• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.630-640
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• 2013

It is one of the most critical steps identifying impaired waterbodies exactly in the selection of target water quality indicators for the management of Total Maximum Daily Loads (TMDLs). Excess ratio and excess level were applied and analyzed by the stream zone basis in order to evaluate water impairment for Nakdong, Geum, Youngsan and Seomjin rivers. Each river basin was divided into stream zones in the light of its watershed and waterbody characteristics. Selected water quality parameters discussed in this study were pH, DO, BOD, COD, SS, T-P, T-Coli and F-Coli. The excess ratios of the water quality parameters were used to discriminate water bodies that did not meet water quality standards. The excess levels were used to classify the degradation of water quality. The excess ratios and the excess levels to the water quality criteria of the medium influence areas were used for each stream zone. The results indicate that the excess ratios and the excess levels are varied on the stream zone in each river basin. Three parameters, pH, DO and SS, met water quality standards in all stream zones. The other five parameters indicated very high excess ratios in most waterbodies, and especially T-P and T-Coli revealed to be very high excess levels in some waterbodies. These parameters could be considered as major target indicators for the management of TMDLs.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.5
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• pp.103-113
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• 2000

A decision support system DSS-WQMRA (Decision Support System-Water Quality Management in Rural Area) was developed to help regional planners for the water quality management in a rural basin. The integrated model DSS-WQMRA, written in JAVA, includes four subsystems such as a GIS, a database, water quality simulation models and a decision model. In the system, the GIS deals with landuse and the location of pollutant sources. The database manages each data and supplies input data for various water quality simulation models. the water quality simulation model is composed of the GWLF( Generalized Watershed Loading Function), PCLM(Pollutant Loading Calculation Module) and the WASP5 model. The decision model based on mixed integer programming is designed to determine optimal costs and thus allow the selection of managemental practices to meet the water quality criteria. The methodology was tested with an example application in the Bokha River Basin, Kyunggi Province in Korea. It was proved that the integrated model DSS-WQMRA could be very useful for water quality management including the non-point source pollution in rural areas.

• Journal of environmental and Sanitary engineering
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• v.17 no.4
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• pp.1-9
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• 2002

In order to define about concern with discharge and water-quality, it is calculated drought flow, low flow, normal flow and wet flow in Chungju watershed from flow duration analysis. Water quality modeling study is performed for forecasting at upstream of Chungju lake. It is devided method of modeling into before and after the equipment of environmental treatment institution. And it is estimated the change of water quality. Before the equipment of environmental treatment, BOD concentration is increased from 23000 to 2006 years at all site and decrease on 2012 years. The rate of increasing BOD concentration is showed height between 2000 years and 2003 years most of all site. And after the equipment of environmental treatment, it is showed first grade of BOD water quality in most of sample site beside Jucheon river. The result of water quality modeling using drought flow showed that a lot of pollution occurred. And water quality using wet flow is good, so much discharge make more improve water quality than little discharge.

• Advances in environmental research
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• v.6 no.1
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• pp.53-65
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• 2017

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 15 physico-chemical parameters collected from 7 monitoring stations in a river during the years from 2014 to 2016 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except alkalinity, which is always the most important parameters in contributing to water quality variations for all three seasons.

• Journal of Environmental Science International
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• v.19 no.1
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• pp.39-45
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• 2010

When the Juam multipurpose dam which is connected with existing large water supply facilities is finished, water environment is changed from stream to lake. The changed quality of water should be examined. In this study, the result of water quality forecasting is analysed and an effective management plan of water quality is presented. Tn this study, the WASPS model that is a dynamic water quality simulation model was selected to forecast the water quality. This model forecasts movement of change of pollutants. For an application of the model, the subject areas were divided into seventeen sub-areas by considering change temperature depending measuring points and on depth of water. Meteorological data collected by the meteorological observatory and data about quality measured by the Korea Water Resources Development Corporation were used for an operation of the model. As a result of quality examination through quality data and estimated pollutant loading, the water quality environment criterion was grade II and the nutritive condition was measured as meso-graphic grade. In this study, an effective management was planned to improve water quality by reducing pollution load. According to the result of examination, when more than 30% of BOD was reduced it was recorded that the environment standard of water quality was improved to the second grade.

