Search

Wastewater-based epidemiology (WBE) is drawing increasing attention as a promising tool for an early warning of emerging infectious diseases such as COVID-19. This study demonstrated the utility of a spatial bisection method (SBM) and a global optimization algorithm (i.e., genetic algorithm, GA), to support better designing and operating a WBE program for disease surveillance and source identification. The performances of SBM and GA were compared in determining the optimal locations of sewer monitoring manholes to minimize the difference among the effective spatial monitoring scales of the selected manholes. While GA was more flexible in determining the spatial resolution of the monitoring areas, SBM allows stepwise selection of optimal sampling manholes with equiareal subcatchments and lowers computational cost. Upon detecting disease outbreaks at a regular sewer monitoring site, additional manholes within the catchment can be selected and monitored to identify source areas with a required spatial resolution. SBM offered an efficient method for rapidly searching for the optimal locations of additional sampling manholes to identify the source areas. This study provides strategic and technical elements of WBE including sampling site selection with required spatial resolution and a source identification method.

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.

The HSPF model was modified to improve the growth-temperature formulation of phytoplankton and used to simulate Chl-a concentrations at the outlet of the Seom River watershed in Korea from 2025 to 2050 under four climate change scenarios: RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5. The mean and median Chl-a concentrations increased by 5–10% and 23–29%, respectively, and the number of algal outbreak cases per year (defined as a day with Chl-a concentration ≥100 µg/L) decreased by 31–88% relative to the current values (2011–2015). Among the climate change scenarios, RCP 2.6 (stringent) showed the largest number of algal outbreak cases, mainly because of the largest yearly variability of precipitation and TP load. For each climate change scenario, three nutrient load reduction scenarios were in the HSPF simulation, and their efficiencies in reducing algal blooms were determined. Nonpoint source reduction in TP and TN from urban land, agricultural land, and grassland by 50% (S1) and controlling the effluent TP concentration of wastewater treatment plants (WWTPs) to 0.1 mg/L (S2) decreased algal outbreaks by 20–58% and 44–100%, respectively. The combination of effluent TP control of WWTPs during summer and S1 was the most effective management scenario, it could almost completely prevent algal outbreaks. This study demonstrates the cost effectiveness of using a season-based pollutant management strategy for controlling algal blooms.

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.

Trends in the use of an anticoagulant as a dispersing stabilizer are addressed. An effective approach to preparing stable nanosized titanium dioxide (nTiO2) for accurate and systematic assessment of nano- toxicity has not been established. Among the dispersants tested here, it was found that sodium polyacrylate (PAA) was the most effective dispersant for nTiO2 in culture media. Our study was the first to demonstrate that a stable PAA-dispersed nTiO2 (nTiO2/PAA) suspension showed more toxic than nTiO2 without PAA in human HaCaT keratinocytes. Initially, MTT results showed that the stable nTiO2/PAA dispersion exhibited significantly greater cytotoxicity than nTiO2 without PAA. In addition, the stable nTiO2/PAA dispersion induced markedly more oxidative stress than nTiO2 without PAA. Importantly, the stable nTiO2/PAA dispersion caused DNA breakage to a greater extent than nTiO2 without PAA. Our findings indicated that the anti-coagulant PAA is suitable for preparing homologous dispersed nTiO2 under realistic physiological culture test conditions.

Various waterborne pathogens originate from human or animal feces and may cause severe gastroenteric outbreaks. Bacteroides spp. that exhibit strong host- or group-specificities are promising markers for identifying fecal sources and their origins. In the present study, 240 water samples were collected from two major aquaculture areas in Republic of Korea over a period of approximately 1 year, and the concentrations and occurrences of four host-specific Bacteroides markers (human, poultry, pig, and ruminant) were evaluated in the study areas. Host-specific Bacteroides markers were detected widely in the study areas, among which the poultry-specific Bacteroides marker was detected at the highest concentration (1.0-1.2 log10 copies L-1). During the sampling period, high concentrations of host-specific Bacteroides markers were detected between September and December 2015. The host-specific Bacteroides marker-combined geospatial map revealed the up-to-downstream gradient of fecal contamination, as well as the effects of land-use patterns on host-specific Bacteroides marker concentrations. In contrast to traditional bacterial indicators, the human-specific Bacteroides marker correlated with human specific pathogens, such as noroviruses (r=0.337; P

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.

