Comparative cancer risk assessment of THMs in drinking water from well water sources and surface water sources

This research evaluates the lifetime cancer risks from trihalomethanes in Tehran's drinking water. The Trihalomethanes were measured in seven different water districts. Sixty-three samples were taken from tap water across the city for 7 months. The samples were analyzed for trihalomethanes using US EPA method 524.2. The average concentration of total trihalomethanes in different districts were between 0.81 and 9.0 μg/L, and the highest concentrations were detected in district 2 at 19.5 μg/L. Total lifetime cancer risks assessment from exposure to trihalomethanes in drinking water (ingestion, inhalation, and skin routes) were performed for people living in different districts in Tehran. The lifetime cancer risk was 7.19 × 10(-5) in district 2 (a more affluent neighborhood) where mostly surface water sources is used to supply drinking water and 9.38 × 10(-6) in district 7 (a less affluent neighborhood) which is mainly supplied with well water sources. Based on the population data, the total expected lifetime cancer cases from exposure to trihalomethanes are 104, 108, 81, 81, 41, 27, and three for districts 1 through 7, respectively. The average lifetime cancer risk was 4.33 × 10(-5) which means a total of 606 lifetime cancer cases for the entire province of Tehran. The highest risk from THMs seems to be from the inhalation route followed by ingestion and dermal contacts.     

Assessment of surface water quality using multivariate statistical techniques: a case study of Behrimaz Stream, Turkey

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis, and factor analysis, were applied for the evaluation of temporal/spatial variations and for the interpretation of a water quality data set of the Behrimaz Stream, obtained during 1 year of monitoring of 20 parameters at four different sites. Hierarchical CA grouped 12 months into two periods (the first and second periods) and classified four monitoring sites into two groups (group A and group B), i.e., relatively less polluted (LP) and medium polluted (MP) sites, based on similarities of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the two different groups obtained from cluster analysis, resulted in five latent factors amounting to 88.32% and 88.93% of the total variance in water quality data sets of LP and MP areas, respectively. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge, temperature, and soluble minerals (natural) and nutrients (nonpoint sources: agricultural activities) in relatively less polluted areas; and organic pollution (point source: domestic wastewater) and nutrients (nonpoint sources: agricultural activities and surface runoff from villages) in medium polluted areas in the basin. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and interpretation of data sets and, in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective stream water quality management.     

Antibiotic resistance analysis of fecal coliforms to determine fecal pollution sources in a mixed-use watershed

Antibiotic resistance analysis was performed on fecal coliform(FC) bacteria from a mixed-use watershed to determine thesource, human or nonhuman, of fecal coliform contamination. The study consisted of discriminant analysis of antibioticresistance patterns generated by exposure to fourconcentrations of six antibiotics (ampicillin, gentamicinsulfate, kanamycin, spectinomycin dihydrochloride,streptomycin sulfate, and tetracycline hydrochloride). Areference database was constructed from 1125 fecal coliformisolates from the following sources: humans, domestic animals(cats and dogs), agricultural animals (chickens, cattle, andhorses), and wild animals. Based on similar antibioticresistance patterns, cat and dog isolates were grouped asdomestic animals and horse and cattle isolates were grouped aslivestock. The resulting average rate of correctclassification (ARCC) for human and nonhuman isolates was94%. A total of 800 FC isolates taken from the watershedduring either a dry event or a wet event were classifiedaccording to source. Human sources contribute a majority(>50%) of the baseflow FC isolates found in the watershed inurbanized areas. Chicken and livestock sources areresponsible for the majority of the baseflow FC isolates foundin the rural reaches of the watershed. Stormwater introducesFC isolates from domestic (16%) and wild (21%) sourcesthroughout the watershed and varying amounts (up to 60%) fromchicken and livestock sources. These results suggest thatantibiotic resistance patterns of FC may be used to determinesources of fecal contamination and aid in the direction ofwater quality improvement.     

Assessment of anthropogenic sources of water pollution using multivariate statistical techniques: a case study of the Alqueva’s reservoir, Portugal

Multivariate statistical techniques were applied to evaluate spatial/temporal variations, and to interpret water quality data set obtained at Alqueva reservoir (south of Portugal). The water quality was monitored at nine different sites, along the water line, over a period of 18 months (from January 2006 to May 2007) using 26 water quality parameters. The cluster analysis allowed the formation of five different similarity groups between sampling sites, reflecting differences on the water quality at different locations of the Alqueva reservoir system. The PCA/FA identified six varifactors, which were responsible for 64% of total variance in water quality data set. The principal parameters, which explained the variability of quality water, were total phosphorus, oxidability, iron, parameters that at high concentrations indicate pollution from anthropogenic sources, and herbicides indicative of an intensive agricultural activity. The spatial analysis showed that the water quality was worse in the north of the reservoir.     

