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Effective management of water quality in watersheds is crucial because it is directly linked to the sustainability of aquatic ecosystems. In conventional watershed management, best management practices (BMPs) have been instrumental in addressing deteriorating water quality issues caused by non-point source pollution. Greenhouse gas (GHG) emissions have emerged as a global concern, necessitating immediate and diverse environmental actions to mitigate their impacts. This study aims to explore BMPs that maximize total phosphorus (TP) load removal efficiencies, while minimizing costs and GHG emissions within watersheds, using the Soil and Water Assessment Tool (SWAT) and non-dominated sorting genetic algorithm III (NSGA-III). The Yeongsan River Watershed between 2012 and 2021 was selected as the study area. Hydrological and BMP data were analyzed. Applying identical BMPs to the watershed showed that the BMPs with high TP removal efficiency may not be effective in terms of cost and GHG emissions. Therefore, the optimal combination of BMPs for the Yeongsan River Watershed was determined using NSGA-III considering TP removal efficiency, cost, and GHG emissions. This study is the first to consider GHG emissions at the watershed level when applying BMPs and is expected to contribute to the development of BMP implementation incorporating GHG emissions.

The numerical study was conducted to compare process performance depending on the pump type and process configuration. The daily monitoring data of seawater temperature and salinity offshore from Daesan, Republic of Korea was used to reflect the site-specific seawater conditions. An algorithm for reverse osmosis in constant permeate mode was developed to simulate the process in time-variant conditions. Two types of pumps with different maximum leachable efficiencies were employed to organize pump-train configuration: separated feed lines and common pressure center design. The results showed pump type and design configuration did not have a significant effect on process performance. The annual means of specific energy consumption (SEC) for every design configuration were under 2 kWh/m3, except for a worst-case. The worst-case was decided when the pump was operated out of the best operation range. The two operation strategies were evaluated to determine the optimal configuration. The permeate flow rate was reduced to 80% of the designed permeate flow rate with two approaches: feed flow rate reduction in every train and pump shutdown in a specific train. The operation mode with feed flow rate reduction was more efficient than the other. The operating pressure reduction led to a decrease in SEC.

The potassium (K) sorption characteristics with three adsorbents, natural zeolite, ammonium acetate-treated zeolite, and manganese nodule, were studied and compared to see the potential use of manganese nodule as an alternative K adsorbent. In general, the Langmuir isotherm could fit the K sorption in the KCl solutions at different pH conditions better than the Freundlich isotherm. Based on the Langmuir parameters, the maximum K sorption was greater for the zeolite-based adsorbents (i.e., 40–42 mg g−1) than the manganese nodule (i.e., 2.0 mg g−1) at acidic conditions, while the manganese nodule (i.e., 9.7 mg g−1) showed better K sorption at neutral conditions. With the seawater samples, the zeolite-based adsorbents showed higher K recovery (4–14%) than the manganese nodule (0–8.8%). The K sorption on the zeolite-based adsorbents followed the pseudo-second-order kinetics and the K sorption rates were higher for the treated zeolite than the natural zeolite. The repeated sorption tests showed that the natural zeolite could potentially be reused up to three times without any significant loss of K sorption capacity, while the ammonium acetate-treated zeolite lost its K sorption capacity after the single sorption test. Overall, the results show that the manganese nodule may potentially be the alternative to zeolite for K recovery under certain conditions, yet the zeolite-based adsorbents are generally better than the manganese nodule. Thus, more studies to enhance the K recovery using zeolite, including surface modified zeolite, are recommended.

Osmotic and hydraulic pressures are both indispensable for operating membrane-based desalting processes, such as forward osmosis (FO), pressure-retarded osmosis (PRO), and reverse osmosis (RO). However, a clear relation between these driving pressures has not thus far been identified; hence, the effect of change in driving pressures on systems has not yet been sufficiently analyzed. In this context, this study formulates an actual mathematical relation between the driving pressures of membrane-based desalting processes by taking into consideration the presence of energy loss in each driving pressure. To do so, this study defines the pseudo-driving pressures representing the water transport direction of a system and the similarity coefficients that quantify the energy conservation rule. Consequently, this study finds three other theoretical constraints that are required to operate membrane-based desalting processes. Furthermore, along with the features of the similarity coefficients, this study diagnoses the commercial advantage of RO over FO/PRO and suggests desirable optimization sequences applicable to each process. Since this study provides researchers with guidelines regarding optimization sequences between membrane parameters and operational parameters for membrane-based desalting processes, it is expected that detailed optimization strategies for the processes could be established.

The Chao Phraya River in Thailand has been greatly affected by climate change and the occurrence of extreme flood events, hindering its economic development. This study assessed the hydrological responses of the Chao Phraya River basin under several climate sensitivity and greenhouse gas emission scenarios. The Soil and Water Assessment Tool (SWAT) model was applied to simulate the streamflow using meteorological and observed data over a nine-year period from 2003 to 2011. The SWAT model produced an acceptable performance for calibration and validation, yielding Nash-Sutcliffe efficiency (NSE) values greater than 0.5. Precipitation scenarios yielded streamflow variations that corresponded to the change of rainfall intensity and amount of rainfall, while scenarios with increased air temperatures predicted future water shortages. High CO2 concentration scenarios incorporated plant responses that led to a dramatic increase in streamflow. The greenhouse gas emission scenarios increased the streamflow variations to 6.8%, 41.9%, and 38.4% from the reference period (2003–2011). This study also provided a framework upon which the peak flow can be managed to control the nonpoint sources during wet season. We hope that the future climate scenarios presented in this study could provide predictive information for the river basin.

Global energy consumption has been highly dependent on fossil fuels which cause severe climate change and, therefore, the exploration of new technologies to produce effective renewable energy plays an important role in the world. Pressure-retarded osmosis (PRO) is one of the promising candidates to reduce the reliance on fossil fuels by harnessing energy from the salinity gradient between seawater and fresh water. In PRO, water is transported though a semi-permeable membrane from a low-concentrated feed solution to a high-concentrated draw solution. The increased volumetric water flow then runs a hydro-turbine to generate power. PRO technology has rapidly improved in recent years; however, the commercial-scale PRO plant is yet to be developed. In this context, recent developments on the PRO process are reviewed in terms of mathematical models, membrane modules, process designs, numerical works, and fouling and cleaning. In addition, the research requirements to accelerate PRO commercialization are discussed. It is expected that this article can help comprehensively understand the PRO process and thereby provide essential information to activate further research and development.