Identifying critical land-uses or source areas is important to prioritize resources for cost-effective stormwater management. This study investigated the use of information on ionic composition as a fingerprint to identify the source land-use of stormwater runoff. We used twelve ionic species in stormwater runoff monitored for a total of 20 storm events at five sites with different land-use compositions during 2012–2014 wet seasons. A stepwise forward discriminant function analysis (DFA) with the jack-knifed cross validation approach was used to select ionic species that better discriminate the land-use of its source. Of the twelve ionic species, 9 species (K + , Mg 2+ , Na + , NH 4 + , Br − , Cl − , F − , NO 2 − , and SO 4 2− ) were selected for better performance of the DFA. The DFA successfully differentiated stormwater samples from urban, rural, and construction sites using concentrations of the ionic species (70%, 95%, and 91% of correct classification, respectively). Over 80% of the new data cases were correctly classified by the trained DFA model. When applied to data cases from a mixed land-use catchment and downstream, the DFA model showed the greater impact of urban areas and rural areas respectively in the earlier and later parts of a storm event.

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.

Yeo Jin Kim,1,2 Md Mujibur Rahman,1 Sang Min Lee,2 Jung Min Kim,3 Kwangsik Park,4 Joo-Hyon Kang,5 Young Rok Seo1,2 1Institute of Environmental Medicine for Green Chemistry, Dongguk University Biomedi Campus, Ilsandong-gu, Goyang-si, Republic of Korea; 2Department of Life Science, Dongguk University Biomedi Campus, Ilsandong-gu, Goyang-si, Republic of Korea; 3Genoplan Korea, Inc., Seocho-gu, Seoul, Republic of Korea; 4College of Pharmacy, Dongduk Women’s University, Seongbuk-gu, Seoul, Republic of Korea; 5Department of Civil & Environmental Engineering, Dongguk University, Jung-gu, Seoul, Republic of Korea Background: Silver nanoparticles (AgNPs) are widely used in industrial and household applications, arousing concern regarding their safety in humans. The risks posed by stabilizer-coated AgNPs continue to be unclear, and assessing their toxicity is for an understanding of the safety issues involved in their use in various applications. Purpose: We aimed to investigated the long-term toxicity of citrate-coated silver nanoparticles (cAgNPs) in liver tissue using several toxicity tests and transcriptomic analysis at 7 and 28 days after a single intravenous injection into rabbit ear veins (n=4). Materials and methods: The cAgNPs used in this study were in the form of a 20% (w/v) aqueous solution, and their size was 7.9±0.95 nm, measured using transmission electron microscopy. The animal experiments were performed based on the principles of good laboratory practice. Results: Our results showed that the structure and function of liver tissue were disrupted due to a single exposure to cAgNPs. In addition, in vivo comet assay showed unrepaired genotoxicity in liver tissue until 4 weeks after a single injection, suggesting a potential carcinogenic effect of cAgNPs. In our transcriptomic analysis, a total of 244 genes were found to have differential expression at 28 days after a single cAgNP injection. Carefully curated pathway analysis of these genes using Pathway Studio and Ingenuity Pathway Analysis tools revealed major molecular networks responding to cAgNP exposure and indicated a high correlation of the genes with inflammation, hepatotoxicity, and cancer. Molecular validation suggested potential biomarkers for assessing the toxicity of accumulated cAgNPs. Conclusion: Our investigation highlights the risk associated with a single cAgNP exposure with unrepaired damage persisting for at least a month. Keywords: nanotoxicity, liver toxicity, prolonged tissue damage, differentially expressed genes, molecular pathway analysis

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.

Hydrodynamic separators (HDSs) have been used extensively to reduce stormwater pollutants from urbanized areas before entering the receiving water bodies. They primarily remove particulates and associated pollutants using gravity settling. Two types of HDSs with different structural configurations of the inner vortex-inducing components were presented in this study. One configuration consisted of a dip cylindrical plate with a center shaft while the other one has a hollow screen inside. With the help of computational fluid dynamics, the performance of these different types of HDSs have been evaluated and comparatively analyzed. The results showed that the particle removal efficiency was better with the cylindrical plate type HDSs than the screen type HDSs because of the larger swirling flow regime formed inside the device. Plate type HDSs were found more effective in removing fine particles (∼50 μm) than the screen type HDSs that were only efficient in removing large particles (≥250 μm). Structural improvements in a HDS such as increase in diameter and angle of the inlet pipe can enhance the removal efficiencies by up to 20% for plate type HDS while increase in the screen diameter can increase removal efficiencies of the screen type HDS.

