• 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.22 no.3
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• pp.535-546
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• 2000

Juam lake is a major water resource for the industrial and agricultural activities as well as the resident life of Kwangju and Chonnam area. However, the water quality of the lake is getting worse due to a large quantity of pollutant inflowing to the lake. As a preliminary step in making the countermeasure to achieve the water quality goal of the lake. it is necessary to understand how the water quality of the lake will be in future. Several computer programs can be used to predict the water quality of lake. Each of these programs requires a number of input data such as hydrological and meteorological data. and the quantity of the pollutant inflowed. but some or most of the input data contain uncertainty. which eventually results in the uncertainty of prediction value (future level of water quality). Generally. the uncetainty stems from the lack of information available. the randomness of future situation. and the incomplete knowledge of expert. Thus. the purpose of this study is to present a method for representing the degree of the uncertainty contained in input data by applying fuzzy theory and incorporating it directly into the water quality modeling process. By using the method. the prediction on the future water quality level of Juam lake can be made that is more appropriate and realistic than the one made without taking uncertainty in account.

• Journal of Wetlands Research
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• v.17 no.4
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• pp.348-358
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• 2015

Impacts of climate change are being observed in the globe as well as the Korean peninsula. In the past 100 years, the average temperature of the earth rose about 0.75 degree in celsius, while that of Korean peninsula rose about 1.5 degree in celsius. The fifth Assessment Report of IPCC(Intergovermental Panel on Climate Change) predicts that the water pollution will be aggravated by change of hydrologic extremes such as floods and droughts and increase of water temperature (KMA and MOLIT, 2009). In this study, future runoff was calculated by applying climate change scenario to analyze the future water quality for each targe period (Obs : 2001 ~ 2010, Target I : 2011 ~ 2040, Target II : 2041 ~ 2070, Target III : 2071 ~ 2100) in Hongcheon river basin, Korea. In addition, The future water quality was analyzed by using multiple linear regression analysis and artificial neural networks after flow-duration curve analysis. As the results of future water quality prediction in Hongcheon river basin, we have known that BOD, COD and SS will be increased at the end of 21 century. Therefore, we need consider long-term water and water quality management planning and monitoring for the improvement of water quality in the future. For the prediction of more reliable future water quality, we may need consider various social factors with climate components.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.495-507
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• 2021

With the recent industrial development, accidental pollution in riverine environments has frequently occurred. It is thus necessary to simulate pollutant transport and dispersion using water quality models for predicting pollutant residence times. In this study, we conducted a field experiment in a meandering reach of the Sum River, South Korea, to validate the field applicability and prediction accuracy of RAMS+ (River Analysis and Modeling System+), which is a two-dimensional (2D) stream flow/water quality analysis program. As a result of the simulation, the flow analysis model HDM-2Di and the water quality analysis model CTM-2D-TX accurately simulated the 2D flow characteristics, and transport and mixing behaviors of the pollutant tracer, respectively. In particular, CTM-2D-TX adequately reproduced the elongation of the pollutant cloud, caused by the storage effect associated with local low-velocity zones. Furthermore, the transport model effectively simulated the secondary flow-driven lateral mixing at the meander bend via 2D dispersion coefficients. We calculated the residence time for the critical concentration, and it was elucidated that the calculated residence times are spatially heterogeneous, even in the channel-width direction. The findings of this study suggest that the 2D water quality model could be the accidental pollution analysis tool more efficient and accurate than one-dimensional models, which cannot produce the 2D information such as the 2D residence time distribution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.145-151
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• 2017

In this paper, we propose the design and implementation of the system that is predicting the failure of ballast water treatment system by analysing its sensor datum and is reserving the most effective service center for sellecting the repair location on the way to the destination port. These data are collected in real time during draining or filling up the sea water from/to the ship, and it is essential to preliminarily repair the equipment showing unstable characteristics by analyzing the normal and abnormal data characteristics. We proposed a software platform for predicting and repairing faults by selecting the most efficient repair center based on this e-Navigation while the vessel is navigating to the next destination port. This system, as announced by the IMO Convention for the Prevention of Marine Pollution in 2017, provides a stable economic impact from stable cargo operation and stable out/in from/to port and marine ecosystem.

