• Journal of Korean Society of Water and Wastewater
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• v.19 no.6
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• pp.728-737
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• 2005

Water quality of the Hwanggujicheon is poor because of the rapid housing and development in the large area of the basin. Establishment of water quality management strategy, based on the pollution sources survey and pollutant loads estimation, has to be established for the preservation of the stream water quality of the region. In this study, waste load allocation model to achieve the water quality goal of the stream and the optimization of pollutant load reduction, was developed. Nonpoint pollutant loads calculated by runoff model in the previous study are utilized for pollutant loads estimation of the drainage areas in this study. From the application result of the allocation model, water quality goals of the Hwanggujicheon that can be achieved as a matter of fact are BOD 8 mg/L. To achieve these goals, 23% of effluent BOD loads have to be reduced in the basin.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.3
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• pp.162-171
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• 2014

In this study, the uncertainty analysis of present land pollutant load estimation with simplified land category in TMDLs was performed and the enhanced method for land pollutant load estimation with level II land cover consisting of 23 categories was suggested, which was verified by L-THIA model. For land TP load estimation in Jinwi stream basin, the result of comparison between existing method with simplified land category (Scenario 1) and enhanced method with level II land cover (Scenario 2) showed high uncertainty in existing method. TP loads estimated by Scenario 2 for land covers included in the site land category were in the range of 3.45 to 56.69 kg/day, in which TP loads differed by sixteen times as much among them. For application of scenario 2 to TMDLs, Land TP loads were estimated by matching level II land cover to 28 land categories in serial cadastral map (Scenario 3). In order to verify accuracy of TP load estimation by scenario 3, the simulation result of L-THIA was compared with that and the difference between the two was as little as 10%. The result of this study is expected to be used as primary data for accurate estimation of land pollutant load in TMDLs.

• Korean Journal of Hydrosciences
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• v.10
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• pp.35-46
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• 1999

An integrated system of GIS and water quality model was suggested including the pollutant loads from the watershed. The developed system consits of two parts. First part is the information on landuse and several surface factors concerning the overland flow processes of water and pollutants. Second part is the modeling modules which include storm event pollutant load model(SEPLM), non-storm event pollutant load model(NSPLM), and river water quality simulation model(RWQSM). Models can calculate the pollutant load from the study area. The databases and models are linked through the interface modules resided in the overall system, which incorporate the graphical display modules and the operating scheme for the optimal use of the system. The developed system was applied to the Chungju multi-purpose reservoir to estimate the pollutant load during the four selected rainfall events between 1991 and 1993, based upon monthly basis and seasonal basis in drought flow, low flow, normal flow and wet flow.

• Korean Journal of Environmental Agriculture
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• v.33 no.1
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• pp.37-43
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• 2014

BACKGROUND: Water quality data are collected less frequently than flow data because of the cost to collect and analyze, while water quality data corresponding to flow data are required to compute pollutant loads or to calibrate other hydrology models. Regression models are applicable to interpolate water quality data corresponding to flow data. METHODS AND RESULTS: A regression model was suggested which is capable to consider flow and time variance, and the regression model coefficients were calibrated using various measured water quality data with genetic-algorithm. Both LOADEST and the regression using genetic-algorithm were evaluated by 19 water quality data sets through calibration and validation. The regression model using genetic-algorithm displayed the similar model behaviors to LOADEST. The load estimates by both LOADEST and the regression model using genetic-algorithm indicated that use of a large proportion of water quality data does not necessarily lead to the load estimates with smaller error to measured load. CONCLUSION: Regression models need to be calibrated and validated before they are used to interpolate pollutant loads, as separating water quality data into two data sets for calibration and validation.

• Journal of the Korean Institute of Landscape Architecture
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• v.29 no.1
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• pp.22-31
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• 2001

The purpose of this study is to compare calculated pollutant loadings using pollutant load unit factors and vector type coverage, and expected mean concentration(EMC) and raster type of digital elevation model(DEM). This study is also focusing on comparison of the advantages and the disadvantages of the two methods, and seeking for a method of calculation of pollutant loadings using DEM. Estimation of pollutant inputs using pollutant load unit factors has limitations in identifying seasonal variations of pollutant loadings. Seasonal changes of runoffs should be considered in the calculation of pollutant loadings from catchments into reservoirs. Evaluation of pollutant inputs using runoff-coefficient and EMC can overcome these drawbacks. Proper EMC and runoff-coefficient values for the Koeup stream catchments of the Koheung estuarine lake were drawn from review of related papers. Arc/Info was employed to establish database of spatial and attribute data of point and non-point pollutant sources and characteristics of the catchments. ArcView was used to calculate point and non-point pollutant loadings. Pollutant loads estimated with either unit factors-coverages, i.e., pollutant load unit factors and vector coverages f point sources and land use, or EMC and digital elevation mode(DEM) were compared with stream monitoring loads. We have found that some differences were shown between monitoring results and estimated loads by Unit Factors-Coverage and EMC-DEM. Monthly variations of pollutant loads evaluated with EMC-DEM were similar to those with monitoring result. The method using EMC-DEM can calculate accumulated flows and pollutant loads and can be utilized to identify stream networks. A future research on correcting the difference between vector type stream using flow direction grid and digitalizing vector type should be conducted in order to obtain more exact calculation of pollutant loadings.