Application of photocatalytic nanoparticles has been recently gaining an increased attention as air purifying material for sustainable urban development. The present work reports the photocatalytic removal of gaseous phase nitrogen oxides (NOx) using TiO2-coated zeolite to be applied as a filter media for the urban green infrastructure such as raingardens. The TiO2-coated zeolite was synthesized by simple wet chemistry method and tested in a continuous-flow photo-reactor for its removal efficiency of NOx under different conditions of the weight percentage of TiO2 coated on the zeolite, and gas retention time. The removal efficiency of NOx in general increased as the weight percentage of TiO2 coated on the zeolite increased up to 15-20%. Greater than 90% of NOx was removed at a retention time of one minute using the TiO2-coated zeolite (TiO2 weight percentage = 20%). Overall, TiO2-coated zeolite showed greater efficiency of NOx removal compared to TiO2 powder probably by providing additional reaction sites from the porous structure of zeolite. It was presumed that the degradation of NOx is attributed to both the physical adsorption and photocatalytic oxidation that could simultaneously occur at the catalyst surface.

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.

The rapid increase of impervious area and climate change greatly affect the hydrological, environmental, and ecological system at the local, regional, and global scales. These phenomena can increase the peak flow and surface runoff carrying anthropogenic pollutants, thereby severely deteriorating the water quality of the surface waters. Low-impact development (LID) practices have been proposed as a promising urban management methodology to mitigate these environmental issues. Numerical models have been increasingly utilized as an analyzing tool for evaluating the LID performance. However, LID-associated numerical models are oversimplified in terms of water quality simulation by only considering the dilution effect by rainfall in LIDs. This study resolved this challenge by modifying the water quality module of LID in the stormwater management model (SWMM). We evaluated the module performance for simulating total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) of the LID facilities. Using the developed model, we conducted the LID scenario analysis under the climate change scenarios. The scenario analysis was applied in the urban area which focused on flow rate and TSS. The modified module provided accurate results for pollutant simulations, yielding an average value of Root mean square error-observation Standard deviation Ratio (RSR) of 0.52, while the original module showed an unacceptable performance, with an RSR value of 1.11. Scenario analysis showed that the hydrological outputs were sensitive to the volume of rainfall while the water quality results were sensitive to the temporal distribution of rainfall. Based on these statements, the modified water quality module in LID developed in this study will be useful in designing LID facilities and in formulating guidelines for LID installation.

Accurate prediction of chlorophyll a (Chl a) concentration in surface water bodies such as lakes or rivers is crucial for water quality management. This study improved the predictive simulation of instream Chl a with the Hydrological Simulation Program-FORTRAN (HSPF) by adding automatic calibration and modifying the growth-temperature formulation of phytoplankton in the original HSPF model. A total of 62 model parameters, selected from a series of sensitivity analyses, were automatically calibrated in a stepwise manner for different variables in the order of flow, sediment, water temperature, ammonia/nitrate couple, and phosphate/Chl a couple. With finer temporal resolution (5–8 days) data than those of majority of the existing HSPF studies, the automatic calibration procedure provided the model with performance ratings of ‘satisfactory’ or better for all the variables including nutrients and Chl a: The percent bias values ranged from -18% - 54% and -20% – 62% for nutrients and Chl a, respectively. The original linear equation on the growth-temperature relationship of phytoplankton in simulating instream Chl a was modified using a quadratic equation and an exponential equation. The exponential equation outperformed the original linear and quadratic equations, particularly in simulating the excess concentrations of Chl a observed during summer seasons. For the validation data set, the exponential equation predicted 78% of the eutrophic cases while the linear and quadratic equation only predicted 53% and 13% of the eutrophic cases, respectively. The modified HSPF model offers an improved prediction of instream Chl a. This approach will be useful for providing early warning of algal blooms, facilitating the implementation of effective management of stream water quality.

