Optimization of the Design Configuration and Operation Strategy of Single-Pass Seawater Reverse Osmosis.

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.

Theoretical Analysis of a Mathematical Relation between Driving Pressures in Membrane-Based Desalting Processes.

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 relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers.

Despite a growing awareness of the problems associated with cyanobacterial blooms in rivers, and particularly in regulated rivers, the drivers of bloom formation and abundance in rivers are not well understood. We developed a Bayesian hierarchical model to assess the relative importance of predictors of summer cyanobacteria abundance, and to test whether the relative importance of each predictor varies by site, using monitoring data from 16 sites in the four major rivers of South Korea. The results suggested that temperature and residence time, but not nutrient levels, are important predictors of summer cyanobacteria abundance in rivers. Although the two predictors were of similar significance across the sites, the residence time was marginally better in accounting for the variation in cyanobacteria abundance. The model with spatial hierarchy demonstrated that temperature played a consistently significant role at all sites, and showed no effect from site-specific factors. In contrast, the importance of residence time varied significantly from site to site. This variation was shown to depend on the trophic state, indicated by the chlorophyll-a and total phosphorus levels. Our results also suggested that the magnitude of weir inflow is a key factor determining the cyanobacteria abundance under baseline conditions.

Modeling spatiotemporal bacterial variability with meteorological and watershed land-use characteristics.

Bacteria are a primary contaminant in natural surface water. The instream concentration of fecal coliform, a potential indicator of pathogens, is influenced by meteorological conditions and land-use characteristics. However, the relationships between these conditions and fecal coliforms are not fully understood. Furthermore, the sources of large variability in fecal coliform counts, e.g., temporal or spatial sources, remain unexplained, especially at large scales. This study proposes the use of Bayesian overdispersed Poisson models, whereby the combined effects of temperature, rainfall, and land-use characteristics on fecal coliform concentration are quantified with predictive uncertainty, and the sources of variability in fecal coliform concentration are assessed. The models were developed using 8-year weekly observations of fecal coliforms obtained from the Wachusett Reservoir watershed in Massachusetts, USA. The results highlight the importance of interactions among meteorological and land-use characteristics in controlling the instream fecal coliform concentration; the increase in fecal coliform concentration with temperature increase was more drastic when rainfall occurred. Also, the responses of fecal coliforms to temperature increases were more pronounced in forest-dominated than in urban-dominated areas. In contrast, the fecal coliform concentration increased more rapidly with rainfall increases in urban-dominated than in forest-dominated areas. The models also demonstrate that among the sources of variability, the monthly component made the most significant contribution to the variability in fecal coliform concentrations. Our results suggest that seasonally dependent processes, including surface runoff, are critical factors that regulate fecal coliform concentration in streams.

Prediction of effluent concentration in a wastewater treatment plant using machine learning models.

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R2), Nash-Sutcliff efficiency (NSE), relative efficiency criteria (drel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.

Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.

Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vector machine (SVM) were used to predict Chl-a concentration for the early warning in the Juam Reservoir and Yeongsan Reservoir, which are located in an upstream region (freshwater reservoir) and downstream region (estuarine reservoir), respectively. Weekly water quality data and meteorological data for a 7-year period were used to train and validate both the ANN and SVM models. The Latin-hypercube one-factor-at-a-time (LH-OAT) method and a pattern search algorithm were applied to perform sensitivity analyses for the input variables and to optimize the parameters of the two models, respectively. Results revealed that the two models well-reproduced the temporal variation of Chl-a based on the weekly input variables. In particular, the SVM model showed better performance than the ANN model, displaying a higher prediction accuracy in the validation step. The Williams-Kloot test and sensitivity analysis demonstrated that the SVM model was superior for predicting Chl-a in terms of prediction accuracy and description of the cause-and-effect relationship between Chl-a concentration and environmental variables in both the Juam Reservoir and Yeongsan Reservoir. Furthermore, a 7-day interval was determined as an efficient early warning interval in the two reservoirs. As such, this study suggested an effective early-warning prediction method for Chl-a concentration and improved the eutrophication management scheme for reservoirs.

Developing a flow control strategy to reduce nutrient load in a reclaimed multi-reservoir system using a 2D hydrodynamic and water quality model.