• Journal of Environmental Impact Assessment
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• v.30 no.2
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• pp.75-88
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• 2021

Six locations in the automated monitoring network at the Geum River Basin were selected forthis study. The water quality characteristics at two of the locations in the water quality monitoring network that were identical, or nearby, were examined, and their correlations were evaluated through statistical analysis. The results of the water quality analysis were converted to the water quality index and expressed in grades for comparison. For the data necessary for the study, public data from four years, from 2016-2019 were used and the evaluation parameters were water temperature, pH, EC, DO, TOC, TN, and TP. Results of the analysis showed that the water quality concentrations measured in the automated monitoring network and the water quality monitoring network differed in some measured values, but they tended to register variation in a specified ratio in most of the locations in the network. The analysis of the correlations of the parameters between the two monitoring networks found that water temperature, EC, and DO showed high correlations between the two monitoring networks. The TOC, TN, and TP showed high correlations, with a 0.7 or higher (correlation coefficient r), with the exception of some of the monitoring networks, although their correlations were lower than those of the basic parameters. The water quality index analysis showed that the water quality index values of the automated monitoring network and the water quality monitoring network were similar. The water quality index decreased and the pollution degree increased in the downstream direction, in both networks.

• Journal of Environmental Impact Assessment
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• v.3 no.2
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• pp.33-45
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• 1994

Water quality of Chongju and Daejeon Water Intake Tower Region, embayments in Daechong Reservoir was found to be worse than that of main lake after analysis of water which were sampled during April, July, October in 1993. Concentration of COD and SS at those two water intake tower sites were 2.8-5.6 mg/l and 2.2-3.2 mg/l, higher than that of main lake. T-N concentration of those two sites was 1.1-1.9 mg/l similar to that of main lake, and T-P concentration of those two sites was 0.14-0.18 mg/l, higher than that of main lake. This study used water quality model of embayment which can analyse pollutant loads from stream and surrounding land use, advection, decay, and diffusion transport between embayment and main lake. The model can predict water quality of embayment according to the change of pollutant load, water elevation of embayment, quantity of water intake in order to suggest water quality management. This study suggests embayment water quality management alternatives, 1) construction of waste water treatment facilities at embayment and main lake for the decrease of pollutant loading, 2) water intake at main lake less polluted or eutrophicated than embayment, and 3) outflow elevation selection for polluted hypolimnion water outflow during stratification.

• Journal of Korean Society on Water Environment
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• v.33 no.5
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• pp.525-530
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• 2017

The attainment ratios of the water quality goals of the 114 mid-watershed representative stations, examined during the period2011 to 2015, were evaluated in the study. Of the four major river basins, the attainment ratio on water quality goal of the Geum River basin turned out to be the lowest. As a result of formal evaluation of the attainment ratios of BOD, COD and TOC, it was found that the attainment ratio of COD was much lower than that of BOD and TOC (I a circumstance thought to be caused by the higher COD/BOD and COD/TOC ratios of the water quality of the river than those of the environmental water quality standard). As well, higher COD/BOD and COD/TOC of wastewater discharged from point and non-point sources (other than those of the environmental water quality standards) might possibly represent one of the reasons. We also compared attainment ratio between the main stream and tributaries, which indicated that the higher attainment ratio was present in the main stream. The attainment ratio is also documented as more significant in the winter season than the summer season, possibly due to the contribution of non-point pollutants swept in by rain during the summer season during documented periods of high precipitation. Thus, water quality management in summer season and improvement of water quality of the tributaries might be important as a means of increasing attainment ratio on water quality goal.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.3
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• pp.81-90
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• 1998

The purpose of this study was to analyze the effect of the groin which was constructed in the down stream of urban river for the prevention of sedimentation on the improvement of the water quality. We studied on the groin in the downstream of Taehwa River constructed to keep the navigation depth of Ulsan day, and used the measured data of the water quality and river discharge. In order to analyze the charge of the water quality due to the groin Que12E model was applied, and BOD and DO was examined. The analysis showed that removal of the groin would not have significant effect on the improvement of the water quality, rather, interception of the pollution source coming into the upstream of the groin would be more influential on the improvement of the water quality. Therefore, It was concluded that to improve the water quality in mild bottom slop of tidal river, interception of the pollution source and river maintenance discharge should precede.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.3
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model