Various waterborne pathogens originate from human or animal feces and may cause severe gastroenteric outbreaks. Bacteroides spp. that exhibit strong host- or group-specificities are promising markers for identifying fecal sources and their origins. In the present study, 240 water samples were collected from two major aquaculture areas in Republic of Korea over a period of approximately 1 year, and the concentrations and occurrences of four host-specific Bacteroides markers (human, poultry, pig, and ruminant) were evaluated in the study areas. Host-specific Bacteroides markers were detected widely in the study areas, among which the poultry-specific Bacteroides marker was detected at the highest concentration (1.0–1.2 log10 copies L−1). During the sampling period, high concentrations of host-specific Bacteroides markers were detected between September and December 2015. The host-specific Bacteroides marker-combined geospatial map revealed the up-to-downstream gradient of fecal contamination, as well as the effects of land-use patterns on host-specific Bacteroides marker concentrations. In contrast to traditional bacterial indicators, the human-specific Bacteroides marker correlated with human specific pathogens, such as noroviruses (r=0.337; P

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

This study investigated adsorption characteristics of lead on bead-type holocellulose aerogels synthesized from wood fiber by using an alkali hydroxide — urea solution. The synthesized holocellulose aerogels were classified into two groups for comparison according to the source of the precursor hydrogel, a dissolved upper layer and a precipitated bottom layer of hydrogel solution, formed during the synthesis procedure. As the equilibrium concentration of lead in the solution was increased, the amount of lead adsorbed on the holocellulose aerogel increased, eventually reaching a maximum. As the pH was increased, the adsorption capacity of lead also increased. The adsorption rate rapidly increased during the earlier 10 min period and then asymptotically approached equilibrium. However, as the lead concentration in the water was increased, the time required for achieving equilibrium also increased due probably to the dynamic exchange of lead molecules during the adsorption process. The lead adsorbed to the aerogels was visually identified by using Scanning electron microscopy (SEM) and electron dispersive spectroscopy (EDS) analyses. This study suggests that the holocellulose aerogel can be potentially used in many water treatment applications targeting heavy — metal, including lead, removal.

We examined the characteristics of wood charcoal generated from ubame oak (Quercus phillyraeoides) and sugi (Cryptomeria japonica) at different carbonization temperatures up to 1200 °C with different heating rates of 0.1, 1 and 10 °C/min. In general, pyrolysis was achieved at a lower temperature when a lower heating rate was applied for both of the wood species. At temperatures greater than or equal to 400 °C, Sugi showed a relatively smaller weight loss at a heating rate of 1 °C/min compared to the other hearing rates while the amount of the weight loss changed little regardless of the heating rate for ubame oak. At 600 °C, a lower heating rate resulted in a greater char yield for sugi while a heating rate of 1 °C/min produced the greatest char yield for ubame oak. At this temperature condition, the largest specific surface areas obtained were 827 m2/g at 10 °C/min and 447.9 m2/g at 1 °C/min for sugi and ubame oak, respectively. At 1200 °C, the greatest char yield was obtained at a heating rate of 1 °C/min for both wood species, and a lower heating rate produced a greater specific surface area for sugi while a porous structure was rarely observed in ubame oak. The X-ray diffractograms of the charcoals of the two wood species showed the noncrystalline structure of graphite carbon for all the tested hearing rates. The bending strength of the charcoal was the greatest at heating rates of 1 °C/min and 0.1 °C/min for sugi and ubame oak, respectively. Furthermore, a higher temperature provided a greater compressive strength of the charcoals. The modulus of elasticity changed little with changing carbonization temperature or changing heating rate.

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 ...

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data.

This study examined the characteristics of charcoals generated from White Lauan (Pentacmecontorta) and Punah (Tetrameristaglabra) by using different carbonization temperatures and heating rates. The scanning electron micrographs showed vestured pits in the White Lauan and raphide crystals in Punah as their respective anatomical characteristics. A slower heating rate resulted in a lower temperature to obtain the same amount of weight loss, regardless of the species being tested. A greater charcoal yield was obtained at a higher heating rate. The specific surface area was smaller in the charcoal produced at a higher carbonization temperature, but the heating rate had little effected. For both wood species, the axial compressive strength of the charcoal increased as the carbonization temperature was increased. The X-ray diffractograms of White Lauan and Punah woods heated at 1200°C indicated thermal decomposition of the crystal structure of cellulose, but no appreciable structural changes occurred under the tested heating rate conditions. Overall, the heating rate affected the charcoal yield but not the specific surface area, compressive strength, and crystal structure.