• Journal of Environmental Impact Assessment
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• v.9 no.1
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• pp.25-37
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• 2000

This paper presents a study of hydrological and hydraulic model applications in environmental impact statements which were submitted during recent years in Korea. In many cases (almost 70 %), the hydrological and hydraulic changes were neglected from the impact identification processes, even if the proposed actions would cause significant impacts on those environmental items. In most cases where the hydrological and hydraulic impacts were predicted, simple equations were used as an impact prediction tool. Computer models were used in very few cases(5%). Even in these few cases, models were improperly applied and thus the predicted impacts would not be reliable. The improper applications and the impact neglections are attributed to the fact that there are no available model application guidelines as well as no requirements by the review agency. The effects of mitigation measures were not analyzed in most cases. Again, these can be attributed to no formal guidelines available for impact predictions until now. A brief guideline is presented in this paper. This study suggested that the model application should be required and guided in detail by the review agency. It is also suggested that the hydrological and hydraulic items shoud be integrated with the water quality predictions in future, since the non-point source pollution runoff is based on the hydrologic phenomena and the water quality reactions on the hydraulic nature.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.4
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• pp.159-171
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• 1990

The purpose of this study was development and application of a multiple box model for long term prediction and control of water quality in estuary reservoir. The model was composed of one main model and two sub models for hydrology and water quality. Water quality constituents for modeling were temperature, chlorophyll-a, BOD, DO, N, and P. The model had been applied to Asan reservoir, and the reservoir had been devided into three boxes-two boxes for a upper layer and one box for a lower layer-to represent stratification. The model appeared satisfactory in representing long term trend of water quality variations by comparing measured and simulated results. According to the application of the model to study alternatives for water quality improvement to deal with increase in future pollution load, use of non-phosphorous detergent for chlorophyll-a, advanced sewage treatment for BOD and T-N, reduction of livestock waste for T-P were evaluated as more effective ones than any others, respectively.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.11
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• pp.1228-1237
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• 2005

In order to predict the long-term effects of pollutants in sediment on the water quality and the improvement of water quality according to dredging operation, models applied to decide the location and the propriety of dredging were developed. At first, the area was divided into several segments and the developed model was applied to simulate the behaviors of contaminants in an aquatic environment by using estimated parameters. And then through the sensitivity analysis between parameters, the optimum values were determined. The long-term modelling in the area A forecasted that PCBs concentration in the hot spot was decreased from $3.1\;{\mu}g/L$ to $2.4\;{\mu}g/L$ in 30 years. Contaminants in sediment as a source of water pollution did not reduce remarkably in the long run. Therefore it is difficult to expect the improvement of water qualities without the fundamental isolation of contaminants from sediment. It is considered that the selective dredging in the spot improves the water quality consequently.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.2
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• pp.104-110
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• 1997

Increase of marine transpotation in coastal area frequently yields oil spill accidents due to collision or grounding of oil tankers, which affects great deal of damages on ocean environments. Exact prediction of oil pollution area in time domain, which is called oil map, is very important for effective and efficient oil recovery and minimization of environmental damage. The prediction is carried out by considering the two distinct processes which are initial diffusion on the still water surface and advection due to tide, wind wave induced surface currents. In the present paper, only the initial diffusion is dealt with. Somewhat new simulation model and its numerical scheme are proposed to predict it. Simple diffusion experiment is also carried out to check the validity of the present method. Furthermore, some example simulations are performed for virtual oil spill accident. Quite realistic oil map including oil thickness distributions can be obtained by the present model.

• Journal of Korean Society on Water Environment
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• v.39 no.2
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• pp.162-174
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• 2023

In order to prevent water pollution caused by organic matter, Total Organic Carbon(TOC) has been adopted indicator and monitored. TOC can be divided into Dissolved Organic Carbon(DOC) and Particulate Organic Carbon(POC). POC is largely precipitated and removed during stream flow, which making DOC environmentally significant. However, there are lack of studies to define spatio-temporal distributions of DOC in stream affected by various land use. Therefore, it is necessary to estimate the past DOC concentration using other water quality indicators to evaluate status of watershed management. In this study, DOC was estimated by correlation and regression analysis using three different organic matter indicators monitored in mixed land-use watersheds. The results of correlation analysis showed that DOC has the highest correlation with TOC. Based on the results of the correlation analysis, the single- and multiple-regression models were developed using Biochemical Oxygen Demand(BOD), Chemical Oxygen Demand(COD), and TOC. The results of the prediction accuracy for three different regression models showed that the single-regression model with TOC was better than those of the other multiple-regression models. The trend analysis using extended average concentration DOC data shows that DOC tends to decrease reflecting watershed management. This study could contribute to assessment and management of organic water pollution in mixed land-use watershed by suggesting methods for assessment of unmeasured DOC concentration.