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

Pressure-retarded osmosis (PRO) is viewed as a highly promising renewable energy process that generates energy without carbon emissions in the age of the climate change regime. While many experimental studies have contributed to the quest for an efficiency that would make the PRO process commercially viable, computational modeling and simulation studies have played crucial roles in investigating the efficiency of PRO, particularly the concept of hybridizing the PRO process with reverse osmosis (RO). It is crucial for researchers to understand the implications of the simulation and modeling works in order to promote the further development of PRO. To that end, the authors collected many relevant papers and reorganized their important methodologies and results. This review, first of all, presents the mathematical derivation of the fundamental modeling theories regarding PRO including water flux and concentration polarization equations. After that, those theories and thermodynamic theories are then applied to depict the limitations of a stand-alone PRO process and the effectiveness of an RO-PRO hybridized process. Lastly, the review diagnoses the challenges facing PRO-basis processes which are insufficiently resolved by conventional engineering approaches and, in response, presents alternative modeling and simulation approaches as well as novel technologies.

This study developed tree-based ensemble machine learning models to predict forward osmosis (FO) permeate flux using extreme gradient boosting (XGBoost) and light-gradient boosting machine (LGBM) methods. The models were trained by approximately 700 data points from the FO experimental data. The results showed that LGBM and XGBoost could predict the FO permeate flux with very high accuracy (>0.95 of R2) in the test set. Feature analysis using Shapley additive explanations values was performed to identify the influences of input variables on the model output and the correlation between the input variables. The results revealed that water permeability and pressure difference have the most significant variables on the FO permeate flux. The correlation between the operating conditions and water permeability cannot be neglected. In this study, we clarified the applicability of ensemble machine learning models for FO systems and suggested directions for future data collection. [ABSTRACT FROM AUTHOR]

Seawater reverse osmosis (SWRO) has played a key role in alleviating water stress worldwide. However, its intensive energy consumption, which is highly dependent on fossil fuels, needs to be reduced to mitigate the CO2 emissions resulting from SWRO. Many technologies, including pressure-retarded osmosis (PRO), have been applied in an attempt to reduce the energy consumption of SWRO. In this study, potential trends in CO2 emissions from SWRO processes in Korea are considered based on SWRO operational data collected from a range of sources and relating to societal scenarios of the future. These societal scenarios are based on current population trends in Korea and the proportion of renewable energy currently utilized in Korea. Next, using a dimensionless performance index for the SWRO-PRO hybrid process, the study presents the potential efficacy of PRO in reducing the magnitude of CO2 emissions from SWRO processes in Korea. The analysis shows that CO2 may be reduced by 10%-40%. The results of the study suggest a promising role for PRO as a suppressor of CO2 emissions. It is anticipated that the study will facilitate further in-depth studies into the use of PRO. [ABSTRACT FROM AUTHOR]

It is essential that water resource management plans require explicit links across different spatial scales in a single watershed. This study aimed to identify existing gaps between sub-basin and watershed management strategies using a watershed simulation model soil and water assessment tool (SWAT). The SWAT was calibrated and validated at the upper and middle regions of the Yeongsan River in Korea using past weather inputs from 2012 to 2014. The simulation outputs from 2014 used as a reference were compared to those with future weather scenarios in 2032 produced from a regional climate model (RCM) such as RCM 2.6, 4.5, 6.0, and 8.5. We found that the calibration and validation results agreed well with the observed data, yielding Nash--Sutcliffe efficiency values more than 0.72 for daily streamflow as well as for monthly sediment and total phosphorus (TP) loads at the final watershed outlet. However, all projected weather data led to a significant difference in streamflow only at the sub-basin level, while all three variables at the watershed level varied significantly from scenario to scenario. These imply that water resource management plans determined at the watershed scale may not be suitable for those at the sub-basin scale, calling for a more refined search that harmonizes both management decisions. [ABSTRACT FROM AUTHOR]

Statistical analyses are often subject to misinterpretation due to poor data quality which is inaccurate, incomplete, or unavailable. This study describes how incomplete data diminishes the screening accuracy of water pollution hotspots using a self-organizing map (SOM), a popular algorithm in reducing the dimension of complex data in a nonlinear fashion. A full data set consisting of 12 water quality and quantity parameters monitored monthly over 3 years at the Yeongsan River in Korea was provided to SOM as a reference input. For purposes of comparison, SOM was further allowed to accept three incomplete data sets in terms of variable availability as well as data loss for single and multiple parameters and different pollution levels. We found that data loss of either single or multiple parameters exceeding 15% of the entire data set led to significant changes in spatial and temporal patterns of the original data. However, the variables intentionally unavailable in the given data set affected the screening performance of water pollution hotspots in SOM, to a less obvious extent, as long as the percentage of missing data fell below 10%. The same applied to data loss with three pollution levels, from high through moderate to low concentrations of one important variable. Therefore, we recommend the use of multiple approaches that couple dimensionality reduction algorithms with reasonable imputation methods for the data set with a high percentage (e.g. above 15%) of missing values. [ABSTRACT FROM AUTHOR]

In this study, we conducted a numerical simulation to evaluate stability in the seawater reverse osmosis (SWRO) process against seasonal variations of feed water. The impact of element configuration on SWRO operation was analyzed using a process model consisting of the solution--diffusion model. Seawater quality and temperature data collected from the intake of the Fujairah desalination plant (United Arab Emirates) were used to identify optimum internally staged design (ISD) configurations. Monitoring data were also used to simulate changes in SWRO performance. The process performance of the ISD configurations as compared to conventional design configurations in terms of specific energy consumption, permeate total dissolved solids concentration and recovery ratio. The results showed that SWRO processes with four ISD configurations met the performance requirements over 12 months despite the fluctuations in seawater quality and temperature. The prediction results of the ISD configurations showed stable operation during the 12 month simulation period. These configurations had a significant advantage with respect to membrane maintenance. This study's findings could be used to optimize the element configuration of the SWRO process by predicting the process performance while accounting for seasonal variations of seawater quality and temperature. [ABSTRACT FROM AUTHOR]