A tulip tree was used to synthesize a holocellulose aerogel from an aqueous alkali hydroxide-urea solution with the substitution of an organic solvent followed by freeze-drying. For comparison, the synthesized holocellulose aerogels were divided into two groups according to the source of the hydrogel, an upper suspended layer and a bottom concentrated layer of the centrifuged solution of cellulose and NaOH/urea solvents. We investigated the effects of the temperature of the pre-cooled NaOH/urea solution (i.e., dissolution temperature) on the pore structure and the adsorption capacity of the holocellulose aerogel. A nano-fibrillar network structure of the holocellulose aerogel was observed, with little morphological difference in pore structure for different dissolution temperatures. Both micropores and mesopores were observed in the holocellulose aerogel. The specific surface area of the holocellulose aerogel was generally greater at lower dissolution temperatures. In a series of adsorption tests using methylene blue, the holocellulose aerogel showed the greatest adsorption capacity at the lowest dissolution temperature tested (−2°C). However, the dissolution temperature generally had little effect on the adsorption capacity. The holocellulose aerogel produced from the upper suspended layer of the centrifuged hydrogel solution showed a greater porosity and adsorption capacity than the one produced from the bottom concentrated layer. Overall, the aerogel made by utilizing a delignified tulip tree display a high surface area and a high adsorption property, indicating its possible application in eco-friendly adsorption materials.

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 study investigated the removal efficiency of target pollutants from an underground stormwater treatment device (a hydrodynamic separator), focusing on the overall performance of the devices of a catchment. An approach for sizing an underground stormwater treatment device was developed, in order to obtain the required reduction percentage of the total suspended solids (TSS) generated from a given impervious catchment. The United States Environmental Protection Agency's stormwater management model (SWMM) was used for developing contours to help determine the size of the device, with respect to the maximum inflow to the device (or bypass rate), and the catchment area served by the device. Additionally, three different configurations of underground stormwater treatment devices were examined. It was found that, for a given catchment area, a single large device provides slightly better performance than multiple small devices. The approach we propose here can be useful to determine the sizes, as well as to clarify the efficiencies, of different installation configurations of underground stormwater treatment device (e.g. a hydrodynamic separator) in relation to their bypass rates and site specific conditions, such as rainfall characteristics and the catchment area to be served.

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 ...

This research was conducted to investigate the phosphorus forms present in water, soil and sediment and to estimate the phosphorus mass balance in a surface flow constructed wetland (CW). Water quality and sediment samples were collected from each cell along the hydrologic path in the CW from October 2008 to December 2010. At the same time, three dominant plant species (e.g. common reed and cattails) were observed through the measurement of the weight, height and phosphorus content. Based on the results, the orthophosphate constituted 24–34% of total phosphorus in water for each cell. The overall average phosphorus removal efficiency of the CW was approximately 38%. The average inflow and outflow phosphorus loads during the monitoring period were 1,167 kg/yr and 408 kg/yr, respectively. The average phosphorus retention rate was 65%, was mainly contributed by the settling of TP into the bottom sediments (30%). The phosphorus uptake of plants was less than 1%. The estimated phosphorus mass balance was effective in predicting the phosphorus retention and release in the CW treating wastewater. Continuous monitoring is underway to support further assessment of the CW system and design.

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

Without maintenance, the performance of any stormwater treatment practice will decline over time until it reaches an unacceptable level. Thus, every stormwater management plan should include an estimated schedule for maintenance activities, and funds should be budgeted to support this schedule. This chapter provides recommendations for maintenance activities based on the treatment practice and assessment results and also presents typical suggested corresponding frequencies of such activities. In addition, this chapter provides the results of a maintenance activity survey, again grouped by the type of practice, that offers insights on typical issues that trigger the need for maintenance, maintenance complexity, and maintenance frequency.

