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The ingestion of Ti-containing nanoparticles from drinking water has emerged as a concern in recent years. This study therefore aimed to characterize Ti-containing nanoparticles in water samples collected from four water treatment plants in Taiwan and to explore the challenges associated with measuring them at low levels using single particle-inductively coupled plasma mass spectrometry. Additionally, the study sought to identify the most effective processes for the removal of Ti-containing nanoparticles. For each water treatment plant, two water samples were collected from raw water, sedimentation effluent, filtration effluent, and finished water, respectively. Results revealed that Ti-containing nanoparticles in raw water, with levels at 8.69 μg/L and 296.8 × 103 particles/L, were removed by approximately 35% and 98%, respectively, in terms of mass concentration and particle number concentration, primarily through flocculation and sedimentation processes. The largest most frequent nanoparticle size in raw water (112.0 ± 2.8 nm) was effectively reduced to 62.0 ± 0.7 nm in finished water, while nanoparticles in the size range of 50–70 nm showed limited changes. Anthracite was identified as a necessary component in the filter beds to further improve removal efficiency at the filtration unit. Moreover, the most frequent sizes of Ti-containing nanoparticles were found to be influenced by salinity. Insights into the challenges associated with measuring low-level Ti-containing nanoparticles in aqueous samples provide valuable information for future research and management of water treatment processes, thereby safeguarding human health. [ABSTRACT FROM AUTHOR]

The potentially harmful effects of consuming potentially toxic elements (PTEs) and microplastics (MPs) regularly via drinking water are a significant cause for worry. This study investigated PTEs (Cd, Cu, Cr, Ni, Pd, Zn, Co), MPs, turbidity, pH, conductivity, and health risk assessment in the water treatment plant in Kielce, Poland. Zn had the highest concentrations throughout the water treatment facility, whereas Cd, Pb, and Co had lower concentrations (< 0.1 µg/L). The order of the concentrations among the specified PTEs was like Zn˃Cu˃Ni˃Cr˃Cd˃Pb and Co. The minimum turbidity was 0.34, and the maximum was 1.9 NTU. The range of pH in water samples was 6.51—7.47. The conductivity was 1,203—1,445 ms in water samples. These identified MPs were categorized into fiber and fragments. The color of these identified MPs was blue, red, black, green, and transparent. The minimum and maximum size of the MPs was 196 and 4,018 µm, while the average size was 2,751 ± 1,905 µm. The average concentration of MPs per liter of the water treatment plant was 108.88 ± 55.61. The elements listed are C, O, Na, Mg, Al, Si, K, Ca, and Ti. Fe and Zn were the predominant elements seen using EDX. HQ values of the PTEs were less than one for adults and children. The human health risk associated with all detected PTEs revealed that the HQ values exhibit a satisfactory degree of non-carcinogenic adverse health risk. HI values for adults and children age groups were less than one. In most water treatment samples, the carcinogenic value exceeds the threshold value of 10−6. The PTEs and MP concentrations in drinking water should be periodically monitored to minimize consumers' environmental pollution and health risks. [ABSTRACT FROM AUTHOR]

One of the main methods for determining coagulant dosage (CD) is the jar test. However, this method is expensive, time-consuming, and requires laboratory equipment. In this situation, machine learning, especially hybrid machine learning, is suitable to estimate the CD without requiring a jar test. To this end, a hybrid model based on a combination of the M5 and the gorilla troops optimizer (GTO) algorithm was introduced as the M5-GTO model. Nine different parameters, including raw water production (RWP), turbidity of water, Conductivity, TDS, Salinity, pH, water temperature (WT), SM, and O2, were also utilized as inputs in the CD modeling. The results of comparing the proposed model with the multiple linear regression, multiple nonlinear regression, artificial neural network, multivariate adaptive regression splines, M5 model tree, k-nearest neighbor, least-squares support vector machine, general regression neural network, and random forest (RF) showed that this model is more accurate in CD modeling. Besides, the M5-GTO well estimated the distribution of observational data. For example, the values of the mean absolute error, root mean square error (RMSE), relative RMSE, normalized RMSE, and correlation coefficient criteria for the M5-GTO were equal to 0.562, 1.172, 0.257, 0.170, and 0.967, which were up to 73%, 54.9%, 54.8%, 55%, and 4% are more accurate than LSSVM (worst algorithm), respectively. In addition, the M5 and RF algorithms were also in the second and third ranks. The partial dependence plots results showed that the RWP and WT had the most significant effects on CD changes. Increasing the RWP reduced the amount of CD, while increasing the WT increased the CD. The algorithm introduced in the present study has a high potential for modeling various parameters in water treatment plants. [ABSTRACT FROM AUTHOR]

Triclosan (TCS) has been increased in the water during the COVID-19 pandemic because it cannot remove by conventional water treatment. In addition, it can accumulate in the human body over time through long-term exposure. Therefore, the occurrence of TCS in the water treatment plant (WTP) and tap water, and its human health risk assessment through tap water ingestion, dermal absorption, and inhalation routes in Isfahan, Iran, were investigated. Moreover, spatial regression methods were used for the prediction of water quality parameters, TCS concentration, and total hazard quotient (HQ). The average TCS concentration in the influent and effluent of WTP and tap water was 1.6, 1.4, and 0.4 μg/L, respectively. Conventional WTP has low efficiency in the removal of TCS (12.6%) from water. The average values of total HQ for males were 7.79×10−5, 4.97×10−4, and 4.97×10−5 and for females were 3.31×10−5, 2.11×10−4, and 2.11×10−5 based on RfDEPA, RfDMDH, and RfDRodricks, respectively that were in the low-risk levels (HQ<1). Furthermore, TCS concentration in tap water and the ingestion rate of drinking water had the highest effect on the risk of TCS exposure from tap water. The non-carcinogenic health risk of TCS in water was low. The results of this study may be useful for promoting WTP processes to remove emerging pollutants. [ABSTRACT FROM AUTHOR]

