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Water treatment technologies are striving to retain their ecological and economic viability despite the rising demand, conventional infrastructure, financial constraints, fluctuating climatic patterns, and highly stringent regulations. This study evaluates the lifecycle environmental impact of urban water treatment systems within the two densely populated South Asian municipalities of Islamabad and Rawalpindi, Pakistan. The scope of this study includes a process-based Life Cycle Assessment (LCA) of the entire water treatment system, particularly the resources and materials consumed during the operation of the treatment plant. The individual and cumulative environmental impact was assessed based on the treatment system data and an in-depth lifecycle inventory analysis. Other than the direct emissions to the environment, the electricity used for service and distribution pumping, coagulant use for floc formation, chlorine gas used for disinfection, and caustic soda used for pH stabilization were the processes identified as the most significant sources of emissions to air and water. The water distribution consumed up to 98% of energy resources. The highest global warming impacts (from 0.3 to 0.6 kg CO2 eq./m3) were assessed as being from the coagulation and distribution processes due to extensive electricity consumption. Direct discharge of the wash and wastewater to the open environment contributed approximately 0.08% of kg-N and 0.002% of kg-P to the eutrophication potential. The outcome of this study resulted in a thorough lifecycle inventory development, including possible alternatives to enhance system sustainability. A definite gap was identified in intermittent sampling at the treatment systems. However, more stringent sampling including the emissions to air can provide a better sustainability score for each unit process. [ABSTRACT FROM AUTHOR]

The water treatment system for hemodialysis (HD) is used to treat multiple patients requiring HD simultaneously. This system requires a large amount of purified reverse osmosis (RO) water. However, a major drawback of this method is the formation of biofilms in dialysate pathways. The purpose of this study was to investigate the efficacy of NOC 18, a nitric oxide (NO) donor that can be used at neutral pH, in disinfecting the RO water pathway. Silicone tubes were obtained from the terminal sites of two different HD units. The biofilm coverage and mean biofilm thickness on the tube lumen were evaluated by scanning electron microscopy. The results demonstrated that treatment with NOC 18 alone and in conjunction with sodium hypochlorite reduced biofilm coverage and mean biofilm thickness. Thus, NO donor is a potential disinfectant that enhances bacterial dispersion from biofilms formed on the silicone tube lumen and reduces biofilm coverage and thickness on the RO water pathway at neutral pH. Furthermore, combined disinfection with the NO donor and sodium hypochlorite might enhance biofilmremoval efficacy in clinical practice. HIGHLIGHTS NO donor reduces microbial in the RO water pathway for hemodialysis.; NOC 18 reduces biofilm coverage and thickness under neutral pH conditions.; Disinfection ability of NO donor alone is weak.; NO donor and sodium hypochlorite together increase biofilm removal.; The cleaning method using NOC 18 is environmentally friendly.;

The protection of critical infrastructure such as water treatment and water distribution systems is crucial for a functioning economy. The use of cyber-physical systems in these systems presents numerous vulnerabilities to attackers. To enhance security, intrusion detection systems play a crucial role in limiting damage from successful attacks. Machine learning can enhance security by analysing data patterns, but several attributes of the data can negatively impact the performance of the machine learning model. Data in critical water system infrastructure can be difficult to work with due to their complexity, variability, irregularities, and sensitivity. The data involve various measurements and can vary over time due to changes in environmental conditions and operational changes. Irregular patterns and small changes can have significant impacts on analysis and decision making, requiring effective data preprocessing techniques to handle the complexities and ensure accurate analysis. This paper explores data preprocessing techniques using a water treatment system dataset as a case study and provides preprocessing techniques specific to processing data in industrial control to yield a more informative dataset. The results showed significant improvement in accuracy, F1 score, and time to detection when using the preprocessed dataset. [ABSTRACT FROM AUTHOR]

The present study proposes a framework to evaluate utilizing a combined system including water treatment system and reservoir operation optimization to control water quality impact of agricultural runoff on the aquatic habitats. Soil and water assessment tool was applied in the river basin scale to simulate outflow and nitrate concentration in the target sub-basin. Furthermore, an operation optimization model of the reservoir was utilized to analyze different scenarios of using water treatment system and reservoir operation to mitigate nitrate concentration impacts. Results indicated that the reservoir should be considered as a tool for controlling water quality in combination with the water treatment system for the cases in which a constructed dam is available at upstream. According to the results in the case study, storage loss is the same in all of the scenarios. Moreover, reservoir is able to compensate reduction of the treatment system area for mitigating water quality impacts. The reliability of water supply is considerably changed in different scenarios. Analysis corroborated that using the optimal reservoir operation is the best scenario in the case study. However, it might be different in other case studies. The most important advantage of the proposed method is to provide an integrated framework to analyze combined scenarios to mitigate nitrate impact in the river basin scale. It is recommendable to utilize the proposed framework in the decision-making system to construct water treatment systems. In fact, it might reduce costs drastically. [ABSTRACT FROM AUTHOR]