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.

Although street sweeping is commonly regarded as a cost-effective storm-water best management practice, there is little quantitative evidence that street sweeping directly improves runoff water quality. In this paper, several previous street sweeping studies were reevaluated using statistical power analysis. Two-group, independent-sample one-sided t -test power analyses were performed using log-transformed event mean concentrations (EMCs) of total suspended solids, suspended sediment concentration or chemical oxygen demand. The effect size between the two groups was estimated using the sweepers’ pickup efficiency, which showed that the failure to detect the difference between mean EMCs of the two sample groups (i.e., unswept and swept groups) is likely due to limited sample numbers. Too few samples, which also resulted in a high coefficient of variation, were analyzed to detect the likely difference between swept and unswept observations. In addition, the temporal gap between street sweeping and subsequen...

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 hydro-meteorological 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.

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.

Storm-water monitoring generally uses flow-weighted automatic composite samplers to collect a representative sample of an entire storm event. Automatic samplers are convenient but unfortunately they can be expensive, especially for temporary sampling needs or for short-term research projects. An alternative method is to use a series of grab samples. This paper examines the accuracy of event mean concentrations (EMCs) and mass first flush ratios calculated from a finite number of grab samples, and compares them to results from flow-weighted automatic samples. Both sampling techniques were evaluated using data collected from a three-year investigation of three highway sites. A large number of grab samples is needed to approach the accuracy and precision of flow weighted composite samples, and 30 grab samples per storm event generally estimated the EMCs within 20% average error. To detect a first flush, it is necessary to take even more grab samples or to adjust the timing of the sample collection toward the beginning of the storm. The superiority of automatic sampling for estimating EMCs for constituents compatible with automatic sampling is demonstrated.

Mass reduction rates of particles and metals were simulated for a two-compartment settling tank composed of a storage compartment and a continuous flow compartment. Particle-size distribution, rainfall, and flow data from 16 storm events measured at three highway sites were used. The volume ratio (i.e., ratio of surface areas for a given depth) between storage and continuous flow compartment was optimized for a given design storm size to maximize total mass reduction rates of particles and heavy metals. Measured settling velocity profiles of runoff samples were used in the simulation. Simulation results showed that in a given total design storm, larger storage compartment fractions (>0.95) enhanced the removal of smaller particles (2–104 μm) and particulate phase metals, and even a small fraction (

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).

Sedimentation column studies and simulations using particle size distribution suggest that low removal efficiencies of smaller particles in highway runoff would be obtained using sedimentation if coagulation-flocculation is not performed. Coagulation-flocculation studies, using metal salts (alum and ferric chloride) and one organic polymer in three molecular weights, were evaluated over the 2004.2005 storm seasons. Only the first flush or approximately the first hour of runoff was coagulated. Efficiencies were quantified with particle size distribution measurements and turbidity. Results with low dosages of metal salts were ineffective and did not improve water quality. High dosages of metal salts using a sweep floc mechanism were effective in dramatically lowering runoff turbidity, but resulted in large quantities of sludge production and required pH control. A cationic organic polymer at low dosages (

Litter characterization is an integrated part of the Caltrans First Flush Characterization Study. These data will provide a basis to develop potential treatment technologies and best management practices to control pollutants in runoff from freeways. During monitoring periods in Southern California areas, the first flush phenomenon was evaluated and the impacts of various parameters such as rain intensity, drainage area, peak flow rate, and antecedent dry period on litter volume and loading rates were evaluated. First flush phenomenon was generally observed for litter concentrations, but was not apparent with litter mass loading rates. Total captured gross pollutants, defined as larger than 0.5 cm, was 90% vegetation with only 10% being litter. The normalized cumulative litter loadings were determined from 1.25 to 13.39 kg/ha for dry litter weight and 0.40 to 8.99 kg/ha for dry biodegradable litter weight. The portions of biodegradable litter to non-biodegradable litter were roughly the same across the entire event. Event mean concentrations were ranged 0.0021 to 0.259 g/L for wet gross pollutants, 0.0001 to 0.027 g/L for wet litters and 0.00007 to 0.018 g/L for dry litters. The mass emission rates should be useful to estimate total litter production for developing total maximum daily loads.