Worldwide, there has been an increase in the presence of potentially toxic cyanobacterial blooms in drinking water sources and within drinking water treatment plants (DWTPs). The objective of this study is to validate the use of in situ probes for the detection and management of cyanobacterial breakthrough in high and low-risk DWTPs. In situ phycocyanin YSI EXO2 probes were devised for remote control and data logging to monitor the cyanobacteria in raw, clarified, filtered, and treated water in three full-scale DWTPs. An additional probe was installed inside the sludge holding tank to measure the water quality of the surface of the sludge storage tank in a high-risk DWTP. Simultaneous grab samplings were carried out for taxonomic cell counts and toxin analysis. A total of 23, 9, and 4 field visits were conducted at the three DWTPs. Phycocyanin readings showed a 93-fold fluctuation within 24 h in the raw water of the high cyanobacterial risk plant, with higher phycocyanin levels during the afternoon period. These data provide new information on the limitations of weekly or daily grab sampling. Also, different moving averages for the phycocyanin probe readings can be used to improve the interpretation of phycocyanin signal trends. The in situ probe successfully detected high cyanobacterial biovolumes entering the clarification process in the high-risk plant. Grab sampling results revealed high cyanobacterial biovolumes in the sludge for both high and low-risk plants. [ABSTRACT FROM AUTHOR]

The world lacks access to clean water for approximately a quarter million people. It is estimated that only 1% of the Earth's 2.5% water can be used. In developing countries, almost 80% of the three million early deaths can be attributed to drinking contaminated water due to organic and inorganic pollutants. Therefore, it is imperative that the treatment plants for surface water and waste water that supply treated water to the public is monitored regularly in order to ensure the quality of treated water. Water treatment plants performance can be evaluated using a variety of methods. However, these methods are absolute since they give equal weight to all parameters, which only partly reflects reality. This paper presents a hybrid decision making technique combining trapezoidal fuzzy BWM with trapezoidal fuzzy TOPSIS, whose goal is to determine the most beneficial alternative to improve the effectiveness of a water treatment plant (WTP). To evaluate the weights of criteria, the trapezoidal fuzzy BWM is employed, and TOPSIS is used to rank the alternatives. From the results, it is clearly found that water quality is the most responsible alternative. A sensitivity analysis validates the results obtained by the proposed study. [ABSTRACT FROM AUTHOR]

Input of wastewater-borne pollutants to the water bodies of the Russian Federation were investigated, and the use of fresh water in Russia was analyzed. The dynamics of the input of pollutants to the water bodies over the period from 1993 to 2020, as well as that of the use of fresh water for the needs of industry, household and drinking water supply, and irrigation and agricultural water supply in the territory of the Russian Federation were analyzed. Mathematical models that adequately reflect the changes in the investigated parameters were selected. The forecast of the inputs of wastewater-borne phenols, lead, mercury, chlorides, sulfates, nitrates, and fats and oils to the water bodies of the Russian Federation, as well as the forecast of the use of fresh water for the needs of household and drinking water supply, irrigation and agricultural water supply, and industry for the year 2025 were prepared. [ABSTRACT FROM AUTHOR]

Microplastics are an emerging threat and a big challenge for the environment. The presence of microplastics (MPs) in water is life-threatening to diverse organisms of aquatic ecosystems. Hence, the scientific community is exploring deeper to find treatment and removal options of MPs. Various physical, chemical and biological methods are researched for MPs removal, among which few have shown good efficiency in the laboratory. These methods also have a few limitations in environmental conditions. Other than finding a suitable method, the creation of legal restrictions at a governmental level by imposing policies against MPs is still a daunting task in many countries. This review is an effort to place all effectual MP removal methods in one document to compare the mechanisms, efficiency, advantages, and disadvantages and find the best solution. Further, it also discusses the policies and regulations available in different countries to design an effective global policy. Efforts are also made to discuss the research gaps, recent advancements, and insights in the field. [ABSTRACT FROM AUTHOR]

In this investigation, we have put forward a novel multi-criteria decision-making (MCDM) technique, intuitionistic fuzzy best–worst method (intuitionistic fuzzy BWM). Moreover, we have designed a novel hybrid MCDM technique called intuitionistic fuzzy best–worst analytic hierarchy process (intuitionistic fuzzy BWAHP) which is the amalgamation of intuitionistic fuzzy BWM and analytic hierarchy process (AHP). In this study, BWM finds its use to evaluate the weightage (or priority value) of criteria, and using AHP, the local weights of alternatives are decided. The proposed technique is utilized to recognize the most significant alternative (or indicator), for the efficiency of a water treatment plant. Observing the result, it can be said that the 'water quality' is the most responsible alternative. The consistency ratio value which is found by our proposed method is less, compared to the existing BWM and fuzzy BWM techniques. Finally, using a comparative study and sensitivity analysis, we verify the findings produced by our proposed method. [ABSTRACT FROM AUTHOR]

