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Ecological risk assessment of contaminated sediment has become a fundamental component of water quality management programs, supporting decision-making for management actions or prompting additional investigations. In this study, we proposed a machine learning (ML)-based approach to assess the ecological risk of contaminated sediment as an alternative to existing index-based methods and costly toxicity testing. The performance of three widely used index-based methods (the pollution load index, potential ecological risk index, and mean probable effect concentration) and three ML algorithms (random forest, support vector machine, and extreme gradient boosting [XGB]) were compared in their prediction of sediment toxicity using 327 nationwide data sets from Korea consisting of 14 sediment quality parameters and sediment toxicity testing data. We also compared the performances of classifiers and regressors in predicting the toxicity for each of RF, SVM, and XGB algorithms. For all algorithms, the classifiers poorly classified toxic and non-toxic samples due to limited information on the sediment composition and the small training dataset. The regressors with a given classification threshold provided better classification, with the XGB regressor outperforming the other models in the classification. A permutation feature importance analysis revealed that Cr, Cu, Pb, and Zn were major contributors to toxicity prediction. The ML-based approach has the potential to be even more useful in the future with the expected increase in available sediment data.

Many countries have attempted to monitor and predict harmful algal blooms to mitigate related problems and establish management practices. The current alert system-based sampling of cell density is used to intimate the bloom status and to inform rapid and adequate response from water-associated organizations. The objective of this study was to develop an early warning system for cyanobacterial blooms to allow for efficient decision making prior to the occurrence of algal blooms and to guide preemptive actions regarding management practices. In this study, two machine learning models: artificial neural network (ANN) and support vector machine (SVM), were constructed for the timely prediction of alert levels of algal bloom using eight years’ worth of meteorological, hydrodynamic, and water quality data in a reservoir where harmful cyanobacterial blooms frequently occur during summer. However, the proportion imbalance on all alert level data as the output variable leads to biased training of the data-driven model and degradation of model prediction performance. Therefore, the synthetic data generated by an adaptive synthetic (ADASYN) sampling method were used to resolve the imbalance of minority class data in the original data and to improve the prediction performance of the models. The results showed that the overall prediction performance yielded by the caution level (L1) and warning level (L2) in the models constructed using a combination of original and synthetic data was higher than the models constructed using original data only. In particular, the optimal ANN and SVM constructed using a combination of original and synthetic data during both training (including validation) and test generated distinctively improved recall and precision values of L1, which is a very critical alert level as it indicates a transition status from normalcy to bloom formation. In addition, both optimal models constructed using synthetic-added data exhibited improvement in recall and precision by more than 33.7% while predicting L -1 and L -2 during the test. Therefore, the application of synthetic data can improve detection performance of machine learning models by solving the imbalance of observed data. Reliable prediction by the improved models can be used to aid the design of management practices to mitigate algal blooms within a reservoir.

We examined the relationship between downstream algal growth potential and the spatial environmental factors of both upland areas and stream buffer zones using spatial analysis and generalized additive models (GAMs). The models employed site-representative concentrations of chlorophyll a (Chl-a) from a total of 688 national water quality monitoring stations and the spatial factors of the corresponding 688 watersheds. The spatial environmental factors included topography, climate, land use class, soil type, and proximity of the monitoring station to the weir downstream and wastewater treatment plants (WWTPs). The explanatory power (adjusted R2 or Radj2) of the models was used to compare different spatial influential scales defined by stream buffers and upstream circular buffers. The spatial environmental factors of the entire watershed area better explained the inter-station variation in Chl-a than did those of the stream buffer and/or upstream circular buffer areas. However, the spatial environmental factors of watershed areas more than 25 km upstream circular buffer zones had only minor influence on the explainability of the models with regards to the inter-station variation in Chl-a levels. Generally, land use patterns were more strongly related to the inter-station Chl-a variation than were point sources of pollutants such as WWTPs. The two most influencing land uses on the inter-station Chl-a variation were urban and agricultural land uses, with varying relative contributions depending on the spatial influential scale: In general relative contribution of urban land use was larger at a larger spatial influential scale while that of agricultural land use showed an opposite trend. In addition, the proximity to the weir downstream explained high Chl-a concentrations in the stream water. Relative importance and causal effects of the spatial environmental variables to instream Chl-a were established based on this national scale correlative analysis, leading to decision-making with the goal of controlling instream algal growth.