Blocking the natural bi-directional flow in an estuarine system using an artificial dyke has commonly caused serious water quality problems. In the southwestern part of South Korea, a parallel triple-reservoir system was constructed by blocking the mouth of three different rivers (Yeongsan, Okcheon, and Kumja), which were then interconnected using two open channels. This system has experienced a deterioration in water quality due to pollutants accumulated from the upper watershed, and has continually discharged pollutant loads to the outer ocean. Therefore, the objective of this study is to establish an effective dam operation plan for reducing nutrient loads released from the integrated reservoir. In this study, the CE-QUAL-W2 model, which is a 2-dimentional hydrodynamic and water quality model, was applied to predict the pollutant load released from each reservoir in response to different flow scenarios for the interconnecting channel. The model was calibrated using two novel methods: a sensitivity analysis to determine meaningful model parameters, and a pattern search to optimize the parameters. From the scenario analysis using flow control, it was determined that the total nitrogen (TN) and total phosphorus (TP) loadings could be reduced by 27.2% and 6.6%, respectively, under the optimal channel flow scenario by regulating the chlorophyll-a concentration in the reservoir. The results confirm that effective dam operation could contribute to a decrease in pollutant loads in the receiving seawater body. As such, this study suggests operational strategies for a multi-reservoir system that can be used to reduce the nutrient load being discharged from reservoirs.

Multi-level stressor analysis from the DNA/biochemical level to community levels in an urban stream and integrative health response (IHR) assessments.

The objectives of this study were to identify multi-level stressors at the DNA/biochemical level to the community level in fish in an urban stream and to develop an integrative health response (IHR) model for ecological health diagnosis. A pristine control site (S (c) ) and an impacted site (S (i) ) were selected from among seven pre-screened sites studied over seven years. Various chemical analyses indicated that nutrient enrichment (Nitrogen, Phosphorus) and organic pollution were significantly greater (t > 8.783, p < 0.01) at the S (i) site compared to the S (c) site. Single-cell gel electrophoresis (comet assays) of DNA-level impairment indicated significantly (t = 5.678, p < 0.01) greater tail intensity, expressed as % tail-DNA, at the S (i) site and genotoxic responses were detected in the downstream reach. Ethoxyresorufin-O-deethylase (EROD) assays, as a physiological bioindicator, were 2.8-fold higher (p < 0.05, NK-test after ANOVA) at the S (i) site. Tissue analysis using a necropsy-based health assessment index (NHAI) showed distinct internal organ disorders in three tissues, i.e., liver, kidney, and gill, at the S (i) site. Population-level analysis using the sentinel species Zacco platypus showed that the regression coefficient (b) was 3.012 for the S (i) site and 2.915 for the S (c) site, indicating population skewness in the downstream reach. Community-level health was impaired at the S (i) site based on an index of biological integrity (IBI), and physical habitat modifications were identified by a qualitative habitat evaluation index (QHEI). Overall, the model values for the integrative health response (IHR), developed using the star plot approach, were 3.22 (80.5%) at the S (c) site and 0.74 (18.5%) at the S (i) site, indicating that, overall, ecological health impairments were evident in the urban reach. Our study was based on multi-level approaches using biological organization and the results suggest that there is a pivotal point of linkage between mechanistic understanding and real ecological consequences of environmental stressors.

The modified SWAT model for predicting fecal coliforms in the Wachusett Reservoir Watershed, USA.

This study assessed fecal coliform contamination in the Wachusett Reservoir Watershed in Massachusetts, USA using Soil and Water Assessment Tool (SWAT) because bacteria are one of the major water quality parameters of concern. The bacteria subroutine in SWAT, considering in-stream bacteria die-off only, was modified in this study to include solar radiation-associated die-off and the contribution of wildlife. The result of sensitivity analysis demonstrates that solar radiation is one of the most significant fate factors of fecal coliform. A water temperature-associated function to represent the contribution of beaver activity in the watershed to fecal contamination improved prediction accuracy. The modified SWAT model provides an improved estimate of bacteria from the watershed. Our approach will be useful for simulating bacterial concentrations to provide predictive and reliable information of fecal contamination thus facilitating the implementation of effective watershed management.

Prevalence of season-specific Escherichia coli strains in the Yeongsan River Basin of South Korea.