Water treatment plants (WTPs) are extremely important in basic sanitation services because of their association with human health by producing safe drinking water. Thus, their proper operation is of utmost relevance and has led to the development of distinct performance evaluation methodologies. Direct filtration is a leading technology applied in WTPs. However, although it costs less than conventional treatment, it might also be less flexible and robust. To evaluate performance with data from real-scale WTPs with direct filtration systems, the use of a statistical analysis methodology for turbidity, apparent color, and pH data from raw and effluent water is proposed. Reliability analysis, a probabilistic-based methodology, was applied for turbidity alongside evaluating the compliance of the treated water with different potability standards. The parameters pH (between 6.0 and 8.0) and apparent color (< 15 HU) showed almost complete compliance (> 99%). Reliability analysis could not be applied for apparent color and pH, while apparent color did not adhere to the lognormal distribution frequency. The turbidity results show high variability in the coefficients of variation and reliability among various plants and a general difficulty in complying with stricter standards, such as the 0.1 and 0.3 NTU defined by the United States Environmental Protection Agency. All WTP studies show a 95% compliance with the World Health Organization standard of 5.0 NTU. A higher value for the coefficient of reliability and a lower value for the variation coefficient could indicate a more stable process, regardless of the treated water quality, highlighting the relevance of applying combined methods for performance evaluation, such as compliance with established standards. [ABSTRACT FROM AUTHOR]

Water treatment plants play a major role in the cycle of water recovery and reuse. Besides the benefits of water treatment plants, they have a great impact on the environment, social life, economy, and natural habitats. In this sense, decision-makers should effectively plan the construction and operational activities of plants, taking into account the expectations of users. Growing public expectations about water treatment plants increase the pressures on investors and government managers. In this study, we focus on defining and determining the weights of public expectations from water treatment plants and handle as a multi-criteria decision-making problem. A two-level hierarchical model is structured to evaluate public expectations from water treatment plants as model criteria. For the problem, a literature review is performed to search the main criteria. The most suitable criteria for the problem are determined using experts' opinions. Then, the sub-criteria are determined. Experts' evaluations are collected by face to face interviews. These evaluations are consolidated and finalized via the modified Delphi method. Trapezoidal Type-2 Fuzzy Analytic Hierarchy Process (T2F-AHP) is employed to determine criteria weights using results obtained by the modified Delphi method. A sensitivity analysis is performed to show the reliability of the proposed methodology. A comparison is also performed between the Analytic Hierarchy Process (AHP) and the proposed methodology. The results of this study can be used as a guide to develop public strategies about water treatment plants. Finally, conclusions and future directions of this work are given. [ABSTRACT FROM AUTHOR]

Surface water treatment plants are major energy consumers in all countries. In recent years, the increasing scarcity of fossil fuels and the growth in demand for energy resulting from the needs of development have prompted optimisation of the use of energy. SWTPs are responsible for the supply of treated water to consumers. Although a significant amount of the total energy produced is consumed by WTPs, the utilisation of this resource is variable, and sufficient amounts of it remain unutilised or wasted in the treatment process. Energy resources supplied to a WTP must be optimally allocated. At present, no mechanism exists to ensure this, and allocation is performed as and when it is needed, with no regulation or control. As a result, much energy is returned unutilised. This results in excess expenditures and affects carbon emissions from the plant because both too much and too little utilisation of energy in running water treatment equipment can result in the generation of greenhouse gases to the atmosphere. Unnecessary consumption of energy reduces its availability for other users. Thus, the economy, the environment, and social well-being are affected by the non-optimal utilisation of energy. This problem is common to all parts of the world but is especially acute in developing countries. Lack of intelligent allocation methods compromises the sustainability not only of the plant but also of the dependent population. Here, nature-based optimisation algorithms (OAs) and a modified analytical hierarchy process (mAHP), an objective multi-criteria decision-making method, were utilised to conduct intelligent, automatic allocation of energy among elements of wastewater treatment plants (WTPs). OAs are used to weight elements according to their relative capacity to ensure reliability and restrict risk to plants (resulting in a reliability–risk index); energy is allocated accordingly using mAHP. Tested at a working WTP in India, it minimised energy wastage down to 0.037% of total energy. This is the first attempt to combine mAHP and aggregated output from two OAs to optimise energy use in a WTP (based on the novel reliability–risk index). Our method builds on the concepts of multi-criteria decision making and metaheuristics optimisation algorithms to develop a new procedure for cognitive allocation of energy ensuring optimal performance while minimising the use of energy. Our decision support system can help maximise productivity, and safeguard sustainability, of plants and their stakeholders. However, time-dependent, nonlinear dynamics in continuously operating WTPs should be tested in future work. [ABSTRACT FROM AUTHOR]

The bromide concentration in water source (WS) of Yancheng City in China increased unexpectedly due to industrial discharge and saltwater intrusion, which leads to the formation of trihalomethane (THMs) in finished water of water treatment plants (WTP), especially brominated THMs. In Yancheng City, drinking water is supplied by WTP1 and WTP2, primarily sourced by WS1 and WS2, respectively. In this paper, the seasonal variations of bromide in WS1 and WS2 and THMs species in WTP1 and WTP2 were analyzed and compared. The effects of bromide in WS on THMs formation in finished water of WTP in terms of bromine substitution factor (BSF) were simulated by statistical linear model. Although the THMs concentrations in WTP1 were approximate to that in WTP2, the brominated THMs concentrations in WTP1 were higher than that in WTP2 due to higher bromide concentration in WS1 than WS2. The cancer risk analysis indicated that THMs' species of DBCM is the dominant THMs for WTP1 as well as WTP2, which can provide more information for WTPs with higher bromide concentration in water source. [ABSTRACT FROM AUTHOR]