Eutrophication is caused by the discharge of excess nutrients from various point and nonpoint sources. In particular, nonpoint sources of nutrients are difficult to identify because of diffuse sources and the effects of climate change. The objective of this study is to investigate the change in nutrient loading from land areas under climate change. In this study, the soil and water assessment tool (SWAT) model was applied to predict the sediment, total nitrogen (TN), and total phosphorus (TP) loads in the Yeongsan River Basin using geographical information (i.e., digital elevation map, land use, and soil type), observed data (i.e., meteorological data, streamflow, and water quality). Future climatic variables were obtained from a general circulation model, which was simulated using four representative concentration pathway (RCP) scenarios such as RCP 2.6, 4.5, 6.0, and 8.5. The results showed that flow rate, sediment load, TN load, and TP load were changed in terms of climate change under the different RCP scenarios. Impacts of climate change on a basin may be an important factor in eutrophication management in the Yeongsan River. The SWAT model provided an accurate prediction of the current nutrient pollutant loads and facilitated the reliable prediction of future nutrient pollutant loads under climate change. [ABSTRACT FROM AUTHOR]

Multiplex PCR analyses of DNAs from genotypically unique Escherichia coli strains isolated from the feces of humans and domesticated animals from Jeonnam Province, South Korea, have shown that none of the strains have belonged to Escherichia coli phylogenetic group B2. The results have shown that the relationship between the presence and types of virulence genes and phylogenetic groupings have differed among geographically distinct Escherichia coli populations.

A Kalman filter (KF) algorithm is a recursive data processing algorithm typically recommended for estimating unmeasured state variables in chemical engineering processes. We studied the performance of a KF algorithm in a range of noise levels as well as indirectly observed data from the process model. For this study, two versions of the KF algorithm widely adopted for a nonlinear system, the extended Kalman filter and the unscented Kalman filter, were applied to 1-year time series data monitored during the operation of the Fujairah seawater reverse osmosis desalination plant. We found that variables indicating the state of fouling, membrane resistance, and solute permeability agreed well with those estimated by the two KF algorithms, specifically in terms of noise reduction and peak detection. When the two KF algorithms were exposed to various noise levels, they showed a corresponding increase in the error rates (for unmeasured state variables) according to the noise levels varying from 10% to 50%, regardless of the algorithms used. The two KF algorithms provided a good prediction performance only for the permeate flow rate rather than for the permeate concentration, out of the two types of measured data. However, the individual KF algorithms still showed different performances in computing estimated and predicted data in the reverse osmosis process. This result calls for further research on the determination of the best KF algorithms for either estimation or prediction of directly and indirectly measured state variables in various chemical processes. [ABSTRACT FROM AUTHOR]

Hollow fiber is one of the most widely used membrane types in the field of membrane-based water treatment, as hollow fiber membranes contain self-supporting structures and are easy to scale up due to their high productivity-per-unit volume. In the commercialization of hollow fiber membranes, module configuration is a key issue that is closely related to system performance. Especially for membrane distillation process, proper design of hollow fiber membrane module is necessary considering mass and heat transport. In an effort to enhance such performance, the inner-workings of the transport phenomena inside these membrane modules are investigated via various numerical approaches. A review of membrane module configurations allows for an examination of recent advances, applications, and numerical approaches related to hollow fiber modules; this is accomplished by a consideration of the characteristics of membrane distillation processes. Based on an analysis of previous research studies, and in an attempt to contribute to improved performance, potential challenges and applications of novel hollow fiber membrane modules are suggested. [ABSTRACT FROM AUTHOR]

In this study, 98%-100% of cross-linked polyamide (PA) membranes were synthesized by interfacial polymerization between trimesoyl chloride (TMC) and m-phenylenediamine (MPD), using molecular dynamics (MD) simulations. Previous studies have not been able to synthesize such highly crosslinked membranes, due to a lack of monomer diffusion near the unreacted sites inside the complex three-dimensional PA matrix. This barrier was removed by raising the temperature of the reaction mixture to 1,000 K for 0.02 ns, which increases the monomer diffusion inside the PA matrix for cross-linking. In this study, an automatic, fully generalized, and self-contained Python module was developed to synthesize PA membranes using MD simulations, employing a new algorithm that mimics the physicochemical synthesis of the membranes. Initial amounts of 100, 200, 300, and 400 TMC molecules with varying ratios (TMC:MPD) of 1:0.5, 1:1, 1:1.5, 1:2, 1:2.5, and 1:3 were used. The reaction progress was investigated based on the number of amide bonds formed, which showed an exponentially decreasing trend. The effect of the TMC to MPD ratio on membrane size and degree of cross-linking (DOCL) revealed that a 1:2 (TMC:MPD) ratio was optimal for maximum membrane growth, with approximately 90% TMC consumption and 90% cross-links. The observed DOCL in the membranes synthesized with TMC:MPD < 1:2 was around 99%, and in a few cases 100% cross-linked membranes were obtained. The cross-sectional examination of membranes showed two distinct pore sizes of approximately 5.32 Å ± 0.66 and less than 5.17 Å ± 0.74, for the membranes synthesized with TMC:MPD = 1:1 and <1:1, respectively. These results will be achieved with the ability to synthesize membranes of various DOCLs, by simply changing the ratio between the monomers. The module is available free of charge, and any efficacious suggestions for the improvement of this module will be fully acknowledged. [ABSTRACT FROM AUTHOR]

Membrane fouling significantly affects membrane performance, but cleaning and replacement schedules are often set at regular time intervals, regardless of the extent of deterioration in performance. The aim of this study is to develop an improved prediction model for membrane fouling in the seawater reverse osmosis (SWRO) process using a hybrid machine learning approach. A Kalman filter (KF) was combined with either an artificial neural network (ANN) or a support vector machine (SVM)--a family of machine learning models--in series. The performance of these integrated models was evaluated with training and testing data sets compiled from the Fujairah SWRO plant over a period of 18 months. Our findings showed that the SVM alone provided, on average, slightly better prediction of membrane resistance (an indirect indicator of membrane fouling) than a single ANN during training and testing sets. However, hybrid machine learning methods consistently outperformed any single model, with the combination of the KF and SVM exhibiting better performance than that of the KF and ANN, except for one special case in which the accuracy of a single SVM already exceeded 0.8 for both Nash-Sutcliffe model efficiency and R². Taken together, our results demonstrated that the hybrid machine learning approach not only enhanced the prediction ability of membrane resistance in classical fouling and machine learning models, but could also be used to adjust cleaning and replacement schedules correctly in response to progressive deterioration in membrane performance during operation. [ABSTRACT FROM AUTHOR]