• Journal of Environmental Impact Assessment
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• v.14 no.3
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• pp.97-107
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• 2005

The purpose of this study is to develop a system, which estimates watershed pollutant loading rate through the combination of GIS and computational mode. Also, the applicability of this study was estimated by the application of the above system for Chuncheon City. The detailed results of these studies are as follows; The pollutant loading estimation system was developed for more convenient estimation of pollutant loading rate in watershed, and the system load was minimized by the separation of estimation module for point and non-point source. This system on the basis of GIS is very economical and efficient because it can be applied to other watershed with the watershed map. System modification is not needed. The pollutant loading estimation system for point source was developed to estimate the pollutant loading rate in watershed through the extraction of the proper data from all districts and yearly data and the execution of spatial analysis which is main function of GIS. From the verification result of spatial analysis, real watershed area and the administrative districtarea extracted by spatial analysis were $1,114,893,340.15m^2$ and $1,114,878,683.68m^2$, respectively. It shows that the spatial analysis results were very exact with only 0.001% error. The pollutant loading estimation system for non-point source was developed to calculate the pollutant loading rate through the overlaying of land-use and watershed map after the construction of new land-use map using the land register database with most exact land use classification. Application result for Chuncheon City shows that the proposed system results in one percent land use error while the statistical method results in five percent. More exact nonpoint source pollutant loading was estimated from this system.

• Journal of Environmental Impact Assessment
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• v.12 no.5
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• pp.349-358
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• 2003

Water pollution of Hwanggujicheon stream is severe because urban area of Suwon City is included in the basin. A countermeasure for water quality prevention of the stream is necessary. In this study, nonpoint pollutant load of BOD, SS, TN and TP are estimated using SWMM. The result indicates that BOD, SS, TN and TP loads during 3 months from July to September are 67.0%, 60.8%, 54.7% and 74.5% of the annual total load, respectively. We can see that most of nonpoint pollutant loads are generated in the rainy season. Annual nonpoint pollutant loads of BOD, SS, TN and TP in the Hwanggujicheon stream are 342 ton, 1,500 ton, 480 ton and 12.6 ton, respectively.

• Journal of Korean Society on Water Environment
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• v.32 no.4
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• pp.375-383
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• 2016

Total Pollution Load Control (TPLC) calculates and manages the allowable pollutant load that is discharged from the watershed, which can meet the water quality target. Delivery Ratio (DR) is generally used for predicting the variation of pollutant mass balance between the pollutants discharged from the watershed and a certain point in the stream, and it is very important for estimation of accurate allowable pollutant load. The concept of DR in TPLC is different from prevalent DR, because DR in TPLC includes both the discharge of pollutants from the watershed and the delivery mechanism. Therefore, DR in TPLC should be estimated by using a proper and unified methodology. The appropriate method and equation for estimation of DR in TPLC was developed through the review of various methodologies, and the applicability of the equation was evaluated in a study area (Geumho A). Determination coefficients (R²) of regression were shown to be relatively high (BOD 0.71~0.87, T-N 0.86~0.90, T-P 0.62~0.69). Applicability of the developed methodology and equations was evaluated as appropriate for TPLC, and it is suggested.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.3
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• pp.15-22
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• 2019

In order to preserve water environment, Total Maximum Daily Load(TMDL) is used to manage the total amount of pollutant from various sources, and the annual average load of source is calculated by the unit load method. Determination of the unit load requires reliable data accumulation and analysis based on a reasonable estimation method. In this study, we propose a revised unit load estimation method by analyzing the unit load calculation procedure of National Institute of Environment Research(NIER) method. Both methods were tested using observed runoff ratio and water quality data of rice paddy fields. The estimated values with the respective NIER and revised NIER methods were highly correlated each other. However, the Event Mean Concentration(EMC) and the runoff ratio considered in the NIER method appeared to be influenced by rainfall classes, and the difference in unit load increases as the runoff and EMC increase. The error can be further increased when the EMC and runoff ratio are changed according to changes in rainfall patterns by climate change and change of agricultural activities. Therefore, it is recommended to calculate unit load by applying the revised NIER method reflecting the non point pollution runoff characteristics for different rainfall classes.

• Journal of Wetlands Research
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• v.6 no.3
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• pp.47-54
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• 2004

A hydraulically and hydrologically based estimation method of pollutant delivery load for water quality modeling is proposed. The proposed method works on grid basis and routes overland flows from one cell to the next following the maximum downslope directions. The method is able to consider spatially-varied data of source pollutant, topography, land slopes, soil characteristics, land use and aspects, which can be extracted from geographic information systems (GIS) and from digital elevation models (DEMs). Because of this feature, the proposed method can be expected to be used for evaluating the impacts of various practices on watershed management for water quality.