A reliable forecasting model for each Water Treatment Plant (WTP) influent characteristics is useful for controlling the plant’s operation. In this paper Auto-Regressive Integrated Moving Average (ARIMA) and Neural Network Auto-Regressive (NNAR) modeling techniques were applied on a WTP’s influent water characteristics time series to make some models for short-term period (to seven days ahead) forecasting. The ARIMA and NNAR models both provided acceptable generalization capability with R2s ranged from 0.44 to 0.91 and 0.45 to 0.92, respectively, for chloride and temperature. Although a more prediction performance was observed for NNAR in comparison with ARIMA for all studied series, the forecasting performance of models was further examined using Time Series Cross-Validation (TSCV) and Diebold-Mariano test. The results showed ARIMA is more accurate than NNAR for forecasting the horizon-daily values for CO2, Cl and Ca time-series. Therefore, despite of the good predictive performance of NNAR, ARIMA may still stands as better alternative for forecasting task of aforementioned series. Thus, as a general rule, not only the predictive performance using R2 statistic but also the forecasting performance of a model using TSCV, are need to be examined and compared for selecting an appropriate forecasting model for WTP’s influent characteristics. [ABSTRACT FROM AUTHOR]

Despite being aware of a number of potential natural states, the decision-maker lacks the expertise to calculate their probability. The uncertainty surrounding input parameters are typically one of the biggest challenges in making decisions. Due to the possibility that ambiguity can lead to unreliable results, making decisions becomes more difficult. In order to overcome this challenge, the proposed hybrid method combines the trapezoidal fuzzy best–worst method (TrFBWM) and single-valued neutrosophic fuzzy measurement of alternatives and ranking according to the compromise solution (SVNF-MARCOS). A major contribution of this study is the development of a new hybrid method, namely, TrF BWM-SVNF MARCOS. The weights of criteria are determined by TrFBWM, while the rank of alternatives is determined by SVNF-MARCOS. The integrated method is used to identify the most important indicator (or alternative) for a water treatment plant's effectiveness. Results show that the choice for 'water quality' is the most responsible indicator. The conclusions produced by the suggested method are validated through a comparative study, sensitivity analysis, and scenario analysis. [ABSTRACT FROM AUTHOR]

Taste and odor (T&O) problems in surface water supplies attract growing environmental and ecological concerns. In this study, 10 T&O compounds, 2-methylisoborneol (2-MIB), geosmin, β-ionone, 2-isopropyl-3-methoxypyrazine (IPMP), 2-isobutyl-3-methoxypyrazine (IBMP), 2,4,6-trichloroanisole (2,4,6-TCA), 2,3,6-trichloroanisole (2,3,6-TCA), 2,3,4-trichloroanisole (2,3,4-TCA), 2,4,6-tribromoanisole (2,4,6-TBA), and trans-2, cis-6-nonadienal (NDE) were investigated in 13 water supply reservoirs and 2 water treatment plants (WTPs) in S City of China. 2-MIB, geosmin, and β-ionone were detected in most of the reservoirs and WTPs. The highest concentrations in reservoirs reached 196.0 ng L for 2-MIB, 11.4 ng L for geosmin, and 39.7 ng L for β-ionone. Canonical correspondence analysis (CCA) was used to examine the relationship between the 3 T&O compounds and environmental parameters of the reservoirs. The results showed that TP was strongly positively correlated with 2-MIB in wet season and negatively correlated in dry season. It was suggested that controlling nutrient (TP, TN/TP, and NH-N) inputs was required for better management of drinking water reservoirs. Furthermore, the maximum concentrations in raw water of WTPs was kept at 82.1 ng L for 2-MIB, 5.6 ng L for geosmin, and 66.1 ng L for β-ionone. β-Ionone could not be detected in the post-filtration and finished water of two WTPs, and both 2-MIB and geosmin significantly decreased in the water of XWTP. It was indicated that T&O compounds could be removed partly or completely by the filtration of conventional treatment processes. [ABSTRACT FROM AUTHOR]

Massive utilization of bisphenol A (BPA) in the industrial production of polycarbonate plastics has led to the occurrence of this compound (at μg/L to ng/L level) in the water treatment plant. Nowadays, the presence of BPA in drinking water sources is a major concern among society because BPA is one of the endocrine disruption compounds (EDCs) that can cause hazard to human health even at extremely low concentration level. Parallel to these issues, membrane technology has emerged as the most feasible treatment process to eliminate this recalcitrant contaminant via physical separation mechanism. This paper reviews the occurrences and effects of BPA toward living organisms as well as the application of membrane technology for their removal in water treatment plant. The potential applications of using polymeric membranes for BPA removal are also discussed. Literature revealed that modifying membrane surface using blending approach is the simple yet effective method to improve membrane properties with respect to BPA removal without compromising water permeability. The regeneration process helps in maintaining the performances of membrane at desired level. The application of large-scale membrane process in treatment plant shows the feasibility of the technology for removing BPA and possible future prospect in water treatment process. [ABSTRACT FROM AUTHOR]