Watershed-scale water quality models are often used for interpreting changes in complex environmental systems. Precipitation is a primary control affecting the output of watershed-scale water quality model, and higher resolution of precipitation data is highly desirable. The objective of this study was to investigate whether the radar rainfall estimates can improve the accuracy of stream flow, TSS load, and TP load simulations with the soil and water assessment tool for high- and low-flow conditions. Yeongsan River watershed (YRW) was selected for this study. This watershed, located south-west of Korean Peninsula, has an area of about 2,938 km², and is divided into 25 sub-watersheds. The simulations were conducted under different rainfall datasets: (1) rainfall observations from nine ground rain gauges (GR), (2) 25 corrected radar rainfall estimates (RR), and (3) a combination of nine ground rain gauges and 16 corrected radar rainfall estimates that represent the 16 ungauged sub-watersheds in YRW (GARR). Simulation results under different the rainfall datasets were compared using the Nash–Sutcliffe efficiency coefficient and percentage bias. The prediction of both high and low stream flows using GARR was better than using GR and RR data. The model performance for predicting TSS load was significantly better under GARR data than under GR and RR data. In case of TP, the model performances using RR and GARR data were significantly better than that using GR data. Overall, combining gauge rainfall and corrected radar rainfall led to an improvement in the prediction accuracy for the watershed-scale water quality model. [ABSTRACT FROM AUTHOR]

Land use and cover is one of the important factors that influence water flow within a watershed. Alteration of land use through time affects hydrological processes as well as the water budget in the watershed. In analyzing these processes that occur within basins and watershed, Soil and Water Assessment Tool (SWAT) is considered an indispensable tool. In this study, scenario analysis was done through the SWAT model by simulating stream discharge in a sub-basin in Yeongsan River Basin for the years 1990 (scenario 1) and 2000 (scenario 2). Input data in two scenarios were all the same except for the land use data. Land use data shows a decrease in forest and agricultural land area and increase in urban area from year 1990 to 2000. Developed models were evaluated to have acceptable performance since statistical parameters Nash-Sutcliffe efficiency (NSE) and ratio of mean squared error to the standard deviation of the measured data (RSR) in the validation for both scenarios are within the satisfactory rating limits. Water budget analysis showed an increase in annual average surface runoff and decrease in annual average evapotranspiration and lateral flow which is attributed decrease in forest and agricultural area and increase in urban area. [ABSTRACT FROM AUTHOR]

Accurate assessment of piping systems’ risk to damage reduces annual operation and maintenance costs. Recently, extreme climate events (e.g. cold snaps or heavy snow) due to global climate change have increased pipe system failure. The objective of this study is to establish a framework for developing screening models of pipe failure events, due to water network systems freezing using two statistical approaches. More specifically, logistic regression was used to estimate the probability of failure at a household level, whereas the customized model developed to predict the frequency of community-wide failure events. The data recorded at least one failure event in Korea from 2008 to 2015, which was provided to the logistic regression model. The customized model, however, only used the data set compiled from three areas of concern with the highest frequency of the failures. Results showed that the logistic model showed the best performance out of the 11 constructed models, in terms of R and the variance inflation factor (of lower than two). The logistic model incorporated three variables: the minimum temperature on the day of failure, the natural logarithm of the total water usage in the previous month and the mean minimum temperature over the previous 10 days. The selected model had an overall prediction accuracy of 66.4%. When the customized model at the community level was examined the three models not only yielded moderate R² values ranging from 0.53 to 0.66, but also helped identify water network systems at risk of failures. Overall, this study demonstrated that the proposed methodology can be used to highlight areas of concern at different geographic scales, along with refining existing statistical models with new variables updated in real time. [ABSTRACT FROM AUTHOR]

While the size of single low impact development (LID) unit determines the performance in attenuating water quantity and quality from storm runoff, the performance of multiple LID units is sensitive to both their size and arrangement order. This study describes a methodology to obtain the best performance for multiple LID units under varying by time-to-peak of storm with the same intensity and duration using storm water management model (SWMM), a popular model for rainfall-runoff and water quality simulation. The hypothetical temporal distributions were designated by Huff curves, which provided characterizing storm mass curves, along with the relationship of intensity-duration-frequency to determine storm intensity for 1-h in 2-year return period. Three types of LID units (rain barrel, infiltration trench, and vegetative swale) were selected to develop aggregate LID scenarios using the SWMM. The results indicated that, when compared with other field experiments, the SWMM successfully estimated change in flow discharge and suspended solid (SS) loss reflecting different storm patterns at the final outlet of and urbanized sub-catchment as well as the effects of LID practices. The performances of aggregate LID scenarios including lag time of peak runoff, peak runoff reduction, volume reduction, and SS loss reduction were sensitive to arrangement order and time-of-storm peak-to-storm duration. Scenario 5, which had the order of vegetative swale, rain barrel, and infiltration trench, showed the most effective serial arrangement, as it exhibited the more consistent results across the storm patters. This study thus provides insights into the effective design of aggregate LID scenarios considering different storm characteristics. [ABSTRACT FROM AUTHOR]

Successful data analysis is an essential component of any environmental monitoring programs. This study introduces an effective data analysis method to identify water pollution hotspots as well as to drop redundant monitoring parameters and samples using a self-organizing map (SOM), which has a strong specialty in pattern extraction from complex monitoring data. A full data set consisted of nine parameters that were obtained on a monthly basis from 83 sites in various tributary streams along the Yeongsan River, Korea, from May 2011 to December 2015. The given data set was further partitioned into a number of subsets to examine their effect on variable importance and temporal pattern analysis. We found that water pollution hotspots were more clearly addressed in load-based SOM analysis than in concentration-based SOM analysis due to strong correlation between variables resulted from variability reduction by combining two variables into a single one for load analysis. In addition, the variables chemical oxygen demand and electrical conductivity and the parameters discharge and total nitrogen were found to participate most and least actively in describing spatial and temporal variation of the observed variables, respectively. About 35% of the sampling locations showed high similarity among the monthly data extending from November in the previous year to February in the following year. We believe that the proposed methodology can be useful in revising the upcoming water monitoring study by clarifying several issues related to monitoring parameters and frequency in the existing program. [ABSTRACT FROM AUTHOR]