Seasonal and spatial variation in the genotypic richness of 3480 Escherichia coli isolates obtained from the Yeongsan River basin in South Korea was investigated by using the horizontal fluorophore-enhanced rep-PCR (HFERP) DNA fingerprinting technique. The relationship between 60 E. coli isolates from each of 58 freshwater samples was determined by using multidimensional scaling (MDS) analysis and self-organized maps (SOMs). The MDS analysis, done based on HFERP DNA fingerprints, showed that E. coli isolates obtained in October through December clustered tightly, while those obtained in other sampling periods were more genetically diverse. However, site-specific E. coli genotypes were not observed. SOMs analysis, done using the 10 most frequently isolated E. coli genotypes, showed the occurrence of season-specific E. coli genotypes and the main SOMs clusters were most influenced by temperature, strain diversity and biochemical oxygen demand. Diversity among E. coli genotypes tended to decrease as water temperature decreased, and the numbers of E. coli genotypes observed in urban area were greater, more diverse and less dependent on water temperature than those obtained from agricultural areas. Taken together, our findings indicate that that an ecological approach needs to be considered in order to obtain a better understanding of E. coli community dynamics in the environment and that SOMs analysis is useful to visualize the multidimensional dependent variables that are influencing the types and dynamics of specific E. coli genotypes in the environment.

Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network.

The arsenic (As) contamination of groundwater has increasingly been recognized as a major global issue of concern. As groundwater resources are one of most important freshwater sources for water supplies in Southeast Asian countries, it is important to investigate the spatial distribution of As contamination and evaluate the health risk of As for these countries. The detection of As contamination in groundwater resources, however, can create a substantial labor and cost burden for Southeast Asian countries. Therefore, modeling approaches for As concentration using conventional on-site measurement data can be an alternative to quantify the As contamination. The objective of this study is to evaluate the predictive performance of four different models; specifically, multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), and the combination of principal components and an artificial neural network (PC-ANN) in the prediction of As concentration, and to provide assessment tools for Southeast Asian countries including Cambodia, Laos, and Thailand. The modeling results show that the prediction accuracy of PC-ANN (Nash-Sutcliffe model efficiency coefficients: 0.98 (traning step) and 0.71 (validation step)) is superior among the four different models. This finding can be explained by the fact that the PC-ANN not only solves the problem of collinearity of input variables, but also reflects the presence of high variability in observed As concentrations. We expect that the model developed in this work can be used to predict As concentrations using conventional water quality data obtained from on-site measurements, and can further provide reliable and predictive information for public health management policies.

Advancing assessment and design of stormwater monitoring programs using a self-organizing map: characterization of trace metal concentration profiles in stormwater runoff.

Stormwater runoff poses a great challenge to the scientific assessment of the effects of diffuse pollution sources on receiving waters. In this study, a self-organizing map (SOM), a research tool for analyzing specific patterns in a large array of data, was applied to the monitoring data obtained from a stormwater monitoring survey to acquire new insights into stream water quality profiles under different rainfall conditions. The components of the input data vectors used by the SOM included concentrations of 10 metal elements, river discharge, and rainfall amount which were collected at the inlet and endpoint of an urban segment of the Yeongsan River, Korea. From the study, it was found that the SOM displayed significant variability in trace metal concentrations for different monitoring sites and rainfall events, with a greater impact of stormwater runoff on stream water quality at the upstream site than at the downstream site, except under low rainfall conditions (≤ 4 mm). In addition, the SOM clearly determined the water quality characteristics for "non-storm" and "storm" data, where the parameters nickel and arsenic and the parameters chromium, cadmium, and lead played an important role in reflecting the spatial and temporal water quality, respectively. When the SOM was used to examine the efficacy of stormwater quality monitoring programs, between 34 and 64% of the sample size in the current data set was shown to be sufficient for estimating the stormwater pollutant loads. The observed errors were small, generally being below 10, 6, and 20% for load estimation, map resolution, and clustering accuracy, respectively. Thus, the method recommended may be used to minimize monitoring costs if both the efficiency and accuracy are further determined by examining a large existing data set.

Evaluation of the relationship between two different methods for enumeration fecal indicator bacteria: colony-forming unit and most probable number.