The objective of this study is to compare the prevalence of rotaviruses groups A and C in Egyptian children and aquatic environment. From 110 stool specimens of children with acute diarrhea and using RT-PCR, 35 samples (31.8 %) were positive for human rotavirus group A and 15 samples (13.6 %) were positive for human rotavirus group C. From 96 samples collected from Zenin wastewater treatment plant over a 2-year period (November 2009-October 2011) and using RT-PCR, rotavirus group A was detected in (4/24) 16.7 %, (5/24) 20.8 %, (4/24) 16.7 %, and (4/24) 16.7 %, while rotavirus group C was detected in (2/24) 8.3 %, (3/24) 12.5 %, (3/24) 12.5 %, and (0/24) 0 % in raw sewage, after primary sedimentation, after secondary sedimentation, and after final chlorination, respectively. Moreover, from 96 samples collected from El-Giza water treatment plant over a 2-year period (November 2009-October 2011), rotavirus group A was detected in (7/24) 29.2 %, (6/24) 25 %, (5/24) 20.8 %, and (3/24) 12.5 %, while rotavirus group C was detected in (3/24) 12.5 %, (1/24) 4.2 %, (1/24) 4.2 %, and (0/24) 0 % in raw Nile water, after sedimentation, after sand filtration, and after final chlorination, respectively. Using SYBR Green real-time RT-PCR, the number of human rotavirus group A genome or infectious units was higher than rotavirus group C. VP6 sequence analysis of the RT-PCR positive rotavirus group C samples revealed that four clinical specimens and three environmental samples showed similar sequences clustered with Moduganari/Human Nigerian strain AF 325806 with 98 % homology, and two clinical specimens and one environmental sample showed similar sequences clustered with Dhaka CB/Human Bangladesh strain AY 754826 with 97 % homology. [ABSTRACT FROM AUTHOR]

The importance of finished water quality from water treatment plants (WTP) is manifold due to its direct impact on consumer health. It is observed from recent studies that change in climatic pattern can affect the concentration of various water quality parameters (WQP) at source itself and change in source water quality may adversely affect the efficiency of WTP also. In the present study Analytical Hierarchy Process (AHP) is utilized to prioritize WQP based on its vulnerability towards climate change. A feed-forward, fully-connected, three-layer neural network model is developed to compute the Vulnerability Index (VI) with the priority vectors of the WQP as determined by AHP approach. The model is utilized to predict the overall vulnerability of three different surface water sources namely river water, lake water and wetland water. The proposed AHP-Neural network model shows that River water is least affected due to climatic abnormalities with minimum VI value and may be considered as the best source water for WTP among the three considered alternatives. The sensitivity analysis of the new model is also carried out. Hence, this proposed model may become a reliable tool to the water supply engineers in the coming days of climate change. [ABSTRACT FROM AUTHOR]

In the city of Islamabad, drinking water quality at the treatment plant usually meets the country's hygienic standards. To provide protection against recontamination, concentration of total residual chlorine in treated water is maintained at levels from 0.2 to 0.5 mg/L in tap water of consumer end and rechlorination is not practiced. Some areas of the city have very low residual chlorine at consumer taps and little protection against secondary microbial contamination of water in pipelines. The analysis of the drinking water quality in the distribution network was carried out during March-August 2008 by collecting 80 water samples in triplicate from treatment plants and remaining of the residential taps. The collected samples were examined for total chlorine, free chlorine residual, chloramines, trihalomethanes, total organic carbon, total coliforms, fecal coliforms and turbidity. The water samples collected from sector F-6, F-10 and F-11 were found to be free of fecal contamination while in sectors E-7 and F-7 contamination was found at few stations among all the collected samples. Water samples collected from sectors F-8, G-9, G-8, I-9, and I-8 were highly contaminated with E. coli, at most of the sampling stations. Trihalomethanes and chloroform were detected at high concentrations in sector F-8. [ABSTRACT FROM AUTHOR]

In this study, a SYBR green quantitative real-time PCR was developed to quantify and detect the Legionella spp. in various environmental water samples. The water samples were taken from watershed, water treatment plant, and thermal spring area in Taiwan. Legionella was detected in 13.6 % (24/176), and the detection rate for river water, raw drinking water, and thermal spring water was 10, 21.4, and 16.6 %, respectively. Using real-time PCR, concentration of Legionella spp. in detected samples ranged between 9.75 × 10 and 3.47 × 10 cells/L in river water, 6.92 × 10 and 4.29 × 10 cells/L in raw drinking water, and 5.71 × 10 and 2.12 × 10 cells/L for thermal spring water samples. The identified species included Legionella pneumophila (20.8 %), Legionella jordanis (4.2 %), Legionella nautarum (4.2 %), Legionella sp. (4.2 %), and uncultured Legionella sp. (66.6 %). The presence of L. pneumophila in aquatic environments suggested a potential public health threat that must be further examined. [ABSTRACT FROM AUTHOR]

N-nitrosodimethylamine (NDMA) and several other N-nitrosamines have been detected as disinfection by-products in drinking waters in many countries around the world. An ultra-performance liquid chromatographytandem mass spectrometry method with solid phase extraction sample preparation was developed to study the occurrence of N-nitrosamines in several water treatment plants and distribution systems in China. Isotope labeled N-nitrosodi- n-propylamine-d14 (NDPA-d14) was selected as the internal standard for quantification. The solid phase extraction procedures including pH, enrichment process and MS/MS parameters including capillary voltage, cone gas flow, cone voltage, collision energy were optimized to give average recoveries of 26% to 112% for nine N-nitrosamine species. The instrument detection limits were estimated to range from 0.5 to 5 μg·L for the nine N-nitrosamine species. NDMA and several other N-nitrosamines were found at fairly high concentrations in several water treatment plants and distribution systems. NDMA was found in all locations, and the highest concentrations in cities B, G, T, and W were 3.0, 35.7, 21.3, and 19.7 ng·L, respectively. A wide range of N-nitrosamines concentrations and species were observed in different locations. Higher concentrations of N-nitrosamines were detected in distribution systems that were further away from the treatment plants, suggesting that the contact time between the residual disinfectant and natural organic matter may play an important role in the formation of these compounds. [ABSTRACT FROM AUTHOR]