This paper describes the effects of weir construction on water quality in the Yeongsan (YS) River. In general, weirs affect aquatic environments of rivers and streams by interrupting natural water flow. To identify changes in water quality before and after weir construction, analysis of variance and autocorrelation tests were conducted on data from four monitoring stations. Seasonal variations in suspended solids (SS), biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and pH were analyzed using data from six years of monitoring. No significant changes in mean BOD5 or COD followed the construction of weirs. However, mean TN and TP were changed significantly during and after construction. Moreover, SS and pH changed significantly during construction, and TN and TP concentrations were slightly improved after weir construction. However, continuous monitoring and analysis of water quality changes in each weir are required to prevent environmental disasters such as algal blooms. [ABSTRACT FROM AUTHOR]

The objective of this study is to design spacers using topology optimization in a two-dimensional (2D) crossflow reverse osmosis (RO) membrane channel in order to improve the performance of RO processes. This study is the first attempt to apply topology optimization to designing spacers in a RO membrane channel. The peiformance was evaluated based on the quantity of permeate flux penetrating both the upper and lower membrane sulfates. Here, Navier-Stokes and convection-diffusion equations were employed to calculate the permeate flux. The nine reference models, consisting of combinations of circle, rectangle, and triangle shapes and zig-zag, cavity, and submerged spacer configurations were then simulated using finite element method so that the peiformance of the model designed by topology optimization could be compared to the reference models. As a result of topology optimization with the allowable pressure drop changes in the channel, characteristics required of the spacer design were determined. The spacer design based on topology optimization was then simplified to consider manufacturability and peiformance. When the simplified design was compared to the reference models, the new design displayed a better peiformance in terms of permeate flux and wall concentration at the membrane surface. [ABSTRACT FROM AUTHOR]

The fouling behavior of seawater reverse osmosis membranes has yet to be definitively investigated due to the complexity of seawater compositions. In this study, a time-series image analysis (TSIA) was performed to investigate the fouling mechanism using scanning electron microscopy (SEM) and atomic force microscopy (AFM). The fouling experiments were conducted with synthetic seawater (SS) and SS mixed with humic acid substances (SHA). The effect of operational time was investigated for 2, 4, 6, 8, 12, and 20 h. According to the TSIA results, different fouling characteristics between SS and SHA experiments were observed. In the SS case, the fouling mechanism is the interaction between inorganic particles and the membrane surface as well as interaction between inorganic particles and the deposited foulants. Then, increased accumulation of deposited foulants was observed with respect to the operational time. However, in the presence of humic acid, the fouling mechanism was significantly influenced by the adsorption of humic acid onto the membrane surface at the initial stage (first 2 h). This organic layer traps inorganic particles and organic substances, and accelerates the fouling formation on the membrane surface, thereby leading to a greater flux decline compared to the SS experiment. [ABSTRACT FROM AUTHOR]

The development and exploitation of sustainable and environment-friendly energy resources are required in order to resolve global energy shortages. Recently, salinity gradient power (SGP) has been considered a feasible candidate, with high potential to become a substitute for the current use of fossil fuels due to benefits such as less periodicity, abundance and no emission of carbon dioxide. In this paper, one SGP, pressure-retarded osmosis (PRO) system, was reviewed in terms of its mechanism, limitations and available applications. In the PRO system, water permeates through a semipermeable membrane from the feed solution to the draw solution, and energy is generated by depressurizing the permeated flow through a hydro turbine. Models for understanding its mechanism and for improving of its performance were reviewed. In addition, applications of sea water reverse osmosis (SWRO), wastewater treatment (WWT) and PRO hybrid process were introduced in order to develop new water-energy nexus processes. In particular, it is thought that the SWRO--PRO hybrid process and SWRO--PRO--WWT hybrid process can contribute to reducing the total energy consumption in SWRO plants as well as to applying the SGP energy to other engineering fields. [ABSTRACT FROM AUTHOR]

Membrane filtration is lately becoming a popular process for the pre-treatment of seawater in reverse osmosis (SWRO) desalination. The common practiced method of controlling membrane fouling and reducing the treatment costs usually involves a short coagulation step prior to the membrane filtration. In this work, a study of the feasibility of a coagulation free microfiltration (MF) as pre-treatment for SWRO desalination from a technical, economical and environmental point of view was performed. The experimental part included filtration of seawater from the Yellow Sea in Korea both with and without granular activated carbon (GAC) pre-treatment using a laboratory scale MF plant and different constant fluxes in outside-in dead-end mode. The results show that a coagulant and GAC free, stand-alone microfiltration using low fluxes and intense chemical cleanings is technically possible. When compared to a state of the art coagulation-MF, such a process could be economically and environmentally favourable when accounting for the lower energy demand and the relinquishment of the sludge treatment system. [ABSTRACT FROM AUTHOR]

A lab-scale vortex separator, based on a swirling motion to remove settleable particles, was used to remove suspended solids in urban stormwater runoff. However, the treatment is limited to the removal of large settleable particles. The synthesized stormwater runoff was made with tap water and the addition of road sediment. The vortex separator has internal components designed to enhance vortex separation by minimizing the turbulence, increasing the efficiency, and preventing captured pollutants from washout. As the flow continues to spiral down around the inlet baffle, a low energy vortex motion directs settleable particles into the protected sediment storage zone. Advanced vortex separation provided an extendable and stabilized flow path while protecting the captured pollutants for a wide range of flow rates. The range of the inflow rate was 30-115 l/m, and the size of the influent particles varied from 75 to 200 µm. Overall removal effi ciencies of 51.8% for SS, 26.6% for COD, 70.5% for TP, and 35.6% for TN were achieved. The efficiency of particle removal for a high inflow rate was better than for a low inflow rate under the same condition of infl uent particles. The particle removal efficiency of the inlet baffle improved by about 5-10% compared to without an inlet baffle. [ABSTRACT FROM AUTHOR]

