• Journal of Korean Society of Water and Wastewater
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• v.27 no.6
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• pp.761-770
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• 2013

This study was carried out to forecast the flow rate and water quality at the inlet of the Saemangeum bay in Korea using the SWMM(Storm Water Management Model) and the WASP(Water Analysis Simulation Program), and to analyze the impacts of pollutant loading from non-point source on the water quality of the bay. The calibration and validation of flow rate and water quality were performed using those from two monitoring points in the Mankyeong river administrated by Korean Ministry of Environment as part of the national water quality monitoring network. When the river flow rate was calibrated and validated using the rainfall intensities during 2011-2012, $R^2$ (i.e., coefficient of determination) was ranged from 0.91 to 0.96. For water qualities, it was shown that $R^2$ of BOD(Biochemical Oxygen Demand) was ranged from 0.56 to 0.86, and $R^2$ of T-N(Total Nitrogen) was from 0.64 to 0.75, and $R^2$ of T-P(Total Phosphorus) was from 0.67 to 0.89. The integrated modeling system showed significant advances in the accuracy to estimate the water quality. Finally, further simulations showed that annual average flow of the river running into the bay was estimated to be $1.439{\times}10^9m^3/year$. The discharged load of BOD, T-N, and T-P into the bay were anticipated to be 618.7 ton/year, 331.5 ton/year, and 40.4 ton/year, respectively.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.2
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• pp.101-108
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• 2013

Ara Canal is the first artificial canal in Korea that connects the Han River and the Yellow Sea. Due to mixture of waters with different salinity and water quality, complicated hydrodynamic and water quality distributions are expected to occur inside the canal. An integrated hydrodynamic and water quality modeling system was developed using the 3 dimensional hydrodynamic model, EFDC (Environmental Fluid Dynamics Code) and the water quality model WASP (Water Quality Analysis and Simulation Program). According to the modeling results, BOD, TN, TP and Chl-a concentrations inside the canal were lower at the West Gate side than the Han River side since influent concentrations of the West Gate side are significantly lower. Chemical stratification due to salinity difference were more evident at the West Gate side as vertical salinity difference were more pronounced in this area. On the other hand, Chl-a concentrations showed more pronounced vertical distribution at the Han River side as Chl-a concentrations were higher in this area. It was notable that Dissolved Oxygen concentrations can be lower than 2 mg/L occasionally in the middle part of the canal. While major factor affecting DO concentrations in the canal are inflows via both gates, the other important factor was found to be BOD decay in the canal due to extended hydraulic residence time. This study can be used to predict hydrodynamic conditions and water quality in the canal during the year and thus can be helpful in the development of gate operation method of the canal.

• Journal of Environmental Health Sciences
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• v.41 no.2
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• pp.90-101
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• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1053-1060
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• 2021

Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.236-241
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• 2023

Water quality problems in rivers, lakes, and estuaries have become serious in Korea. In order to overcome eutrophication of freshwater lakes and river basins, systematic management of water quality is necessary. To manage water quality in freshwater lakes and basins, apply hydrological models suitable for the basin and water quality models such as rivers and lakes to reduce water pollution based on the prediction results of these models. Improvement measures must be presented. In order to apply appropriate water pollution improvement measures in the watershed, accurate pollution sources must be identified and pollution loads must be predicted and presented. Based on GIS, the connection between the pollutant database and the hydrological and water quality prediction model will be integrated based on spatial location, making it possible to provide systematic support to improve watershed water quality by comprehensively including the water quality modeling process. In this paper, in order to accurately predict water pollution in freshwater lakes and river basins, a water quality model system is established using GIS-based spatial information to present a comprehensive water quality management method for freshwater lake basins in the future, and to systematically manage pollution sources through water quality modeling. This study was conducted to easily and efficiently operate hydrological and water quality models using automated spatial information.

• Journal of Korean Society on Water Environment
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• v.31 no.5
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• pp.569-575
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• 2015

The current Total Pollution Load Control (TPLC) sets the Target Water Quality (TWQ) by utilizing the delivery ratio, unit loads, and water quality modeling, it also allocates the watershed's permitted discharge load. Currently, common target pollutants of every unit watershed in TPLC are BOD and T-P. This study has reviewed the 1th and 2th of TWQ setting process for the Nakdong River 3th TWQ setting in Total Pollution Load Control (TPLC). As a result of review, 1th and 2th were divided into one management basin (mulgeum) for setting management goals. However, 3th was divided into six management basins (mulgeum, gnagjeong, geumho river, nam river, miryang river, end of nakdong river). The principle of management goal setting were to achieve the objective criteria of Medium Areas for the linkage of the water environment management policy. And Anti-Degredation (principle of preventing deterioration) were applied to the 3th TWQ. Also, additional indicators were considered in accordance with the reduction scenarios for the final management goals.