In this study, an ensemble machine learning model was developed to predict the recovery rate of water quality in a water treatment plant after a disturbance. XGBoost, one of the most popular ensemble machine learning models, was used as the main framework of the model. Water quality and operational data observed in a pilot plant were used to train and test the model. Disturbance was determined when the observed turbidity was higher than the given turbidity criteria. Therefore, the recovery rate of water quality at a time t was defined during the falling limb of the turbidity recovery period. It was considered as a relative ratio of the differences between the peak and observed turbidities at time t to the difference between the peak turbidity and turbidity criteria. The root mean square error–observation standard deviation ratio of the XGBoost model improved from 0.730 to 0.373 by pretreatment, removing the observation for the rising limb of the disturbance from the training data. Moreover, Shapley value analysis, a novel explainable artificial intelligence method, was used to provide a reasonable interpretation of the model's performance. [ABSTRACT FROM AUTHOR]

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Copyright of World Water Policy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, an ensemble machine learning model was developed to predict the recovery rate of water quality in a water treatment plant after a disturbance. XGBoost, one of the most popular ensemble machine learning models, was used as the main framework of the model. Water quality and operational data observed in a pilot plant were used to train and test the model. Disturbance was determined when the observed turbidity was higher than the given turbidity criteria. Therefore, the recovery rate of water quality at a time t was defined during the falling limb of the turbidity recovery period. It was considered as a relative ratio of the differences between the peak and observed turbidities at time t to the difference between the peak turbidity and turbidity criteria. The root mean square error–observation standard deviation ratio of the XGBoost model improved from 0.730 to 0.373 by pretreatment, removing the observation for the rising limb of the disturbance from the training data. Moreover, Shapley value analysis, a novel explainable artificial intelligence method, was used to provide a reasonable interpretation of the model’s performance.

All nurses with any responsibility for hemodialysis need to know and understand the clinical ramifications of water treatment and dialysate preparation. Although some hemodialysis programs may have full-time technical staff who operate and test the water treatment and dialysate preparation systems, many programs rely on nursing staff for the day-to-day operation and testing of these systems. It is critical for all staff members to work together to keep patients safe. This article details reasons safe water and dialysate are critical for patients on hemodialysis and reviews components of water treatment systems, dialysate concentrate preparation, and monitoring and testing necessary to assure that both water and dialysate are safe for patient use. Integrated hemodialysis systems (i.e., those that combine water treatment and dialysate preparation and delivery in an individual machine "package") are also described. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers. [ABSTRACT FROM AUTHOR]

How to establish an effective electrical automation control water treatment system, optimize the operation effect, reduce cost investment, of great significance. The design of an electric automatic control water treatment system based on LabVIEW is proposed. Siemens industrial computer is used as the upper computer, and the S7-300 series of Siemens is used in a programmable logic controller (PLC) system. Based on the LabVIEW development platform, the OPC server is set up by Siemens SIMATIC. NET, which can connect with PLC to realize real-time communication. SQL Server is used as a backstage database to realize basic monitoring functions such as data collection, equipment control, real-time curve display, history record, fault alarm, etc. The experimental results show that the system can predict the biological oxygen demand concentration online and compare it with the measured value. It can adjust the control strategy in time, reduce the fault, save the dosage and reach the goal of economical operation. [ABSTRACT FROM AUTHOR]