Photocatalytic nanoparticles have been receiving considerable attention for their potential use in many environmental management applications, including urban air quality control. This paper investigates the performance of surface modified WO3/TiO2 composite particles in removing gaseous nitrogen oxides (NOx) under visible light irradiation. The WO3/TiO2 composite particles were synthesized using a modified wet chemical method with different concentrations of NaOH solution used as a surface modification agent for the host TiO2 particles. The NOx removal efficiency of the WO3/TiO2 particles was evaluated using a lab-scale continuous gas flow photo-reactor with a gas contact time of 1 min. Results showed that surface modification using NaOH can enhance the photocatalytic activity of the WO3/TiO2 particles. The NOx removal efficiency of the surface modified WO3/TiO2 was greater than 90%, while that of WO3/TiO2 particles prepared by the conventional wet chemical method was ∼75%. The enhanced removal efficiency might be attributed to the formation of oxygen vacancies on the TiO2 surface, providing sites for WO3 particles to effectively bind with TiO2. However, excess amount of NaOH >3 M deteriorated the photocatalytic performance due to the increased agglomeration of the host TiO2 particles.

Although norovirus outbreaks are well-recognized to have strong winter seasonality relevant to low temperature and humidity, the role of artificial human-made features within geographical areas in norovirus outbreaks has rarely been studied. The aim of this study is to assess the natural and human-made environmental factors favoring the occurrence of norovirus outbreaks using nationwide surveillance data. We used a geographic information system and binary response models to examine whether the norovirus outbreaks are spatially patterned and whether these patterns are associated with specific environmental variables including service levels of water supply and sanitation systems and land-use types. The results showed that small-scale low-tech local sewage treatment plants and winter sports areas were statistically significant factors favoring norovirus outbreaks. Compactness of the land development also affected the occurrence of norovirus outbreaks; transportation, water, and forest land-uses were less favored for effective transmission of norovirus, while commercial areas were associated with an increased rate of norovirus outbreaks. We observed associations of norovirus outbreaks with various outcomes of human activities, including discharge of poorly treated sewage, overcrowding of people during winter season, and compactness of land development, which might help prioritize target regions and strategies for the management of norovirus outbreaks.

Stormwater treatment strategies were evaluated for the upper Ballona Creek Watershed in Los Angeles, CA using an empirical model of stormwater runoff quantity and quality with zeroth-order regularization and a limited memory Broyden-Fletcher-Goldfarb-Shanno Bound constrained optimization routine. The model used landuse based estimation on the runoff volume, event mean concentration (EMC) and pollutant load employing ten different landuses, including highways and local roads. The model was validated by showing that its predictions were in reasonable agreement (r2 ~0.6 to 0.8) with total zinc (Zn), Total Kjeldahl Nitrogen (TKN), and Total Suspended Solids (TSS) loadings measured at the monitoring site at the bottom of the watershed. The developed model was used to demonstrate and quantify the benefits of the stormwater treatment practices (STPs) prioritized at specific landuses with high pollutant mass emission rates. For this demonstration, total Zn was selected as it is one of the most concerning pollutants in an extremely urbanized area such as the Ballona Creek Watershed. Transportation landuse including local roads and highways was found to be the best candidate for the STP applications due to their high percent load contribution per percent area. By focusing STPs for transportation landuse, the water quality goal of total Zn in the study watershed was expected be achieved at approximately 75% less cost.

While the urban runoff are increasingly being studied as a source of fecal indicator bacteria (FIB), less is known about the occurrence of FIB in watershed with mixed land use and ongoing land use and land cover (LULC) change. In this study, Escherichia coli (EC) and fecal streptococcus (FS) were monitored from 2012 to 2013 in agricultural, mixed and urban LULC and analyzed according to the most probable number (MPN). Pearson correlation was used to determine the relationship between FIB and environmental parameters (physicochemical and hydrometeorological). Multiple linear regressions (MLR) were used to identify the significant parameters that affect the FIB concentrations and to predict the response of FIB in LULC change. Overall, the FIB concentrations were higher in urban LULC (EC=3.33-7.39; FS=3.30-7.36log10MPN/100mL) possibly because of runoff from commercial market and 100% impervious cover (IC). Also, during early-summer season; this reflects a greater persistence and growth rate of FIB in a warmer environment. During intra-event, however, the FIB concentrations varied according to site condition. Anthropogenic activities and IC influenced the correlation between the FIB concentrations and environmental parameters. Stormwater temperature (TEMP), turbidity, and TSS positively correlated with the FIB concentrations (p>0.01), since IC increased, implying an accumulation of bacterial sources in urban activities. TEMP, BOD5, turbidity, TSS, and antecedent dry days (ADD) were the most significant explanatory variables for FIB as determined in MLR, possibly because they promoted the FIB growth and survival. The model confirmed the FIB concentrations: EC (R(2)=0.71-0.85; NSE=0.72-0.86) and FS (R(2)=0.65-0.83; NSE=0.66-0.84) are predicted to increase due to urbanization. Therefore, these findings will help in stormwater monitoring strategies, designing the best management practice for FIB removal and as input data for stormwater models.