Determination of selected steroid hormones in some surface water around animal farms in Cape Town using HPLC-DAD

In this study, a method for the simultaneous determination of two steroid hormones, 17β-estradiol (E2) and estriol (E3), and a hormone mimicking polycarbonate, bisphenol-A (BPA), was developed and validated. This was thereafter used for the determination of the levels of the hormones in surface water collected around some livestock farms. The sensitivity of the method allowed the LODs and LOQs of the hormones and mimic hormone in the range 1.14–2.510 and 3.42–7.53 μg/L, respectively. The results revealed wide variability in the concentrations of E2 and E3, while BPA was not detected at any of the sampling stations. The concentration of E3 ranged between <1.14 and 45.5 μg/L (N = 120) in station 2 water. The highest concentration of E2 (15.7 μg/L, N = 80) was observed in water from station 1. The varied concentrations may be connected with the nature and sources of release, inconsistencies in analyte distribution due to dynamics of water flow pattern and the physical/chemical properties of the receiving water bodies.     

Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study

In this paper, a new methodology is developed for integrated allocation of water and waste-loads in river basins utilizing a fuzzy transformation method (FTM). The fuzzy transformation method is used to incorporate the existing uncertainties in model inputs. In the proposed methodology, the FTM, as a simulation model, is utilized in an optimization framework for constructing a fuzzy water and waste-loads allocation model. In addition, economic as well as environmental impacts of water allocation to different water users are considered. For equitable water and waste load allocation, all possible coalition of water users are considered and total benefit of each coalition, which is a fuzzy number, is reallocated to water users who are participating in the coalition. The fuzzy cost savings are reallocated using a fuzzy nucleolus cooperative game and the FTM. As a case study, the Dez River system in south-west of Iran is modeled and analyzed using the methodology developed here. The results show the effectiveness of the methodology in optimal water and waste-loads allocations under uncertainty.     

Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China

In the study, multivariate statistical methods including factor, principal component and cluster analysis were applied to analyze surface water quality data sets obtained from Xiangjiang watershed, and generated during 7 years (1994-2000) monitoring of 12 parameters at 34 different profiles. Hierarchical cluster analysis grouped 34 sampling sites into three clusters, including relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, and based on the similarity of water quality characteristics, the watershed was divided into three zones. Factor analysis/principal component analysis, applied to analyze the data sets of the three different groups obtained from cluster analysis, resulted in four latent factors accounting for 71.62%, 71.77% and 72.01% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The PCs obtained from factor analysis indicate that the parameters for water quality variations are mainly related to dissolve heavy metals. Thus, these methods are believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.     

Evaluation of surface water quality characteristics by using multivariate statistical techniques: A case study of the Euphrates river basin, Turkey

The surface water quality of the Euphrates river basin in Turkey are evaluated by using the multivariate statistical techniques known as factor analysis (FA) and multidimensional scaling (MDS) analysis. When FA was applied to the water quality data obtained from the 15 different surface water quality monitoring stations, two factors were identified, which were responsible from the 86.02% of the total variance of the water quality in the Euphrates river basin. The first factor called the urban land use factor explained 44.20% of the total variance and the second factor called the agricultural use factor explained 41.81% of the total variance. MDS technique showed that electrical conductivity (EC), percent sodium (Na%) and total salt are the most important variables causing difference in the water quality analysis.     

A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.     

Ecological assessments of surface water bodies at the river basin level: a case study from England

Ecological assessments of surface water bodies are essential in order to evaluate the level of degradation in freshwater ecosystems and to address the subsequent decline in services they provide. These assessments cover multiple aspects of the aquatic environment, particularly biological elements due to their ability to respond to all pressures within an ecosystem. Such assessments can enable the identification of the multiple pressures which threaten water bodies, facilitating sustainable decisions regarding their management to be identified. Here, the design requirements of the networks which facilitate ecological assessments are presented. A river basin district in England is used as a case study to investigate the number of elements monitored, the number of failing elements and the relationship between failing elements. Findings demonstrate the value of ensuring that monitoring networks are risk based and appropriately designed to meet their objectives. This therefore requires that monitoring is not only for the communicating of compliance but also for use iteratively so that the design of monitoring networks and ultimately management can be continually improved.     