Most probable number (MPN) and colony-forming unit (CFU) estimates of fecal indicator bacteria (FIB) concentration are common measures of water quality in aquatic environments. Thus, FIB intensively monitored in Yeongsan Watershed in an attempt to compare two different methods and to develop a statistical model to convert from CFU to MPN estimates or vice versa. As a result, the significant difference was found in the MPN and CFU estimates. The enumerated Escherichia coli concentrations in MPN are greater than those in CFU, except for the measurement in winter. Especially in fall, E. coli concentrations in MPN are one order of magnitude greater than that in CFU. Contrarily, enterococci bacteria in MPN are lower than those in CFU. However, in general, a strongly positive relationship are found between MPN and CFU estimates. Therefore, the statistical models were developed, and showed the reasonable converting FIB concentrations from CFU estimates to MPN estimates. We expect this study will provide preliminary information towards future research on whether different analysis methods may result in different water quality standard violation frequencies for the same water sample.

Use of barcoded pyrosequencing and shared OTUs to determine sources of fecal bacteria in watersheds.

While many current microbial source tracking (MST) methods rely on the use of specific molecular marker genes to identify sources of fecal contamination, these methods often fail to determine all point and nonpoint contributors of fecal inputs into waterways. In this study, we developed a new library-dependent MST method that uses pyrosequencing-derived shared operational taxonomy units (OTUs) to define sources of fecal contamination in waterways. A total 56,841 pyrosequencing reads of 16S rDNA obtained from the feces of humans and animals were evaluated and used to compare fecal microbial diversity in three freshwater samples obtained from the Yeongsan river basin in Jeonnam Province, South Korea. Sites included an urbanized agricultural area (Y1) (Escherichia coli counts ≥ 1600 CFU/100 mL), an open area (Y2) with no major industrial activities (940 CFU/100 mL), and a typical agricultural area (Y3) (≥ 1600 CFU/100 mL). Data analyses indicated that the majority of bacteria in the feces of humans and domesticated animals were comprised of members of the phyla Bacteroidetes or Firmicutes, whereas the majority of bacteria in wild goose feces and freshwater samples were classified to the phylum Proteobacteria. Analysis of OTUs shared between the fecal and environmental samples suggested that the potential sources of the fecal contamination at the sites were of human and swine origin. Quantification of fecal contamination was also examined by comparing the density of pyrosequencing reads in each fecal sample within shared OTUs. Taken together, our results indicated that analysis of shared OTUs derived from barcoded pyrosequencing reads provide the necessary resolution and discrimination to be useful as a next generation platform for microbial source tracking studies.

Linking land-use type and stream water quality using spatial data of fecal indicator bacteria and heavy metals in the Yeongsan river basin.

This study reveals land-use factors that explain stream water quality during wet and dry weather conditions in a large river basin using two different linear models-multiple linear regression (MLR) models and constrained least squares (CLS) models. Six land-use types and three topographical parameters (size, slope, and permeability) of the watershed were incorporated into the models as explanatory variables. The suggested models were then demonstrated using a digitized elevation map in conjunction with the land-use and the measured concentration data for Escherichia coli (EC), Enterococci bacteria (ENT), and six heavy metal species collected monthly during 2007-2008 at 50 monitoring sites in the Yeongsan Watershed, Korea. The results showed that the MLR models can be a powerful tool for predicting the average concentrations of pollutants in stream water (the Nash-Sutcliffe (NS) model efficiency coefficients ranged from 0.67 to 0.95). On the other hand, the CLS models, with moderately good prediction performance (the NS coefficients ranged 0.28-0.85), were more suitable for quantifying contributions of respective land-uses to the stream water quality. The CLS models suggested that industrial and urban land-uses are major contributors to the stream concentrations of EC and ENT, whereas agricultural, industrial, and mining areas were significant sources of many heavy metal species. In addition, the slope, size, and permeability of the watershed were found to be important factors determining the extent of the contribution from each land-use type to the stream water quality. The models proposed in this paper can be considered useful tools for developing land cover guidelines and for prioritizing locations for implementing management practices to maintain stream water quality standard in a large river basin.

High diversity and abundance of antibiotic-resistant Escherichia coli isolated from humans and farm animal hosts in Jeonnam Province, South Korea.