While identification of critical pollutant sources is the key initial step for cost-effective runoff management, it is challenging due to the highly uncertain nature of runoff pollution, especially during a storm event. To identify critical sources and their quantitative contributions to runoff pollution (especially focusing on phosphorous), two ordination methods were used in this study: principal component analysis (PCA) and positive matrix factorization (PMF). For the ordination analyses, we used runoff quality data for 14 storm events, including data for phosphorus, 11 heavy metal species, and eight ionic species measured at the outlets of subcatchments with different land use compositions in a mixed land use watershed. Five factors as sources of runoff pollutants were identified by PCA: agrochemicals, groundwater, native soils, domestic sewage, and urban sources (building materials and automotive activities). PMF identified similar factors to those identified by PCA, with more detailed source mechanisms for groundwater (i.e., nitrate leaching and cation exchange) and urban sources (vehicle components/motor oils/building materials and vehicle exhausts), confirming the sources identified by PCA. PMF was further used to quantify contributions of the identified sources to the water quality. Based on the results, agrochemicals and automotive activities were the two dominant and ubiquitous phosphorus sources (39-61 and 16-47 %, respectively) in the study area, regardless of land use types.

A number of severe norovirus outbreaks due to the consumption of contaminated shellfish have been reported recently. In this study, we evaluated the distribution of coliphage densities to determine their efficacy as fecal indicators of enteric viruses, including noroviruses, in water samples collected from a shellfish growing area in Republic of Korea over a period of approximately 1 year. Male-specific and somatic coliphages in water samples were analyzed using the single agar layer method, and norovirus genogroups I and II, which infect mainly humans, were analyzed using duplex reverse transcription quantitative PCR. Male-specific and somatic coliphages were detected widely throughout the study area. Several environmental parameters, including salinity, precipitation, temperature, and wind speed were significantly correlated with coliphage concentrations (P

The diffuse phosphorus (P) load to receiving waters has been of great concern because it is considered the main cause of eutrophication and algal bloom in natural waters. For establishing effective P control strategy, it is the important first step to obtain information on characteristics of diffuse source of P and its discharge behavior during storm events. In this study, wet and dry sampling collection were performed to measure concentrations of P in different types (particulate, dissolved, and soluble reactive P for waters and adsorbed, non-apatite, apatite, and residual P for soil/sediment) in storm water runoff and soil/sediment on the catchment surface from different land uses. The results showed that urban land uses can be the most significant contributor to diffuse P loading because of the high concentration of P in the storm water run-off. Despite minimal wet discharge concentration of P observed, agricultural land-use can be a potentially important P source due to the relatively greater ...

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.

This study examined the accuracy of an urban stormwater monitoring program in estimating the annual discharge load (L(T)) and the annual reduction rate by a stormwater treatment device (R(T)) for total suspended solids. A calibrated stormwater management model was used to generate the entire stormwater runoff events in one year and was used to estimate L(T) and R(T) under different monitoring strategies having limited numbers of runoff events, including random, wet season, antecedent dry days (ADD)-based, monthly, and seasonally weighted. For random monitoring, 12 storms were required to estimate the values of L(T) and R(T) with mean relative errors of 13.98 and 0.24%, respectively. Monthly monitoring had slightly greater mean relative errors compared to random monitoring. Wet season and ADD-based monitoring under- or overestimated both L(T) and R(T). Monitoring with equal numbers of storms from the wet and dry seasons best estimated L(T) and R(T).

