 2.81E+00 greenpepper, respectively. The result of LCI analysis focussed on the greenhouse gas (GHG), it was observed that the values of carbon footprint were 4.13E+00 kg $CO_2$-eq. $kg^{-1}$ for redpepper and 4.70E+00 kg $CO_2$-eq. $kg^{-1}$ for greenpepper; especially for 90% and 6% of $CO_2$ emission from fertilizer and pepper production, respectively. $N_2O$ was emitted from the process of N fertilizer production (76%) and pepper production (23%). The emission value of $CO_2$ from greenhouse production was more higher than it of field production system. The result of LCIA (Life Cycle Impact Assessment) was showed that characterization of values of GWP (Global Warming Potential) were 4.13E+00 kg $CO_2$-eq. $kg^{-1}$ for field production system and 4.70E+00 kg $CO_2$-eq. $kg^{-1}$ for greenhouse production system. It was observed that the process of fertilizer production might be contributed to approximately 52% for redpepper production system and 48% for greenpepper production system of GWP.

• Journal of Life Science
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• v.17 no.5 s.85
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• pp.634-640
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• 2007

Our previous study suggested that n-3 fatty acid deficiency was associated with significantly reduced spatial learning as assessed by Morris water maze test. Here we investigated an effect of n-3 fatty acid deficiency on rat brain, retina and serum fatty acyl compositions at 15 wks age using a first generational artificial rearing technique. Newborn Rat pups were separated on day 2 and assigned to two artificial rearing groups or a dam-reared control group. Pups were hand fed artificial milk via custom-designed nursing bottles containing either 0.02%(n-3 Deficient) or 3.1% (n-3 Adequate) of total fatty acids as a-linolenic acid(LNA). At day 21, rats were weaned to either n-3 deficient or n-3 adequate pelleted diets and fatty acid compositions of brain, retina and liver were analyzed at 15 wks age. Brain docosahexaenoic acid(DHA) was lower(58% and 61%, P<0.05) in n-3 deficient in comparison to n-3 adequate and dam-reared groups, receptively, while brain docosapentaenoic acid(DPAn-6) was increased in the n-3 deficient group. In retina and serum fatty acid compositions, the decreased precentage of DHA and increased precentage of DPAn-6 were observed. These results suggested that artificial rearing method can be used to produce n-3 fatty acid deficiency in the first generation and that adequate brain DHA levels are required for optimal brain function.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.898-903
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• 2010

This study was carried out to estimate carbon emission using LCA (Life Cycle Assessment) and to establish LCI (Life Cycle Inventory) database of soybean production system. Based on collecting the data for operating LCI, it was shown that input of organic fertilizer was value of 3.10E+00 kg $kg^{-1}$ soybean and it of mineral fertilizer was 4.57E-01 kg $kg^{-1}$ soybean for soybean cultivation. It was the highest value among input for soybean production. And direct field emission was 1.48E-01 kg $kg^{-1}$ soybean during soybean cropping. The result of LCI analysis focussed on greenhouse gas (GHG) was showed that carbon footprint was 3.36E+00 kg $CO_2$-eq $kg^{-1}$ soybean. Especially $CO_2$ for 71% of the GHG emission. Also of the GHG emission $CH_4$, and $N_2O$ were estimated to be 18% and 11%, respectively. It might be due to emit from mainly fertilizer production (92%) and soybean cultivation (7%) for soybean production system. $N_2O$ was emitted from soybean cropping for 67% of the GHG emission. In $CO_2$-eq. value, $CO_2$ and $N_2O$ were 2.36E+00 kg $CO_2$-eq. $kg^{-1}$ soybean and 3.50E-01 kg $CO_2$-eq. $kg^{-1}$ soybean, respectively. With LCIA (Life Cycle Impact Assessment) for soybean production system, it was observed that the process of fertilizer production might be contributed to approximately 90% of GWP (global warming potential). Characterization value of GWP was 3.36E+00 kg $CO_2$-eq $kg^{-1}$.