• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.1184-1188
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• 2005

The impacts of turbidity flow induced by summer rainfall events on water supply, aquatic ecosystems, and socioeconomics are significant and major concerns in most of reservoirs operations. As a decision support tool, the real-time turbidity flow monitoring and modeling system RTMMS is under development using a laterally integrated two-dimensional (2D) hydrodynamic and water quality model. The objectives of this paper is to present the preliminary field observation results on the characteristics of rainfall-induced turbidity flows and their density flow regimes, and the model performance in replicating the fate and transport of turbidity plume in a reservoir. The rainfall-induced turbidity flows caused significant drop of river water temperature by 5 to $10^{\circ}C$ and resulted in density differences of 1.2 to $2.6kg/m^3$ between inflow water and ambient reservoir water, which consequently led development of density flows such as plunge flow and interflow in the reservoir. The 2D model was set up for the reservoir. and applied to simulate the temperature stratification, density flow regimes, and temporal and spatial turbidity distributions during flood season of 2004 After intensive refinements on grid resolutions , the model showed efficient and satisfactory performance in simulating the observed reservoir thermal stratification and turbidity profiles that all are essentially required to enhance the performance of RTMMS.

• Journal of Korean Society on Water Environment
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• v.21 no.1
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• pp.21-28
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• 2005

The training and prediction modeling using an artificial neural network was implemented to predict the turbidity of treated water as well as to estimate the optimized feed concentration of polyaluminium chloride (PACl) in a water treatment plant. The parameters used in the input layers were pH, temperature, turbidity and alkalinity, while those in output layers were PACl and turbidity of treated water. Levenberg-Marquadt method of feedforward back-propagation perceptron in the neural network toolbox of MATLAB program was used in this study. Correlation coefficients of the training data with the measured data were 0.9997 for PACl and 0.6850 for turbidity and those of the testing data with measured data were 0.9140 for PACl and 0.3828 for turbidity, when four parameters at input layer, 12-12 nodes each at both the first and the second hidden layers, and two parameters(PACl and turbidity) at output layer were used. Although the predictability of PACl was improved, compared to that of the previous studies to use the only coagulant dose as output layer, turbidity in treated water could not be predicted well. Acquisition of more data through several years obtained with the advanced on-line measuring system could make the artificial neural network useful and practical in actual water treatment plants.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.5
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• pp.659-668
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• 2012

In Korea, almost every water treatment plant suffers from seasonal problem of algae and turbidity which result from eutrophication and heavy rainfall. To relieve this problem, experimental investigation was performed to study the applicability of a floating-media and sand filter to preliminary water treatment in terms of algae and turbidity removal. Experimental results using pure-cultured algae influent showed that the shape of algae species as well as filtration velocity affects the removal efficiency. From the experiments using natural river water, it was concluded that algae removal is more sensitive to floating-media depth but turbidity more sensitive to sand depth. As the filtration velocity increased, the removal of turbidity decreased but that of algae was not affected. The floating-media and sand filter removed more than 30 % of TP, TN, turbidity, Chl-a and CODcr, and less than 20 % of DOC and $UV_{254}$.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.2
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• pp.135-143
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• 2005

It was evaluated that the effect of turbidity removal by Pore Controllable Fiber Filter(PCF) installed in NS(Naksang) small water treatmant plant(system) using under flow water as raw water in the study. The results of the study are as the followings. Firstly, the removal efficiency of turbidity by PCF without coagulation(in operation mode not using coagulants) was mostly below 20 percent. On the other hand, when operation using proper coagulants, that of turbidity was mostly over 80 percent. Secondly, slow sand filtration after PCF, total turbidity removal efficiency of final treated water was 84.3 percent, and the contribution by PCF was 57.1 percent and that of slow sand filtration was 27.7 percent. Therefore the introduction of PCF as pre-treatment process would be helpful to reduce the loading of high turbidity of slow sand filtration. Thirdly, the results of particle counter measurements showed that when operated PCF with coagulants, fine flocs captured or adsorbed at the pore of PCF were flow out into the effluents from 120 minutes after backwashing because of the increase of headloss of PCF. Therefore the decision of backwashing time should made consideration into the outflow of fine flocs from PCF. Fourth, coagulant dosages on PCF at the same turbidity was largely variable because of the effect of the raw water characteristics and the turbidity increase velocity at rainy days, therefore flexible coagulant dosages should be considered rather than fixed coagulant dosage by the influent jar-test result.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.1
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• pp.71-78
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• 2006

The removal of protozoa in the coagulation process was evaluated under the different pH and turbidity using the jar test after the addition of polyaluminium chloride (PAC) as a coagulant. Two well-known protozoa of Cryptosporidium parvum and Giardia lamblia were tested at the same time with turbidity, the critical water quality parameter of the water treatment process. Both protozoa were removed about 1log (and up to 2log) at the optimum injection of PAC. The source water turbidity and pH affected the removal of protozoa and turbidity. At neutral and alkaline pH, 1.3-1.7log removal of protozoa for low turbid water with 5NTU, and 1.6-2.3log removal for high turbid water with 30NTU were achieved. However, at acidic pH, maximum 0.8-1.0log and 1.1-1.2log were removed for low and high turbid water, respectively, at the optimum PAC injection of 15mg/L. The relation of protozoa and turbidity removals were expressed as the 1st order equation (significantly positive relation) in the most of the tested conditions. In addition, the relation of protozoan removals with residual turbidity were also expressed the 1st order equation (significantly negative relation), although the significance of the equations were reduced at acidic pH. Therefore, residual turbidity could be a good index of efficient protozoan removal in the coagulation process, probably except at the low pH condition.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.1
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• pp.37-46
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• 2007