This research investigated the performance of a constructed wetland (CW) that functions as a post-treatment unit for the secondary effluent of a piggery wastewater treatment facility. The pollutant mass removal efficiency was evaluated from 37 sampling events on non-rainy days during 2008–2010. Based on the findings, the pollutant concentrations decreased somewhat along the cells from the influent to the effluent while DO and pH increased along the cells and appeared to peak at the deep marsh region during the spring season. The overall cumulative treatment efficiencies for the entire monitoring period were 53% for total suspended solids; 35–37% for biological oxygen demand and chemical oxygen demand; 33% for total phosphorous; and 17–21% for total nitrogen and other nitrogen forms. The main reason for the low treatment performance was attributed to the low carbon to nutrient ratio (i.e., the COD/BOD:TN:TP ratio) in the CW influent. Moreover, algal bloom was frequently observed in the deep marsh ...

The objective of this study was to present and evaluate the rate of biomass growth due to temperature changes and to identify the type of vegetation that best adapts to temperature changes in a surface flow constructed wetland (CW). The operation of the CW started in 2009 treating stream agricultural runoff from a watershed area of 221 ha which are mainly wet paddy fields. The CW is composed of seven connected cells which include a sedimentation zone, deep and shallow marshes. Five typical wetland plant species such as Acorus calamus, Oenanthe javanica, Phragmites australis, Phragmites japonica, and Typha orientalis were planted surrounding the CW. The water quality and plant biomass were monitored from April to December 2009. The mean inflow DO and pH concentration were 6.9 ± 2.0 mg/l and 7.0 ± 0.5, respectively during the whole season. The water and air temperature at the CW were not significantly different (Ta = 0.86Taw; n = 15; r = 0.91; p

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.

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 24 h 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.

Statistical regression models involve linear equations, which often lead to significant prediction errors due to poor statistical stability and accuracy. This concern arises from multicollinearity in the models, which may drastically affect model performance in terms of a trade-off scenario for effective water resource management logistics. In this paper, we propose a new methodology for improving the statistical stability and accuracy of regression models, and then show how to cope with pitfalls in the models and determine optimal parameters with a decreased number of predictive variables. Here, a comparison of the predictive performance was made using four types of multiple linear regression (MLR) and principal component regression (PCR) models in the prediction of chlorophyll-a (chl-a) concentration in the Yeongsan (YS) Reservoir, Korea, an estuarine reservoir that historically suffers from high levels of nutrient input. During a 3-year water quality monitoring period, results showed that PCRs could be a compact solution for improving the accuracy of the models, as in each case MLR could not accurately produce reliable predictions due to a persistent collinearity problem. Furthermore, based on R2 (goodness of fit) and F-overall number (confidence of regression), and the number of explanatory variables (R-F-N) curve, it was revealed that PCR-F(7) was the best model among the four regression models in predicting chl-a, having the fewest explanatory variables (seven) and the lowest uncertainty. Seven PCs were identified as significant variables, related to eight water quality parameters: pH, 5-day biochemical oxygen demand, total coliform, fecal indicator bacteria, chemical oxygen demand, ammonia–nitrogen, total nitrogen, and dissolved oxygen. Overall, the results not only demonstrated that the models employed successfully simulated chl-a in a reservoir in both the test and validation periods, but also suggested that the optimal parameters should cautiously be considered in the design of regression models.

Simulations were performed using a one-dimensional kinematic wave model to examine the relationships between the time of concentration (T(c)) and the first flush (FF) for impervious landuses. The model used chemical oxygen demand parameters from a highway runoff site in west Los Angeles for eight storm events. FF indices such as the mass first flush ratio (MFF(n)) and the partial event mean concentration to event mean concentration ratio (PEMC(t)/EMC) were calculated from simulations using various rainfall intensities, rainfall durations, bed slopes, and watershed lengths (L), and matched measured values with an RMS error of 25% for six events. Resulting values of MFF(20) and PEMC(60)/EMC were correlated to T(c) calculated using ASCE's kinematic wave formula, and showed a non-monotone relationship, with very short or long T(c) having low MFF(20) with a maximum for mid-range T(c). Contours of the MFF(20) and PEMC(60)/EMC were created to predict and quantify FF from known T(c), L, and rainfall duration (T).