To manage and control the nonpoint source (NPS) pollution and to improve the water quality of impaired streams, rivers and lakes, practices including constructed wetlands, permeable pavement, swales and others are being attempted in the world. Before applying these techniques, an analysis of stormwater runoff characteristics should be understood due to the complexity in estimating system design factors for best management practices (BMPs). This study investigates the stormwater discharge from an agricultural area in Korea. Based on this investigation, pollutant and fl ow coeffi cient of variation (PFCoV) values were developed in an attempt to explain the stormwater runoff in the agricultural area. Four fi eld studies categorized by rainfall type were then employed to assess the PFCoV values. The results show that the physical meaning of PFCoV values indicates the variation of NPS pollutants during a storm event. As such, this simple and meaningful result can be applied to a wide range of stormwater management designs or water quality controls in agricultural areas. [ABSTRACT FROM AUTHOR]

A four-year monitoring study was conducted to determine the diffuse pollutant unit loads for the six representative transportation landuses (i.e., highway, parking lot, bridge, service area and toll gate) in urban areas in Korea. Pollutant event mean concentrations (EMCs) were calculated from 123 storm events at eleven sites and the average annual rainfall and runoff coefficients were determined to estimate the unit loads using the National Institute of Environmental Research (NIER) method. Apparent differences in the unit load values existed among landuses as explained by the high variability of EMCs for different rainfall events. The study has found that runoff from bridges and highways contained the highest loadings of TSS and COD while service areas are the primary sources of nutrients and metals. The results of this study could be used by the Ministry of Environment (MOE) to separate the unit loads of transportation landuses from the urban area category in the current unit load system. The values obtained are also useful for planning and simulation purposes especially in the total maximum daily load (TMDL) programs and diffuse pollution abatement measures. [ABSTRACT FROM AUTHOR]

Different numerical schemes for the fate and transport models of fecal indicator bacteria (FIB) were used to predict the concentration of FIB in a creek, which were then compared to a steady state model (QUAL2E). Escherichia coli (EC) and enterococci bacteria (ENT) were selected as representative FIB to compare the model performance under different flow and weather conditions in the Gwangju Creek in Korea. The results revealed that model accuracy of the forward time centered space (FTCS) scheme is the highest compared to the upstream, Dufort-Frankel, Crank-Nicolson methods and steady state model (QUAL2E) under dry weather conditions. In wet weather conditions, however, the upstream scheme shows the best performance among the five models. The upstream scheme thus represented a potential method for predicting the fate and transport of FIB originating from nonpoint sources during the rainy season. This study demonstrates that prediction results could vary in response to different numerical schemes and that the amount of discrepancy between the observed and predicted results can be quite signifi cant. We expect that this study could be applied to the water quality forecasting system as a real time management in near future. [ABSTRACT FROM AUTHOR]

As a representative urban stream in Korea, the Gwangju (GJ) stream suffers from chronic fecal contamination. In this study, to characterize levels of fecal pollution in the GJ stream, the monthly monitoring data for seven years (from 2001 to 2007) and the hourly monitoring data from two field experiments were examined with respect to seasonal/daily variations and spatial distribution under wet and dry weather conditions. This research revealed that concentrations of fecal indicator bacteria strongly varied depending on the prevalent meteorological conditions. That is, during the dry daytime, fecal indicator bacteria concentrations decreased due to inactivation from solar irradiation, but rapidly increased in the absence of sunlight, suggesting external source inputs. In addition, bacterial concentrations substantially increased during rainfall events, due probably to a major contribution from combined sewer overflow. The observations in this study can be useful for implementing fecal pollution management strategies and for predicting fecal contamination as a function of meteorological conditions. [ABSTRACT FROM AUTHOR]

Forward osmosis (FO) is a membrane-based desalination technique using an osmotic pressure gradient as a driving force. FO enables lower energy consumption to produce water than reverse osmosis (RO) because it does not require high transmembrane pressure. However, FO needs to be combined with other processes such as RO or evaporation because the draw salts in the FO products should be removed. This paper focused on theoretical investigation of combined systems with FO and RO for seawater desalination. A theoretical model to predict the performance of the combined systems was developed based on the solution-diffusion model and the modified film theory. The effect of internal concentration polarization on FO efficiency was also considered in the model. A pilot-scale system of FO and RO was assumed for model calculations. Results showed that the combinations of FO with seawater reverse osmosis (SWRO) have potential for desalination with high recovery (up to 80%). It was also found that FO may be incorporated with brackish reverse osmosis (BWRO) to produce high quality of water with higher flux and recovery than conventional RO-based desalination systems. [ABSTRACT FROM AUTHOR]

Reverse osmosis (RO) membranes have high rejection for most of solutes in seawater except for boron. Therefore, boron rejection should be considered in the design and operation of the RO process for seawater desalination. In this paper, we investigated boron rejection and its relation to salt rejection using an irreversible thermodynamic model. Permeability constants for commercially-available RO membranes were obtained using theoretical model and the simulation data from membrane performance test program provided by membrane manufacturers. The effect of pH and concentration of the feed water on the boron rejection was also theoretically investigated under various operating conditions. The model calculations revealed that the rejection of boron follows a different mechanism from those of other ionic solutes and could not be readily correlated with ion rejections. To overcome the limit of mechanistic models, we explored an alternative approach for predicting boron permeability from membrane properties and ion permeability. It appears that this alternative approach can aid to achieve a better understanding of boron rejection by seawater RO membranes. [ABSTRACT FROM AUTHOR]

Scale formation of soluble salts is one of the major factors limiting the performance of reverse osmosis (RO) membranes for desalination. However, it is difficult to predict membrane fouling due to scale formation in a complicated feed water containing dissolved organics such as humic substances. This study aims at prediction of the complicated fouling phenomenon by scale formation in the presence of dissolved organic matters. Experimental studies with model solutions were conducted in a small batch filtration device. Humic acid and calcium sulfate were used as model dissolved organic matters and scale-forming salts. A genetic programming technique was applied to predict the effect of dissolved organic matters on scale formation. [ABSTRACT FROM AUTHOR]