BACKGROUND Nemours plant‐based biomass exhibit higher removal capacities in comparison to chemical treatment methods. The present study aimed to investigate the potential of Moringa oleifera (MO) seeds and Musa cavendish (MC) peel biomass pre‐treated by HNO3 (1 N) and NaOH (0.1 M) for removing nickel (Ni), lead (Pb) and cadmium (Cd) ions from synthetic groundwater. It further aimed to establish the design and test the efficiency of an individual domestic groundwater treatment system with low‐cost using local materials. The batch tests with biomass (MC, MO and MC + MO) were conducted at pH 7. Adsorption was investigated using Freundlich and Langmuir models for single elements, and the Sheindorf–Rebhun–Sheintuch (SRS) model for multicontaminant elements. By contrast, the simulation of actual groundwater quality was studied using a Monte Carlo simulation model. RESULTS: The adsorption results fitted with the Freundlich model revealed that ≈91%, 98% and 84% of groundwater samples met WHO standards for Pb2+, Ni2+ and Cd2+, respectively. The FTIR analysis of MC, MO and MC + MO revealed that CC, CO and NH were the main functional groups playing roles in the removal of heavy metals from the water samples. The mechanism of adsorption took place by chemisorption and physisorption. The total cost of the water treatment system with a capacity of 50 L was US$17, and considered suitable for remote, lower income communities. CONCLUSION: The MC + MO biomass was efficient in adsorbing heavy metal ions from water samples owing to the presence of abundant functional groups. [ABSTRACT FROM AUTHOR]

All living things need water to live. The increasing population in the world results the increased demand of water, include in Indonesia. Water scarcity is also a big issue in Indonesia, especially in rural area like Gunung Kidul. In Gunung Kidul people difficult to get clean water. Clean water can be obtained with drinking water system. The drinking water system that now exist in Temuireng Village, Girisuko, Panggang, Gunung Kidul could meet the water need of the people, but there is a problem with the design of the water treatment system. The drinking water treatment system still use semimanual mechanism that less effective and efficient. The purpose of this research is to design the automatic drinking water treatment system that now exist in Temuireng Village to improve the quality of the water in that area and to reduce the operating cost of the systems. With considering the need, technology and some other aspects, this research obtained the new prototype design of the drinking water treatment systems that be equipped with automation. [ABSTRACT FROM AUTHOR]

The primary challenges of petroleum industries are to provide a secure quantity and quality of water resources and how to manage the generated wastewater adequately. Appropriate application of water treatment systems would play a substantial role in drilling operations. Therefore, wastewater management and controlling the amount of produced hazardous materials should be significantly taken into consideration. The objective of this extensive study is to calculate the required water for the waterflooding, polymer flooding, and hydraulic fracturing performances, and subsequently, the percentage of fresh water saving in a shale oil reservoir was calculated accordingly. First of all, the required water and treated water for each well were calculated, and then, the percentage of saving water was averagely calculated. As a result, the percentage of fresh water saving for waterflooding, polymer flooding, and hydraulic fracturing were 71.5%, 70%, and 83.7%, respectively. It was indicated that most of the injected water was treated again and reinjected in the fracturing operations. Furthermore, the total volume of required water for the drilling of Pazanan oilfield's wells was approximately 125 million gallons that indicated the treatment processes provided about 95 million gallons of this volume. Consequently, the average volume of fresh water saving was relatively 70% which was clarified the accuracy of wastewater separation and purification in the treatment system. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • O 3 -MB was applied to remove 39 pharmaceuticals in 10 therapeutic classes in water. • Over 90% removal rate was obtained for 33 compounds (>80% for 37 compounds). • O 3 -MB enhanced removal efficiency by 8–34% relative to the conventional O 3. • UV and/or H 2 O 2 increased the reaction rate constant by 2.9–5.5 times relative to O 3 -MB. • MB treatment improved O 3 consumption up to 2.8 times higher than O 3 treatments. Abstract Ozonated-microbubble (O 3 -MB) was applied to remove 39 pharmaceuticals in 10 therapeutic classes (ant-ivirals, anti-bacterials, anti-cancer drugs, psychotropics, anti-hypertensives, analgesic-antipyretics, contrast media, bronchodilators, anti-pruritics, and herbal medicines) in water and the results were compared with those given by O 3 alone and combined treatments with UV, and/or H 2 O 2 , or both. The summarized results clearly indicate that O 3 -MB based treatments significantly (P < 0.01) enhanced removal rates by 8–34% to O 3 alone, and removal rates of >90% (>80%) were attained for 33 (37) compounds. Introduction of MB technology was indispensable to improve degradation of famciclovir (anti-viral), bicalutamide (anti-cancer), sulpiride (phychotropic) and acetaminophen glucuronide (analgesic-antipyretic) over 96.0%. Recalcitrant properties of all contrast media type compounds (iohexol, ioversol, iopromide, iomeprol and iopamidol) agains t both O 3 and O 3 -MB were significantly improved by combined treatment with UV, and removal rates became 93.8–98.8% from 0% to 52.0%, indicating effectiveness of combined use of photolysis more than oxidation by hydroxyl radicals. Combination of UV and/or H 2 O 2 with O 3 -MB increased the reaction rate constant by 2.9–5.5 times in average relative to O 3 and O 3 -MB. In addition, MB treatment improved O 3 consumption up to 2.8 times higher than the O 3 treatments. These results indicate that the combined treatments of O 3 -MB with UV and/or H 2 O 2 could minimize the environmental pollutant load of pharmaceuticals discharged into rivers. To the best of our knowledge, this is the first investigation which showed enhancement of removal rates of multiple pharmaceuticals together with combined use of UV and/or H 2 O 2. [ABSTRACT FROM AUTHOR]