The objective of this study is to assess the risk of insufficient water supply posed by high-turbidity water. Several phenomena can pose risks to the sufficiency of a water supply; this study concerns risks to water treatment plants from particular properties of rainfall and raw water turbidity. High-turbidity water can impede water treatment plant operations; rainfall properties can influence the degree of soil erosion. Thus, water turbidity relates to rainfall characteristics. Exceedance probabilities are presented for different rainfall intensities and turbidities of water. When the turbidity of raw water is higher than 5,000 NTU, it can cause operational problems for a water treatment plant. Calculations show that the turbidity of raw water at the Ban-Sin water treatment plant will be higher than 5,000 NTU if the rainfall intensity is larger than 165 mm/day. The exceedance probability of high turbidity (turbidity >5,000 NTU) in the Ban-Sin water treatment plant is larger than 10%. When any water treatment plant cannot work regularly, its ability to supply water to its customers is at risk. [ABSTRACT FROM AUTHOR]

We studied periphytic algal biofilms that formed in the sedimentation basins (SBs) of a water treatment plant (WTP) in South Korea. The submerged cement wall of the basin was heavily coated by mats of algal filaments, which resembled a small sheet of carpet. These biofilms were more common in the dry season (spring and autumn), than the rainy season (summer) and winter. Oscillatoria and Phormidium (Cyanobacteria) were predominant in the biofilm of the SB walls, where the water was stagnant. Lyngbya (Cyanobacteria) and Oedogonium (Chlorophyta) were dominant on the walls of overflow weirs and troughs of the SB, where there was a substantial current. As the biofilm matured, the assemblages were detached from the walls and floated as 'scum' on the water, causing contamination. Many factors control the development of cyanobacterial biofilms on the walls of WTPs, including the nutrient levels of the raw water, and interstitial or pore water of biofilm assemblages, temperature, and irradiance. We isolated and grew unialgal cultures of the five most important cyanobacteria of the WTP biofilm. Oscillatoria splendida strongly produced the musty odor from the cultures. We suggest implementation of antifouling strategies to reduce the formation of biofilm on the wall and contamination of water supplies. [ABSTRACT FROM AUTHOR]

This paper reports the results of the in situ application of diffusive gradients in thin-films (DGT) passive samplers for trace metals (Cd, Co, Cr, Cu, Ni, Pb and Zn) monitoring in transboundary Udy and Lopan rivers of the Seversky Donets watershed in the Kharkiv region (Ukraine), which has a long history of industrial development. The research discusses potential sources of DGT-measured labile metals in water and seasonal variations. Our results demonstrate the application of DGT for identifying and measuring labile metal concentrations in contrasted climate conditions (cold snowy winter and hot summer) and appropriateness of such a tool for continuous water monitoring with the presence of an ice cover. Results show that DGT-measured concentrations of most of trace metals were much higher downstream of the wastewater treatment plants discharges than upstream; thus wastewater treatment plants seemed not able to reduce or to remove trace metals' contaminations and become major sources of pollutants in the studied rivers. The calculation of the average metal fluxes based on the DGT-measured concentrations confirmed that the urban wastewater discharges significantly contribute to the metal fluxes into the Udy and the Lopan rivers during both low-flow and high-flow periods. Compared to the wastewaters inputs to the rivers, the transboundary effect is limited, but should be taken into account as the origin of some metals is from sources located on the adjacent Russian territory. [ABSTRACT FROM AUTHOR]

method that utilizes solid-phase microextraction (SPME) coupled with gas chromatography (GC) and chemical ionization tandem mass spectrometry (MS/MS) was developed for analyzing a group of emerging pollutants, N-nitrosamines, in water. The developed analytical method requires a water sample of less than 5 ml and only 1.5 h for complete analysis. The method detection limits for N-nitrosodimethylamine (NDMA), N-nitrosodiethylamine, and N-nitrosodi- n-propylamine were in the range of 3.2 to 3.5 ng/l; for N-nitrosomorpholine, it was 15.2 ng/l. The method was successfully employed to measure the N-nitrosamine concentration at trace levels of nanogram per liter in four water treatment plants (WTPs) and one water distribution system. In the WTPs, only NDMA was detected in the treatment processes. Within the treatment train, NDMA was observed after chlorination. The level of NDMA significantly declined after slow sand filtration due presumably to microbial degradation. The NDMA concentration collected from consumer tap water was about 40% higher on average than that in the finished water. The excellent performance of the SPME/GC/MS/MS method in various water matrices as well as the shorter analysis time and smaller sample volume compared to currently used extraction techniques makes it an alternative means for the analysis of N-nitrosamine in drinking water, wastewater, and laboratory research with small reactors. [ABSTRACT FROM AUTHOR]