The spread of antibiotics resistance among bacteria is a threat to human health. Since South Korea uses approximately 1.5 times more antibiotics than do other OECD countries, this is likely to impact the numbers and types of antibiotic-resistant bacteria found in the environment. In this study we examined feces from domesticated animals and humans for the diversity and abundance of antibiotic-resistant Escherichia coli. Abundant antibiotic-resistant E. coli were isolated from all the tested animals and humans and were examined by horizontal, fluorophore-enhanced, rep-PCR (HFERP) DNA fingerprint analysis. A total of 793 unique, non-clonal, E. coli isolates were obtained from the 513 human and animal hosts examined. Antibiotic resistance analysis, done using 14 antibiotics, indicated that 72.3% of the isolates (573 of 793) were found resistant to more than one antibiotic. The E. coli isolated from swine were resistant to the greatest number of antibiotics. Tetracycline resistant E. coli were routinely isolated from all animal hosts (36 to 77% per host), except for dairy cattle (9.3%). Twenty nine E. coli isolates from all hosts, except for duck, were resistant to more than 10 antibiotics. Gene transfer and southern hybridization studies revealed that resistance to 13 of the antibiotics was self-transmissible, and likely mediated by plasmids and integrons. Since genetically diverse and numerically abundant antibiotic-resistant E. coli were consistently recovered from chicken, swine and other domesticated animals in South Korea, our results suggest that the use of sub-therapeutic levels of antibiotics for disease prophylaxis and growth promotion should be curtailed.

Spatial and temporal variability of fecal indicator bacteria in an urban stream under different meteorological regimes.

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.

Meteorological effects on the levels of fecal indicator bacteria in an urban stream: a modeling approach.

Gwangju Creek (GJC) in Korea, which drains a highly urbanized watershed, has suffered from substantial fecal contamination, thereby limiting the beneficial use of the water in addition to threatening public health. In this study, to quantitatively estimate the sinks and sources of fecal indicator bacteria (FIB) in GJC under varying meteorological conditions, two FIB (i.e., Escherichia coli and enterococci bacteria) were monitored hourly for 24h periods during both wet and dry weather conditions at four sites along GJC, and the collected data was subsequently used to develop a spatiotemporal FIB prediction model. The monitoring data revealed that storm washoff and irradiational die-off by sunlight are the two key processes controlling FIB populations in wet and dry weather, respectively. FIB populations significantly increased during precipitation, with greater concentrations occurring at higher rainfall intensity. During dry weather, FIB populations decreased in the presence of sunlight in daytime but quickly recovered at nighttime due to continuous point-source inputs. In this way, the contributions of the key processes (i.e., irradiational die-off by sunlight, settling, storm washoff, and resuspension) to the FIB levels in GJC under different meteorological conditions were quantitatively estimated using the developed model. The modeling results showed that the die-off by sunlight is the major sink of FIB during the daytime in dry weather with a minor contribution from the settling process. During wet weather, storm washoff and resuspension are equally important processes that are responsible for the substantial increase of FIB populations.

Factors dominating stratification cycle and seasonal water quality variation in a Korean estuarine reservoir.

A comprehensive monitoring program was conducted during 2005-2007 to investigate seasonal variations of hydrologic stability and water quality in the Yeongsan Reservoir (YSR), located at the downstream end of the Yeongsan River, Korea. A principal component analysis (PCA) was performed to identify factors dominating the seasonal water quality variation from a large suite of measured data--11 physico-chemical parameters from 48 sampling sites. The results showed that three principal components explained approximately 62% of spatio-seasonal water quality variation, which are related to stratifications, pollutant loadings and resultant eutrophication, and the advective mixing process during the episodic rainfall-runoff events. A comparison was then made between YSR and an upstream freshwater reservoir (Damyang Reservoir, DYR) in the same river basin during an autumn season. It was found that the saline stratification and pollutant input from the upstream contributed to greater concentrations of nutrients and organic matter in YSR compared to DYR. In YSR, saline stratification in combination with thermal stratification was a dominant cause of the longer period (for two consecutive seasons) of hypoxic conditions at the reservoir bottom. The results presented here will help better understand the season- and geography-dependent characteristics of reservoir water quality in Asian Monsoon climate regions such as Korea.

Factors affecting metal exchange between sediment and water in an estuarine reservoir: a spatial and seasonal observation.

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 per thousand 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.