Characterization of spatial and temporal patterns in surface water quality: a case study of four major Lebanese rivers

In this work, four major Lebanese rivers were investigated, the Damour, Ibrahim, Kadisha, and Orontes, which are located in South, Central, and North Lebanon and Bekaa Valley, respectively. Five sampling sites were considered from upstream to downstream, and 12 sampling campaigns over four seasons were conducted during 2010–2011. Thirty-seven physicochemical parameters and five microbial tests were evaluated. A principal component analysis (PCA) was used for data evaluation. The first PCA, applied to the matrix-containing data that was acquired on all four rivers, showed that each river was distinct in terms of trophic state and pollution sources. The Ibrahim River was more likely to be polluted with industrial and human discharges, while the Kadisha River was severely polluted with anthropogenic human wastes. The Orontes and Damour rivers seemed to have the lowest rates of water pollution, especially the Orontes, which had the best water quality. PCA was also performed on individual data matrices for each river. In all cases, the results showed that the springs of each river have good water quality and are free from severe contamination. The other monitoring sites on each river were likely exposed to human activities and showed important spatial evolution. Through this work, a spatiotemporal fingerprint was obtained for each studied river, defining a “water mass reference” for each one. This model could be used as a monitoring tool for subsequent water quality surveys to highlight any temporal evolution of water quality.

Open image in new window

Graphical abstract ?

     

Design of on-line river water quality monitoring systems using the entropy theory: a case study

The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems.     

Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin,northeastern China,from 2011 to 2015

The analysis of a large number of multidimensional surface water monitoring data for extracting potential information plays an important role in water quality management. In this study, growing hierarchical self-organizing map (GHSOM) was applied to a water quality assessment of the Songhua River Basin in China using 22 water quality parameters monitored monthly from 13 monitoring sites from 2011 to 2015 (14,782 observations). The spatial and temporal features and correlation between the water quality parameters were explored, and the major contaminants were identified. The results showed that the downstream of the Second Songhua River had the worst water quality of the Songhua River Basin. The upstream and midstream of Nenjiang River and the Second Songhua River had the best. The major contaminants of the Songhua River were chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), and fecal coliform (FC). In the Songhua River, the water pollution at downstream has been gradually eased in years. However, FC and biochemical oxygen demand (BOD₅) showed growth over time. The component planes showed that three sets of parameters had positive correlations with each other. GHSOM was found to have advantages over self-organizing maps and hierarchical clustering analysis as follows: (1) automatically generating the necessary neurons, (2) intuitively exhibiting the hierarchical inheritance relationship between the original data, and (3) depicting the boundaries of the classification much more clearly. Therefore, the application of GHSOM in water quality assessments, especially with large amounts of monitoring data, enables the extraction of more information and provides strong support for water quality management.     

Monitoring of aminophenol isomers in surface water samples using a new HPLC method

A new HPLC method was developed for the simultaneous determination of aminophenol isomers by means of a mixed-mode stationary phase containing both SCX and C18 moieties. All factors influencing the separation were discussed and optimized. The chromatographic conditions for the separation of aminophenols are the stationary phase duet SCX/C18, the mobile phase of aqueous phosphate buffer (pH 4.85):methanol?=?85:15 (v/v) delivered with a flow rate of 1 mL/min and a detection at 285 nm. The method proposed was validated in terms of linearity, limits of detection and quantification, accuracy and precision. The HPLC method elaborated here was applied with good results on river water samples. In order to survey the quality of surface rivers entered in treatment plants which deliver water for Bucharest, two major rivers were included in a monitoring program which last more than 1 year.     

Assessing the influence of nutrient reduction on water quality using a three-dimensional model: case study in a tidal estuarine system

A coupled three-dimensional hydrodynamic and water quality model has been developed and applied to the Danshuei River estuarine system and adjacent coastal sea. The water quality model considers various species of nitrogen, phosphorus, organic carbon, and phytoplankton as well as dissolved oxygen and is driven by a three-dimensional hydrodynamic model. The hydrodynamic and water quality models were validated with observations of water surface elevation, velocity, salinity distribution, and water quality parameters. Statistical error analysis shows that predictions of hydrodynamics, salinity, dissolved oxygen, and nutrients from the model simulation quantitatively agreed with the observed data. The validated model was then applied to predict water quality conditions as a result of a reduction in nutrient loadings based on different engineering strategies. The simulated results revealed that the dissolved oxygen concentration would increase significantly and would be higher than 2 mg/L in the main stream and in three tributaries to meet the minimum statutory requirement for dissolved oxygen. Active estuarine management focused on the reduction of anthropogenic nutrient loads is needed for improvement in water quality.     