• Journal of Korea Water Resources Association
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• v.45 no.3
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• pp.331-347
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• 2012

The downstream of the Nam River Dam is crucial region for long-term water resource planning for Busan and Gyeongnam Province. Thus, the analysis of flow behavior and water quality is necessary for the sustainable surface water management and the control of pollutant source. In this study, the flow field and BOD transport at the downstream of Nam River Dam were analyzed by incorporating 2-D water quality model, RAM4 and 3-D water quality model, WASP with the hydrodynamic model, RAM2 and EFDC, respectively. The application of 2-D flow analysis model, RAM2 showed that velocity distributions at the five transverse sections of the meandering part closely followed the measured values by ADCP, and the flow field and overflow characteristic at the submerged weir showed satisfactory performance compared with the result of 3-D EFDC model. In addition, the BOD concentration field obtained by RAM2-RAM4 coupled modeling was in good agreement with the result by EFDC-WASP model throughout the computational domain. The hydrodynamic characteristic and water quality at the downstream reach of Nam River Dam are mainly influenced by the Dam discharge, and the water quantity is closely related to the water quality control and fishery environment at the lower part of Nakdong River. Therefore, when further quantitative analysis is necessary regarding these issues, 2-D semi-coupled modeling is recommended in terms of computational effectiveness and model application aspect.

• Journal of Korean Society on Water Environment
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• v.28 no.2
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• pp.320-331
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• 2012

A combination system of catch canal and constructed wetland was designed and suggested to improve water quality in gagricultural region of lower Dong-jin river basin. In order to evaluate an water quality improvement efficiency of the designed combination system, the NPS-WET model was applied in this study. Simulation result of the NPS-WET shown that the nutrient load removal rate of constructed wetland was BOD, T-N, T-P and SS was 30.7~39.0%, 46~60%, 40.7~57.0% and 68.2~74.7%, respectively. Nutrients reduction of constructed wetland was higher in growing season than winter season because vital activity of microorganism, macrophyte and algae was augmented with high air and water temperature. Effluents from constructed wetland can affect water-quality of catch canal drains, especially, water-quality on junction point to Dong-jin river. Water-quality improvement in low-flowed catch canal (Un-san) was more significant than in high-flowed catch canal (Won-pyeong). In conclusion, a feasible design of constructed wetland is necessary to treat large quantity of receiving water. The NPS-WET is useful tool for assessing water-quality improvement efficiency using constructed wetland.

• Journal of Environmental Impact Assessment
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• v.18 no.1
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• pp.1-9
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• 2009

Even though the center of Cheongju city needs redevelopment because of a doughnut phenomenon, it has to be permitted within the environmental carrying capacity like a target water quality proposed on the Total maximum daily loads(TMDL) of Musim and Miho river watersheds. The aim of in this study is to identify the limit of redeveloping Cheongju downtown after analyzing its environmental carrying capacity using QUAL2E model. As a result of modeling various scenarios, the water quality of Musin river was shown that $BOD_5$ is 2.3mg/L which is the target water quality in the double of existing development plan of the Cheongju downtown. The water quality of Miho river was $BOD_5$ 3.97mg/L which is less than the target water quality of Miho B watershed in the same condition. Therefore, this means that the limit of redevelopment within the environmental carrying capacity of cheongju downtown was estimated to be the double of existing development plan.

• Journal of Industrial Technology
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• v.30 no.B
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• pp.125-135
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• 2010

The influences on water quality of each river by effluents from environmental facilities $located^{*}$ in 14 unit watersheds of North- and South-Han River, and Gyungan-cheon were analyzed. Also, the water quality modeling for study area was carried out to analyze the improvement effect of water quality by the strengthening of T-P effluent standard of environmental facilities. For the calibration and verification of model, water quality data and effluent loading calculated for 2006 were used. Data of low water period were used for calibration, and normal water period for verification. The results of calibration and verification were well matched with the real water quality dataset of revers. Also, the validity of the results were estimated using RI (Reliability Index) method. When the T-P effluent standards for environmental facilities were strengthened, T-P concentrations were predicted to improve from $0.025mg/{\ell}$ to $0.023mg/{\ell}$ in the outlet location of North-Han River, from $0.056mg/{\ell}$ to $0.040mg/{\ell}$ for South-Han River,and from $0.233mg/{\ell}$ to $0.146mg/{\ell}$ for Gyungan-cheon. Also, the T-P concentrations of tributaries including Jojong-cheon, Dal-cheong, Sumgang, Chungmi-cheon, Bokha-cheon, Heuk-cheon, and Wonju-cheon were predicted to improve from $0.063mg/{\ell}$ to $0.010mg/{\ell}$, from $0.091mg/{\ell}$ to $0.053mg/{\ell}$, from $0.199mg/{\ell}$ to $0.100mg/{\ell}$, from $0.168mg/{\ell}$ to $0.148mg/{\ell}$, from $0.186mg/{\ell}$ to $0.105mg/{\ell}$, from $0.019mg/{\ell}$ to $0.013mg/{\ell}$, and from $0.822mg/{\ell}$ to $0.236mg/{\ell}$, respectively.