Antibiotic-resistant E. coli concentrations showed large spatial and temporal variations, with greater concentrations observed in tributaries and downstream than in the upstream and midstream. Twenty percent of the geometric mean concentrations of antibiotic-resistant E. coli in the Tama River basin (Japan) exceeded the maximum acceptable concentration of indicator E. coli established by the USEPA. The indicator E. coli concentrations were positively correlated with those of antibiotic-resistant E. coli and multiple-antibiotic-resistant E. coli (resistance to more than two kinds of antibiotics), respectively, but not the detection rate of antibiotic-resistant E. coli, implying that use of antibiotic-resistant E. coli concentration rather than the detection rate can be a better approach for water quality assessment. Multiple-antibiotic-resistant E. coli is a useful indicator for estimating the resistance diffusion, water quality degradation and public health risk potential. This assessment provides beneficial information for setting national regulatory or environmental standards and managing integrated watershed areas.

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.

Accurate simulation of the surf zone is a prerequisite to improve beach management as well as to understand the fundamentals of fate and transport of contaminants. In the present study, a diagnostic model modified from a classic solute model is provided to illuminate non-conservative pollutants behavior in the surf zone. To readily understand controlling processes in the surf zone, a new dimensionless quantity is employed with index of kappa number (K, a ratio of inactivation rate to transport rate of microbial pollutant in the surf zone), which was then evaluated under different environmental frames during a week simulation period. The sensitivity analysis showed that hydrodynamics and concentration gradients in the surf zone mostly depend on n (number of rip currents), indicating that n should be carefully adjusted in the model. The simulation results reveal, furthermore, that large deviation typically occurs in the daytime, signifying inactivation of fecal indicator bacteria is the main process to control surf zone water quality during the day. Overall, the analytical model shows a good agreement between predicted and synthetic data (R2=0.51 and 0.67 for FC and ENT, respectively) for the simulated period, amplifying its potential use in the surf zone modelling. It is recommended that when the dimensionless index is much larger than 0.5, the present modified model can predict better than the conventional model, but if index is smaller than 0.5, the conventional model is more efficient with respect to time and cost.

Comprehensive water quality monitoring was conducted to assess the water quality conditions and to determine the impact of urban infrastructure on ambient water quality in Angkor, Cambodia. During this study, surface water, groundwater, and sediment samples were collected for two distinctive seasons in 2006–2007 at 58 monitoring sites along and near the Siem Reap River, in Tole Sap Lake (TSL), and West Baray, the primary water resources in this region. To assess the seasonal and spatial variability of 27 water quality parameters, multivariate analysis of variance, hierarchical cluster analysis, and the Kruskal-Wallis test were conducted using the obtained data. Differences and relationships between the surface water and groundwater were also investigated using t-test and correlation analysis, respectively. The results of these tests showed that the bacterial indicators need special attention as the urban infrastructure of the downtown area caused increased levels of these bacterial indicators in both surface water and groundwater. However, for most parameters, though surface water showed strong seasonal variations, groundwater presented relatively stable conditions between seasons (p>0.05) with site-specific geochemical conditions. Sediment quality illustrated that pollution levels of 10 trace metals were the highest in TSL because of its unique characteristic (river with backward flow), but did not reflect any potential enrichment from urban development. Overall, the results reveal that while the urban infrastructure in this region has not significantly affected most of the water quality parameters, bacteria and coliphages are still a main concern due to their contributions in widespread waterborne diseases. Thus, careful mitigation plans for reducing each pollutant source are needed in the Angkor area.

Stormwater runoff from three highway sites in Los Angeles, California, was monitored, during the 2000 to 2003 wet seasons. Correlations among heavy metals, polycyclic aromatic hydrocarbons (PAHs), and storm characteristics were performed using datasets collected for 62 storm events. Statistical correlation analyses of the event mean concentrations (EMCs) and mass first-flush ratios (MFFs) with storm characteristics were conducted to determine if the first flush is related to site or storm characteristics. This study agreed with other highway runoff characterization studies, in that strong correlations were observed among the heavy metals and between heavy metals and total PAHs, and total suspended solids were well correlated with most heavy metals. Only antecedent dry days among storm characteristics were reasonably well-correlated with the EMCs of heavy metals and total PAHs, and dissolved and total metals exhibited similar MFFs, with approximately 30 to 35% of the mass being discharged in the first 20% of the runoff volume.