In order to improve the engineering ability of students in water supply and drainage engineering major, and strengthen the students’ practical ability of instrument and electromechanical control, the practice teaching link of " operation and maintenance management in water treatment system " is newly added in water treatment engineering system. This practice teaching link is guided by the concept of outcome-based education. The learning outcomes are developed in three aspects: equipment and tube assemblies, system operation regulation, and maintenance of electrical control system. In order to improve teaching and learning method, simulation teaching software for water treatment system entity engineering is developed, and the actual experimental resources are digitalized also. According to the learning outcomes and the simulation teaching software, the new training manual is compiled and the student-centered practice training project is designed to improve practical teaching content. The new construction of practical teaching, which combines engineering practice and strengthens students ’ practical ability, realizes the teaching mode of "theoretical teaching + virtual simulation + practice operation", and changes the single form of evaluation method for curriculum. [ABSTRACT FROM AUTHOR]

The quantitative analysis of the disaster effect on water supply systems can provide useful information for water supply system management. In this study, a total disaster index (TDI) was developed using open-source public data in 419 water treatment plants in Korea with 23 input variables. The TDI quantifies the possible effects or damage caused by three major disasters (typhoons, heavy rain, and earthquakes) on water supply systems. The four components (regional factor, risk factor, urgency factor, and response and recovery factor) were calculated using input variables to determine the disaster index (DI) of each disaster. The weight of the input variables was determined using principal component analysis (PCA), and the weights of the DI of three natural disasters and four components used to calculate the TDI were determined by the analytical hierarchy process (AHP). Specifically, two ensemble machine learning models, random forest (RF) and XGBoost (XGB), were used to develop models to predict the TDI. Both models predicted the TDI with the coefficient of determination and root-mean-square error-observations standard deviation ratio of 0.8435 and 0.3957 for the RF model and 0.8629 and 0.3703 for the XGB model, respectively. The relative importance analysis suggests that the number of input variables can be minimized, which improves the models’ practical applicability.

The purpose of this study is to raise the drinking rate of Arisu, the tap water in Seoul, and to propose new solution for continuous water quality management and raising awareness. The research scope was limited to point-of-use water treatment system, and based on the this contents I proceed this study how this will help to increase direct drinking rate of Arisu. Through research, Korea has provided users with the ability to filter contaminated tap water simply as water to be rinsed through water purification. Therefore, it was predicted that it would be difficult to improve tap water as drinking water. With this study, I expect that the strength of point-of-use system and the reliability of tap water quality will be raised and hope various product will be developed to improve the perception as drinking water and I also expect that this can be applied to whole country even further in the scope of Seoul city. [ABSTRACT FROM AUTHOR]

Groundwater available in the Kathmandu Valley is not suitable for drinking due to chemical and microbial contamination. We installed a treatment system, which was made with locally available materials and was low-cost, and supplied drinking water to the intervention site where groundwater contains high amounts of ammonia, iron, and turbidity. This research aims to evaluate the socioeconomic impact of treated water distribution. One hundred households were randomly selected and asked to use treated water for drinking, and another 100 households in the nearby community were taken randomly as a control. We conducted questionnaire surveys with the enrolled households before and five months after starting water distribution to assess the water use patterns and quality perceptions. The socioeconomic impact of the intervention was evaluated by a pre-post comparison and by the difference-in-difference method. The intervention significantly enhanced most of the parameters of water quality perception, reduced the in-house water treatment, and improved the perceived water stress and quality of life. For the control site, these parameters generally became worse in the post-survey, which suggests that the survey might have affected people’s mindset regarding water security. The system is an option for sustainable management of drinking water in the water-scarce, hard-hit areas in the developing countries.