A seawater reverse osmosis (SWRO) desalination process has become an alternative technology to solve water shortage problem in the world. Although SWRO is an advanced and useful technology for humanity, there is still unsettled cost effective issue. Therefore, the estimation of operation and maintenance (O&M) costs should be significantly considered before a new construction of SWRO desalination plant. The objective of this feasibility study is to estimate total annual profits (TAP) according to O&M for a new construction of SWRO desalination plant at 9 candidate sites along the shoreline in Korea. The cost estimation model was developed and validated with Fujairah SWRO plant operation data in 2005. As a result, TAPs for the 9 candidate sites were estimated and compared with that of Fujairah SWRO plant. The result reflects that the cost estimation model in this study is able to propose a reasonable range of TAP for a new construction project of SWRO desalination plant in Korea. [ABSTRACT FROM AUTHOR]

To date, over 80 papers on transport modeling and natural organic matter (NOM) relating to sea-water reverse osmosis (SWRO), have been reviewed. As a result of such focus, NOM, one of the main foulants related to reverse osmosis (RO) membranes, has been shown to possess intrinsic chemical complexities and ambiguities, necessitating further investigation. Consequently, since such NOM fouling and transport mechanisms associated with SWRO are not fully understood, a summation of previous studies has been included in the paper in question to systematize information, not only as to RO membrane transport modeling, but NOM fouling characteristics, as well. Accordingly, RO transport models in the review are classified into three categories: diffusion-based, pore, and irreversible thermodynamic models. In addition, specific features, unique assumptions, and applications for each model are examined. The paper consists of the following components towards meaningful understanding of NOM fouling model development during SWRO: 1) SWRO fundamentals as to membranes, 2) NOM fundamentals as to seawater, 3) RO transport modeling theories, 4) conclusion, and 5) future directions of NOM fouling model development. [ABSTRACT FROM AUTHOR]

In the present study, the feasibility of a tri-hybrid desalination system for the production of electricity, energy for heating and cooling and water was studied. The tri-hybrid desalination system is a combination of a cogeneration and a desalination system. The system is composed of a generator driven by a gas engine, a gas boiler, a turbo-refrigerator, an absorption refrigerator for cogeneration, and an RO and an MED for the desalination system. The excess heat was calculated based on the electrical, heating and cooling load data and assumed that would be used for the production of water. The quantitative analysis of the product water and the energy consumption rate was compared on a month-by-month basis, and the feasibility of the tri-hybrid desalination system is explained. [ABSTRACT FROM AUTHOR]

The successful operation of a seawater reverse osmosis desalination plant depends greatly on the performance of the pretreatment step. The start-up of a pretreatment filtration process usually begins with a dynamic part called ripening, during which the filter media increases its adsorption ability and improves the removal of foulants from the seawater. In that period (typically lasting 30-60 min, depending on the system's conditions) the effluent often does not meet the quality requirements of the RO membranes and must often be disposed of. An observation and analysis of the ripening phenomena was performed using lab scale acrylic filter columns containing granular activated carbon. The seawater originated from the Yellow Sea in Korea and an inline coagulation was performed prior to filtration using an optimal dose of FeCl3 based on a jar test. The turbidity, total suspended solids concentration and dissolved organic carbon were measured before and after filtration with the goal of assessing the filtration performance. The filter media, which was cleaned and dried before filtration showed distinct ripening characteristics. The measurements were then used for parameter identification of a typical filtration model. Based on the fitted model one can predict the optimal filter depth that would reduce the waste stream production of the plant. [ABSTRACT FROM AUTHOR]

The model of salt transport through the membrane in multi-component system was developed with the irreversible thermodynamics theory considering the effect of the interaction between the salt ions. With the proposed transport model, the model for spiral-wound module was built based on the mass conservation law and concentration polarization derived by film theory. Through the parameter estimation, the frictional coefficients related to permeability of water and each ion was determined and then phenomenological analysis was conducted. The simulation results show that the salt rejection is increasing as the applied pressure and feed flow rate increasing. Also, the predictions by present model are in good agreement with the experimental data in the literature. [ABSTRACT FROM AUTHOR]

Desalination offers one of the reasonable alternatives as a solution to water scarcity. Among the desalination technologies, seawater reverse osmosis (SWRO) process becomes increasingly attractive for freshwater supply with comparatively low energy consumption and cost-effective operation. In this study, a benchmark of operating Fujairah SWRO desalination plant in the United Arab Emirates is applied to assess the relative performance of a new and large plant construction in other sites. A process model was developed to simulate the performance of SWRO desalination process and to estimate its operating cost. The developed model was validated using one-year SWRO operation data obtained from Fujairah SWRO desalination plant. Temperature and salinity data were collected from World Ocean Atlas 2005, and then applied to the model to estimate the operating cost under the site-specific conditions of 9 different countries. The results of this study present that the performance of the SWRO desalination process was significantly influenced by feed water concentration (i.e., mainly salinity) and temperature, and that the total operating cost is significantly dependent of the local electricity costs. As a conclusion, World Ocean Atlas data and the benchmark provide a method of comparing the performance of various SWRO desalination systems among different countries. Furthermore the methodology is applicable to simulate the process model with site-specific water quality data and to estimate the operating cost before a new construction of SWRO desalination plant at a target area elsewhere. [ABSTRACT FROM AUTHOR]

Recovery and salt rejection rate, which are directly related to the membrane properties and operating conditions (i.e., feed flow rate, pressure, temperature, and TDS concentration), are two main indicators for evaluating the performance of a reverse osmosis (RO) membrane process. A simple and rapid test with minimum data is inevitably requested to diagnose membrane performance at a certain operating conditions, because experiments at all operating conditions are inefficient and consumable work in the view of cost as well as man-hour. In this study, permeate flow rate and TDS concentration of three kinds of commercial RO membranes were carefully examined under various operating conditions [i.e., feed pressure (45-65 kgf/cm2) and temperature (5-30°C)]. Based on the experimental data, membrane resistance models including temperature and pressure correction factors were developed for the rapid diagnosis of SWRO membrane performances. As a result, the models developed in this study have good agreements between observed and simulated data. Based on the procedure in this study, the performance of any type of RO membranes can be rapidly examined by simple model parameter inputs. Furthermore, the developed diagnosis tool for performance test of SWRO membranes can be practically applied to build database of membrane performance for designing the SWRO process with minimum data as well as to reduce the cost and effort for data acquisition. [ABSTRACT FROM AUTHOR]