Applicability of universal Bacteroidales genetic marker for microbial monitoring of drinking water sources in comparison to conventional indicators

Water quality monitoring is essential for the provision of safe drinking water. In this study, we compared a selection of fecal indicators with universal Bacteroidales genetic marker to identify fecal pollution of a variety of drinking water sources. A total of 60 samples were collected from water sources. The microbiological parameters included total coliforms, fecal coliforms, Escherichia coli and fecal streptococci as the fecal indicator bacteria (FIB), Clostridium perfringens and H₂S bacteria as alternative indicators, universal Bacteroidales genetic marker as a promising alternative fecal indicator, and Salmonella spp., Shigella spp., and E. coli O157 as pathogenic bacteria. From 60 samples analyzed, Bacteroidales was the most frequently detected indicator followed by total coliforms. However, the Bacteroidales assay failed to detect the marker in nine samples positive for FIB and other alternative indicators. The results of our study showed that the absence of Bacteroidales is not necessarily an evidence of fecal and pathogenic bacteria absence and may be unable to ensure the safety of the water. Further research, however, is required for a better understanding of the use of a Bacteroidales genetic marker as an indicator in water quality monitoring programs.     

Monitoring of potentially toxic cyanobacteria using an online multi-probe in drinking water sources

Toxic cyanobacteria threaten the water quality of drinking water sources across the globe. Two such water bodies in Canada (a reservoir on the Yamaska River and a bay of Lake Champlain in Québec) were monitored using a YSI 6600 V2-4 (YSI, Yellow Springs, Ohio, USA) submersible multi-probe measuring in vivo phycocyanin (PC) and chlorophyll-a (Chl-a) fluorescence, pH, dissolved oxygen, conductivity, temperature, and turbidity in parallel. The linearity of the in vivo fluorescence PC and Chl-a probe measurements were validated in the laboratory with Microcystis aeruginosa (r(2) = 0.96 and r(2) = 0.82 respectively). Under environmental conditions, in vivo PC fluorescence was strongly correlated with extracted PC (r = 0.79) while in vivo Chl-a fluorescence had a weaker relationship with extracted Chl-a (r = 0.23). Multiple regression analysis revealed significant correlations between extracted Chl-a, extracted PC and cyanobacterial biovolume and in vivo fluorescence parameters measured by the sensors (i.e. turbidity and pH). This information will help water authorities select the in vivo parameters that are the most useful indicators for monitoring cyanobacteria. Despite highly toxic cyanobacterial bloom development 10 m from the drinking water treatment plant's (DWTP) intake on several sampling dates, low in vivo PC fluorescence, cyanobacterial biovolume, and microcystin concentrations were detected in the plant's untreated water. The reservoir's hydrodynamics appear to have prevented the transport of toxins and cells into the DWTP which would have deteriorated the water quality. The multi-probe readings and toxin analyses provided critical evidence that the DWTP's untreated water was unaffected by the toxic cyanobacterial blooms present in its source water.     

Dry deposition study by using dry deposition plate and water surface sampler in Shalu, central Taiwan

This study presents the chemical composition of dry deposition by using dry deposition plate and water surfaces sampler during daytime and nighttime sampling periods at a near highway traffic sampling site. In addition, the characterization for mass and water soluble species of total suspended particulate (TSP), PM2.5 and PM10 were also studied at this sampling site during August 22 to October 31 of 2006 around central Taiwan. The samples collected were analyzed by using Ion Chromatography (DIONEX 100) for the ionic species analysis. Results of the particulate dry deposition fluxes are higher in the water surfaces sampler than that of the dry deposition plate. In other words, the results also indicated that water surface can absorb more ambient dry deposition inorganic pollutants than that of dry deposition plate in this study. The results obtained in this study indicated that the ionic species of Cl(-), NO3(-) and SO4(2-) occupied about average 60-70% downward flux out of total ionic species for either dry deposition plate or water surfaces sampler during August to October of 2006 at this near highway traffic sampling site.