Water quality monitoring exercise was carried out with water quality index (WQI) method by using water characteristics data for bore wells and a water treatment plant in Delhi city from December 2006 to August 2007. The water treatment plant received surface water as raw water, and product water is supplied after treatment. The WQI is used to classify water quality as excellent, good, medium, bad, and very bad. The National Sanitation Foundation WQI procedure was used to calculate the WQI. The index ranges from 0 to 100, where 100 represents an excellent water quality condition. Water samples were collected monthly from a bore well in Nehru Camp (site 1), a bore well in Sanjay Gandhi pumping station (site 2), and water treatment plant in Haiderpur (site 3). Five parameters were analyzed, namely, nitrate, pH, total dissolved solids, turbidity, and temperature. We found that the WQI was around 73–80 in site 3, which corresponds to “good,” and it decreased to 54.32–60.19 and 59.93–70.63 in site 1 and site 2, respectively, indicating that these bore wells were classified as “medium” quality. [ABSTRACT FROM AUTHOR]

Concern over the presence of fecal coliform in public drinking water supplies has been expressed in recent years in Pakistan since it has been regarded as pathogenic organism of prime importance in gastroenteritis. Two major drinking water distribution systems in the Cantt area of Rawalpindi district covering the Westridge and Tench areas was monitored over a 2-month period to determine the prevalence of fecal coliform and chlorine residual. The collected samples were examined for total chlorine, free chlorine residual, chloramines, total coliforms, fecal coliforms, and turbidity. The drinking water quality monitoring in the distribution network was performed by collecting samples from water source, overhead reservoir, and residential taps. In the Westridge area, total chlorine varied from the lowest value of 0.27 mg/L at Station # W-5 to the highest value of 0.42 mg/L at Station # W-2, total coliforms varied from 1.1 to 3.6 most probable number (MPN)/100 mL with presence of Escherichia coli in all samples, total dissolved solids (TDS) ranged from 199.5 to 205 mg/L, conductivity fluctuated between 399 and 411 μS/cm, and turbidity varied from 0.43 to 0.73 NTU. In the Tench area, the value of total chlorine ranged from 0.14 mg/L at Station # T-7 to 0.55 mg/L at Station # T-1. Total coliform varied from 3.6 to 5.1 MPN/100 mL and fecal coliform were detected at all the stations except at Station # T-1. TDS ranged from 201.4 to 257 mg/L, conductivity varied from 343 to 513 μS/cm, and turbidity ranged between 0.66 and 1.55 NTU. It is recommended to the respective agencies to ensure that the chlorine residual is available at consumer end. [ABSTRACT FROM AUTHOR]

The present study investigates the chemical composition of Ma'an Wastewater Treatment Plant in south Jordan. Samples of effluent of this plant were collected over 1 year period. All samples were analyzed for pH, conductivity, major ions (Cl-, NO3- , HCO3- , SO42- , PO43- , Na+, K+, Ca2+ and Mg2+) and trace metals B, Fe, Cu, Zn, Cd and Pb. The pH value ranges from 6.79 to 8.15 with a median value of 7.39 ± 0.32. The water quality was characterized by its high salinity hazard (C3) and low sodium hazard (S1) which can be considered as marginal for human consumption. Moreover, concentrations of tracemetals in treated wastewater were found to be low and within guidelines for irrigation water due to low level of industrialization activities in the study area. Generally, the result of this study suggests that the treated wastewater is suitable for irrigational purposes, while these effluents can be considered as possible additional resources for irrigation in Jordan. [ABSTRACT FROM AUTHOR]

In this research, genetic programming and multivariate statistical analysis techniques have been applied for decision support on the coagulant dosage and the mixing ratio as two kinds of coagulants have been injected at the same time in the coagulating sedimentation process of water treatment. The coagulant dosage has typically been determined through the Jar-test, which requires a long experiment time in a field-water treatment plant. It is difficult to efficiently determine the coagulant dosage since water quality changes with time. As there are no human experts who have sufficient knowledge and experience in the field, coagulants may be injected with an improper mixing ratio, which causes poor performance in the coagulating sedimentation process. In this study, a model for the approximation of coagulant dosage has been developed using genetic programming (GP). The performance of this model was evaluated through validation. A guideline on the optimal mixing ratio between PACS (Poly Aluminum Chloride Silicate) and PAC (Poly Aluminum Chloride) has been provided through statistical analysis. [ABSTRACT FROM AUTHOR]

Wastewater reuse can significantly reduce environmental pollution and save the water sources. The study selected Cheng-Ching Lake water treatment plant in southern Taiwan to discuss the feasibility of wastewater recycling and treatment efficiency of wastewater treatment units. The treatment units of this plant include wastewater basin, sedimentation basin, sludge thickener and sludge dewatering facility. In this study, the treatment efficiency of SS and turbidity were 48.35–99.68% and 24.15–99.36%, respectively, showing the significant removal efficiency of the wastewater process. However, the removal efficiencies of NH3–N, total organic carbon (TOC) and chemical oxygen demand (COD) are limited by wastewater treatment processes. Because NH3–N, TOC and COD of the mixing supernatant and raw water are regulated raw water quality standards, supernatant reuse is feasible and workable during wastewater processes at this plant. Overall, analytical results indicated that supernatant reuse is feasible. [ABSTRACT FROM AUTHOR]