Seawater reverse osmosis (SWRO) desalination is considered one of the most promising technologies for supplying freshwater to the regions suffering from water scarcity. Although a considerable amount of research has been conducted in attempts to improve SWRO desalination technology, reducing the cost of producing freshwater is still required. In Korea, this effort is already been being carried out by the Seawater Engineering & Architecture of High Efficiency Reverse Osmosis (SeaHERO) project. Core Technology 2 (CT2), one of the most important sub-projects out of four CTs under SeaHERO, pursues two main goals: optimization of processes for low energy consumption/ high efficiency and localization of core parts/equipments of the SWRO plant. Four unit technologies (UTs) are then incorporated into CT2 and each UT has its own technical target; UT1 focuses on the development of SWRO desalination system integration and optimization technology in terms of energy saving; UT2 and 3 work to develop high performance RO membranes and 16-inch modules with polyamide-type materials and novel (i.e., non-polyamide) materials, respectively; UT4 works to develop a high-pressure/massive-capacity pump and hydrostatic energy recovery device for a high efficiency SWRO plant. Accordingly, the integration of the four UTs of CT2 in the SeaHERO project is expected to contribute to reducing the cost and energy consumption in producing freshwater from seawater using the SWRO process. [ABSTRACT FROM AUTHOR]

Water quality response in a reservoir has often been assessed using relatively restricted datasets that cannot provide sufficient information, thereby giving rise to a dramatic over- or underestimate of actual figures. In this paper we discuss how the levels of metallic elements between the sediment and overlying water in an estuarine reservoir can be influenced by aquatic parameters in response to spatial and seasonal conditions. To better elucidate the interfacial exchange between sediment and water, statistical analyses are employed to intensive data sets collected from the Yeongsan Reservoir (YSR), Korea, which has undergone widespread deterioration in water quality due to the continuous growth of anthropogenic sources. During three seasonal sampling campaigns, we found that oxygen deficiency at the bottom water layer promotes Fe and Ni accumulation in sediment, likely due to the formation of sulfide and oxide complexes under anoxic and suboxic environments, respectively. In addition, salinity levels as high as 11‰ in the bottom water layer during autumn substantially increase the release of Mn, restricting the use of YSR as a primary source of agricultural irrigation water. Although most dissolved metals are at acceptable levels for sustaining aquatic life, it is recommended that for long-term planning the elevated Fe and Mn levels in sediment should be controlled with oxygen deficiency during dry weather to ensure a sustainable water supply or, at a minimum, better coordinated operation of YSR. [ABSTRACT FROM AUTHOR]

This paper describes a management scheme to control river water quality using additional water discharges from upstream dams, which results in an increase environmental flow (EF) followed by an enhancement of water quality in a target river. To suggest a creditable management plan among a suite of ideal scenarios, the monthly averaged water quality monitoring data from 2001 to 2006 at the Yeongsan (YS) River, Korea were investigated with respect to seasonal variation and spatial distribution. From the analysis, it was found that while biochemical oxygen demand (BOD5) level in the YS River was extremely high during the dry/drought season (April, May, and June; AMJ), the level was subsequently decreased during the monsoon season (July, August, and September; JAS) due mainly to the dilution effect of rainfall. To improve the water quality in AMJ, we here suggested a scenario of increasing EF using surplus water discharges from upstream dams, which was examined by one dimensional riverine water quality model, QUAL2E model. Simulation result showed that additional discharge from the upstream dams could lead, on average, to a 36% of water quality improvement in mainstream with respect to BOD5. Model coefficients were validated by comparing the six year monitoring data to minimize a sum of squares error, and showed a good agreement with the observed data. Overall, the methodology developed in this paper appears to be quite clear and straightforward, and thus, can be applied to a wide range of the flow managements or water quality controls in a stream with artificial structures. [ABSTRACT FROM AUTHOR]

The Yeongsan (YS) Reservoir is an estuarine reservoir which provides surrounding areas with public goods, such as water supply for agricultural and industrial areas and flood control. Beneficial uses of the YS Reservoir, however, are recently threatened by enriched non-point and point source inputs. A series of multivariate statistical approaches including principal component analysis (PCA) were applied to extract significant characteristics contained in a large suite of water quality data (18 variables monthly recorded for 5 years); thereby to provide the important phenomenal information for establishing effective water resource management plans for the YS Reservoir. The PCA results identified the most important five principal components (PCs), explaining 71% of total variance of the original data set. The five PCs were interpreted as hydrometeorological effect, nitrogen loading, phosphorus loading, primary production of phytoplankton, and fecal indicator bacteria (FIB) loading. Furthermore, hydro-meteorological effect and nitrogen loading could be characterized by a yearly periodicity whereas FIB loading showed an increasing trend with respect to time. The study results presented here might be useful to establish preliminary strategies for abating water quality degradation in the YS Reservoir. [ABSTRACT FROM AUTHOR]

The present study proposes a methodology for determining the effective dispersion coefficient based on the field measurements performed in Gwangju (GJ) Creek in South Korea which is environmentally degraded by the artificial interferences such as weirs and culverts. Many previous works determining the dispersion coefficient were limited in application due to the complexity and artificial interferences in natural stream. Therefore, the sequential combination of N-Tank-In-Series (NTIS) model and Advection-Dispersion-Reaction (ADR) model was proposed for evaluating dispersion process in complex stream channel in this study. The series of water quality data were intensively monitored in the field to determine the effective dispersion coefficient of E. coli in rainy day. As a result, the suggested methodology reasonably estimates the dispersion coefficient for GJ Creek with 1.25m2/s. Also, the sequential combined method provided Number of tank-Velocity-Dispersion coefficient (NVD) curves for convenient evaluation of dispersion coefficient of other rivers or streams. Comparing the previous studies, the present methodology is quite general and simple for determining the effective dispersion coefficients which are applicable for other rivers and streams. [ABSTRACT FROM AUTHOR]