A feasibility test was conducted to evaluate the addition of turbidity substance in a coagulation process to remove natural organic matters (NOM), the precursor of disinfection by-products (DBPs). The experimental water sources were synthetic water containing 5 mg/L of humic acid and 50 mg/L of NaHCO3 and drinking water resource of Ulsan city (S Dam water, D Dam water and Nak-Dong raw water). The examined turbidity substances were kaolin, acid clay, and modified clay (0.38 meq $NH_4{^+}-N/g$ clay). In Jar tests at different concentrations of the turbidity substances (5, 10, 15, 20, 30 mg/L) using the synthetic water, the turbidity substances improved the removal of turbidity, UV-254 absorbance and dissolved organic carbon (DOC) by 23.8-38.1%, 17.0-24.5% and 2.5-44.5%, respectively. The modified clay showed higher removal efficiencies than other substances. In Jar tests using the drinking water, 10 and 20 mg/L of modified clay enhanced the removal efficiencies of turbidity, UV-254 absorbance, DOC, trihalomethane formation potential (THMFP), and haloacetic acid formation potential (HAAFP) by 3.0~4.3%, 19.1~29.0%, 12~34.9%, 4.9~36.7%, and 1.6~30.2%, respectively.

• Analytical Science and Technology
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• v.24 no.5
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• pp.378-386
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• 2011

The goal of this study is to understand the influence of temperature on phosphorus release rate from soil into water. As the temperature increases, $PO_4$-P reaches equilibrium more quickly and the equilibrium concentration increases, and thus the $PO_4$-P concentration increases, and pH decreases. The $PO_4$-P concentration affects pH. $PO_4$-P released from turbidity is not adsorbed onto the turbidity. $PO_4$-P was independent on the turbidity and yet $PO_4$-P was steadily increasing. However, $PO_4$-P was dependent upon the turbidity concentration as the turbidity releases $PO_4$-P. The total phosphorous (T-P) and turbidity were directly linked because T-P changed with the turbidity. T-P includes the $PO_4$-P content of water and the phosphorus content of the turbidity. As the temperature decreases, density of water increases, and the precipitation of turbidity decreases, resulting in an increases in T-P concentration. As the temperature increases, the T-P concentration decreases, but the PO4-P release rate from turbidity increases. At the same time, even at different temperatures, the T-P concentrations of the samples were about the same. When the lake gets deepened, the water temperature decreases, hence, the phosphorus release rate from soil into water was decreased. This mechanism is of great interest because phosphorus is released from soil sediment into the lake water.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.933-942
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• 2006

In this study, temperature, turbidity, suspended paniculate matter (SPM) distribution and mineral characteristics were investigated to explain spatial distribution of the turbid-water environment of Yongdam reservoir in July, 2005. Six stations were selected along a longitudinal axis of the reservoir and sampling was conducted in four depths of each station. Water temperature was showed the typical stratified structure by the effects of irradiance and inflow. Content of inorganic matter in suspended particles increased with the concentration of suspended particulate matter (SPM) due to the reduction of ash-free dry matter (AFDM). Turbidity ranged from 0.6 to 95.1 NTU and the maximum turbidity value of each station sharply increased toward downstream from upstream. The high turbidity layers were located at the depth between 12~16 m. Particle size ranged from 0.435 to $482.9{\mu}m$. day and silt-sized particles corresponded 91.9~98.9% and 1.1~8.0% in total numbers of SPM, respectively. Turbidity showed high correlations with clay (r=0.763, p<0.05) and silt content (r=0.870, p<0.05).Inorganic matter content (r=0.960, p<0.01) was more correlated with turbidity than organic matter (r=0.823, p<0.05). Mineral characterization using x-ray diffraction and electron probe microanalyzer demonstrated that the major minerals contained in the SPM were kaolinite, illite, vermiculite and smectite. As results of this study, surface water discharge as well as small size of the SPM were suggested as long-term interfering factors in settling down the turbid water in the reservoir.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.1
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• pp.93-103
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• 2008

This study was conducted to optimize a unidirectional flushing program in distribution pipes by analysis of water pressure, velocity, quality, and other parameters during flushing. As a result, correlation coefficient between flushed pipe length and the flushing duration was obtained $R^2=0.83$ and the equation $Y_{Time}=0.0571{\cdot}X_{Pipe\;length}+4.7648$ for 10 pipes. The averaged flushing velocity in the pipes, 1.1 m/s, was enough to remove loose deposits on the inner wall of the pipes. 3 of 92 water samples taken during flushing met the National Drinking Water Quality Standard for Fe and Mn, but not for Al. Turbidity less than 1 NTU is suggested for the appropriate criteria to finish flushing in pipes. The coefficient of determination ($R^2$) between turbidity and TSS was 0.95 and the equation was induced as $Y_{TSS}=1.2068{\cdot}X_{Turbidity}$. The amount of removed deposits could be estimated from the turbidity data of discharged water in field because turbidity and TSS in the discharged water is highly correlated.