Purpose: Coagulation and flocculation are the prominent processes and unit-operations in water treatment plants. One of the most challenging operations in water treatment process is determining of the coagulant dose.The Jar-test method is usually used to determine the coagulant dose. Considering that this traditional method is time consuming, associated with human error and highly affected by raw water quality fluctuations. In this study, artificial fuzzy neural network (ANFIS) according to subtractive clustering (SUB) method was applied in order to determine the optimal dose of coagulant in the water treatment plants.Adopting SUB method tend to moderate the number of rules and the interconnections besides enhancing the model responsibility and smart model recognition. The amount of pH, turbidity of raw water influent, alkalinity, temperature, and electrical conductivity were collected as input data.The results of modeling by ANFIS with correlation coefficients of 0.85 and 0.84 and RMSE 1.32 and 1.83, respectively, for alum and polyaluminum chloride (PAC) coagulant dose, indicated that ANFIS is an effective method for determination of the optimal coagulation dose in the water treatment plant.Method: Coagulation and flocculation are the prominent processes and unit-operations in water treatment plants. One of the most challenging operations in water treatment process is determining of the coagulant dose.The Jar-test method is usually used to determine the coagulant dose. Considering that this traditional method is time consuming, associated with human error and highly affected by raw water quality fluctuations. In this study, artificial fuzzy neural network (ANFIS) according to subtractive clustering (SUB) method was applied in order to determine the optimal dose of coagulant in the water treatment plants.Adopting SUB method tend to moderate the number of rules and the interconnections besides enhancing the model responsibility and smart model recognition. The amount of pH, turbidity of raw water influent, alkalinity, temperature, and electrical conductivity were collected as input data.The results of modeling by ANFIS with correlation coefficients of 0.85 and 0.84 and RMSE 1.32 and 1.83, respectively, for alum and polyaluminum chloride (PAC) coagulant dose, indicated that ANFIS is an effective method for determination of the optimal coagulation dose in the water treatment plant.Results: Coagulation and flocculation are the prominent processes and unit-operations in water treatment plants. One of the most challenging operations in water treatment process is determining of the coagulant dose.The Jar-test method is usually used to determine the coagulant dose. Considering that this traditional method is time consuming, associated with human error and highly affected by raw water quality fluctuations. In this study, artificial fuzzy neural network (ANFIS) according to subtractive clustering (SUB) method was applied in order to determine the optimal dose of coagulant in the water treatment plants.Adopting SUB method tend to moderate the number of rules and the interconnections besides enhancing the model responsibility and smart model recognition. The amount of pH, turbidity of raw water influent, alkalinity, temperature, and electrical conductivity were collected as input data.The results of modeling by ANFIS with correlation coefficients of 0.85 and 0.84 and RMSE 1.32 and 1.83, respectively, for alum and polyaluminum chloride (PAC) coagulant dose, indicated that ANFIS is an effective method for determination of the optimal coagulation dose in the water treatment plant.Conclusions: Coagulation and flocculation are the prominent processes and unit-operations in water treatment plants. One of the most challenging operations in water treatment process is determining of the coagulant dose.The Jar-test method is usually used to determine the coagulant dose. Considering that this traditional method is time consuming, associated with human error and highly affected by raw water quality fluctuations. In this study, artificial fuzzy neural network (ANFIS) according to subtractive clustering (SUB) method was applied in order to determine the optimal dose of coagulant in the water treatment plants.Adopting SUB method tend to moderate the number of rules and the interconnections besides enhancing the model responsibility and smart model recognition. The amount of pH, turbidity of raw water influent, alkalinity, temperature, and electrical conductivity were collected as input data.The results of modeling by ANFIS with correlation coefficients of 0.85 and 0.84 and RMSE 1.32 and 1.83, respectively, for alum and polyaluminum chloride (PAC) coagulant dose, indicated that ANFIS is an effective method for determination of the optimal coagulation dose in the water treatment plant. [ABSTRACT FROM AUTHOR]

The goal of the flocculation process is to change the particle size distribution to best suit the subsequent processes. Although several methods exist to evaluate the flocculation process, no single universally accepted method has yet to be developed. The purpose of this paper is to present an experience whereby particle counting was used in the evaluation of the flocculation process. A commercially available on-line continuous particle counter has been used in evaluating the design and the operation of this process at two conventional Water Treatment Plants. The evaluation is based on particle dynamics, i.e., the change of the number of small and large particles. Some design deficiencies in the flocculation process have been identified from this method, and thus some operational parameters are suggested for optimum performance. The optimum condition may be site specific, and the method presented in this paper will be beneficial in the evaluation of the flocculation process at other water treatment plants. [ABSTRACT FROM AUTHOR]

Disposal of sludges generated at water treatment plants (WTPs) and wastewater treatment plants (WWTPs) located in highly urbanized regions challenges water industry. Legal restrictions based on public health and sustainability push managers forward in order to find beneficial use markets rather than deficit and high-cost landfilling. A GIS-based linear optimization method for sludge management was firstly proposed. The metropolitan area of Campinas belongs to the watershed of the rivers Piracicaba, Capivari, and Jundiaí (PCJ), which comprises 76 municipalities, representing over 5 million urban consumers, supplied by 100 WTPs and 116 WWTPs. An assessment of soils feasibility for WWTP sludge reception was carried out. The beneficial uses assumed to be the best for WTP and WWTP sludges were, respectively, addition in the industrial process of ceramic bricks manufacture, and application on sugarcane crop areas for ethanol production. Three scenarios were set for sludges from WTPs and also for WWTPs. Those scenarios represented maximum, intermediate, and minimum reception capability for each reception location or area. Ceramic industries located within PCJ watershed showed to be capable of receptioning the total amount of WTP sludges, if a minimum 2% mass/mass replacement of raw materials (mainly clay) is provided. There are plenty of feasible areas for WWTP sludge application; thus, sludge agronomic quality and farmers’ acceptance constitute the only steps to climb. This paper brings an innovative tool regarding sludge management which may be useful to decision-makers, especially wherever several sources and reception areas are playing on game board. The proposed method can be applied at different locations and for other sludge uses. [ABSTRACT FROM AUTHOR]