Artificial lakes, initially built in estuaries for positive purposes such as flood prevention and providing irrigation water, have been found to have negative impacts including blocking tidal cycles, disappearance of brackish water zones, sediment increase, water pollution, change of microbial diversity inhabiting patterns, and a decline in fish diversity. In this study, multidisciplinary field studies including physical, chemical, and biological analyses were performed to demonstrate decadal and seasonal scale changes in the ecological environment in Yeongsan Reservoir (YSR), Korea, since the construction of a 4.35 km-long dam in 1981. The results of the study show that the volume of sediment accumulated in YSR was 75.2 million m(3) since the dam was constructed, resulting in a 33.6% reduction of the total water storage capacity. Also, water quality in YSR was affected by complex physico-chemical and hydrological phenomena, including saline and thermal stratifications, and pollutant loadings leading to eutrophication. Subsequent sediment bacteria analyses showed microbial diversity according to different depths in sediment, indicating the environmental change of sediment ecology. Moreover, the fish diversity in this study (2006-2007) was found to be considerably reduced compared to a similar study in 1989 (42% reduction), and the ecological health was deemed to be in a "poor" condition based on the 10-metric Lentic Ecosystem Health Assessment (LEHA) model. Accordingly, these results indicate that aquatic ecosystems are detrimentally affected by estuarine dams that block tidal flows, and when applied to short/long-term management strategies for artificial lakes in estuaries, suggest that similar construction projects have to be suitably controlled.

A comprehensive water quality monitoring program was conducted in the Yeongsan (YS) River, Korea from 2005 to present to investigate wet and dry weather pollutant discharge in an attempt to establish point and non-point pollution management strategies. As part of this monitoring program, 11 heavy metal species were measured during dry and wet weather conditions in the YS River, where Gwangju City (GJ), a subcatchment of the YS River, was further monitored to clarify the responsibility of different metal species discharged into the mainstream. Monthly grab water samples showed that greater amounts of metals along the YS River were discharged during the wet summer months due largely to storm runoff. In addition, further monitoring results revealed that GJ, a highly urbanized area, was a significant contributor of the heavy metals being discharged into the YS River during both wet and dry weather. The most abundant metal species discharged from GJ were manganese, aluminum and iron with different contributions of wet and dry weather flows to the total discharge load. Wet weather flow was a significant contributor to the annual dissolved metal loads, accounting for 44-93% of the annual load depending on the metal species, with the exception of chromium and cadmium (9% and 27%, respectively). Mostly, metal loads during wet weather were shown to be proportional to the rainfall depth and antecedent dry period. A substantial fraction of metals were also associated with solids, suggesting that sedimentation might be an appropriate management practice for reducing the metal load generated in GJ. Overall, although dissolved metal concentrations in YS River were at an acceptable level for aquatic community protection, continual metal discharge throughout the year was considered to be a potential problem in the long-term due to gradual water quality degradation as well as continuous metal accumulation in the system.

A deterministic model was developed to predict pollutant mass first flush and to utilize it for better design of best management practices (BMPs) that focus on treating the first flush. The model used the kinematic wave equation to calculate flow and mass transport, and erosion equations to calculate pollutant concentrations, which were assumed to be from a short and a long term source. The model parameters were calibrated with a parameter estimation procedure using three years' monitoring data from a highway runoff site in west Los Angeles. The simulation results showed that there exists an optimum watershed size to maximize first flush. Contours of watershed length, developed from simulations for different conditions of rainfall and watershed geometry, can be used to design runoff collection systems for highways and parking lots to maximize first flush.

To clarify nitrogen (N) sources, the overall N budget in a forested watershed in Kanagawa Prefecture, Central Japan was estimated by measuring dissolved inorganic N (DIN; NH4 + + NO3 – + NO2 –) from Nov 2004 through Oct 2005. The estimated N budget (–1.43 kg N ha–1 year–1) showed that the N output rate (stream water N) was higher than the N input rate (bulk deposition N) in the watershed. The annual NO2 – and NO3 – input rates were 0.02 and 1.99 kg N ha–1 year–1, respectively. NH4 + was the predominant source in this forested watershed, accounting for 71% (4.99 kg N ha–1 year–1) of DIN input rate. In addition, this study estimated rainfall pH, air temperature, and wind direction, which were considered as controlling factors related to the atmospheric deposition rate of NH4 +. This study showed that the rainfall NH4 + was inversely proportional to the initial pH of the rainfall, which was calculated by adding the amount of H+ consumed by the dissociation process of NH3(aq) to the measured rainfall pH. This result implies that acid rain can elevate the solubility of NH3(g) and the dissociation capacity of NH4 + throughout the process of precipitation. Also, this study provides strong evidence that the high NH4 + deposition rate is mainly derived from NH3(g) emitted from livestock wastes under the NH3 transport condition of warm summer and favorable wind direction.