Unveiling the greenhouse gas emissions of drinking water treatment plant throughout the construction and operation stages based on life cycle assessment.

The carbon peaking and carbon neutrality targets proposed by the Chinese government have initiated a green transformation that is full of challenges and opportunities and endowed sustainable development strategy for combating global warming issue. It is essential to execute comprehensive identification and carbon reduction measures across all industries that produce greenhouse gas (GHG) emissions. Water supply system, as an energy-intensive sector, plays a crucial role in GHG reduction. This work conducted a life cycle assessment (LCA) to account the GHG emissions associated with the construction and operation phases of the drinking water treatment plant (DWTP). During the construction phase, the total GHG emissions were 19,525.762 t CO2-eq, with concrete work and rebar project being the dominant contributors (87.712%). The promotion of renewable or recyclable green building materials and low-carbon construction methods, such as the utilization of prefabricated components and on-site assembly, holds significant importance in reducing GHG emissions during the construction phase of DWTP. Regarding the operation stage, the DWTP possessed an average annual GHG emission of 37,660.160 t CO2-eq and an average GHG intensity of 0.202 kg CO2-eq/m3. Most emissions were attributed to electricity consumption (67.388%), chemicals utilization (12.893%), and heat consumption (10.414%). By increasing the use of clean energy and implementing strict control measures in the water supply pumps, energy consumption and GHG emissions can be effectively reduced. This study offers valuable insights into the mapping of GHG emissions in the DWTP, facilitating the identification of key areas for targeted implementation of energy-saving and carbon-reducing measures.

Machine learning and user interface for cyber risk management of water infrastructure.

With the continuous modernization of water plants, the risk of cyberattacks on them potentially endangers public health and the economic efficiency of water treatment and distribution. This article signifies the importance of developing improved techniques to support cyber risk management for critical water infrastructure, given an evolving threat environment. In particular, we propose a method that uniquely combines machine learning, the theory of belief functions, operational performance metrics, and dynamic visualization to provide the required granularity for attack inference, localization, and impact estimation. We illustrate how the focus on visual domain-aware anomaly exploration leads to performance improvement, more precise anomaly localization, and effective risk prioritization. Proposed elements of the method can be used independently, supporting the exploration of various anomaly detection methods. It thus can facilitate the effective management of operational risk by providing rich context information and bridging the interpretation gap.

Fate of Cryptosporidium and Giardia through conventional and compact drinking water treatment plants.

Over the past three decades, a notable rise in the occurrence of enteric protozoan pathogens, especially Giardia and Cryptosporidium spp., in drinking water sources has been observed. This rise could be attributed not only to an actual increase in water contamination but also to improvements in detection methods. These waterborne pathogens have played a pivotal role in disease outbreaks and the overall escalation of disease rates in both developed and developing nations worldwide. Consequently, the control of waterborne diseases has become a vital component of public health policies and a primary objective of drinking water treatment plants (DWTPs). Limited studies applied real-time PCR (qPCR) and/or immunofluorescence assay (IFA) for monitoring Giardia and Cryptosporidium spp., particularly in developing countries like Egypt. Water samples from two conventional drinking water treatment plants and two compact units (CUs) were analyzed using both IFA and qPCR methods to detect Giardia and Cryptosporidium. Using qPCR and IFA, the conventional DWTPs showed complete removal of Giardia and Cryptosporidium, whereas Mansheyat Alqanater and Niklah CUs achieved only partial removal. Specifically, Cryptosporidium gene copies removal rates were 33.33% and 60% for Mansheyat Alqanater and Niklah CUs, respectively. Niklah CU also removed 50% of Giardia gene copies, but no Giardia gene copies were removed by Mansheyat Alqanater CU. Using IFA, both Mansheyat Alqanater and Niklah CUs showed a similar removal rate of 50% for Giardia cysts. Additionally, Niklah CU achieved a 50% removal of Cryptosporidium oocysts, whereas Mansheyat Alqanater CU did not show any removal of Cryptosporidium oocysts. Conventional DWTPs were more effective than CUs in removing enteric protozoa. The contamination of drinking water by enteric pathogenic protozoa remains a significant issue globally, leading to increased disease rates. Infectious disease surveillance in drinking water is an important epidemiological tool to monitor the health of a population.

Removing Iron and Organic Substances from Water over the Course of Its Treatment with the Application of Average and Highly Alkaline Polyaluminium Chlorides.

In topic-related literature pertaining to the treatment of water, there is a lack of information on the influence of iron ions in highly basic polyaluminum chlorides on the efficiency of purifying water with increased contents of organic substance. The aim of this work was to determine the changes in the content of organic substances as well as iron compounds in water intended for human consumption following unit treatment processes with particular attention paid to the coagulation process. As coagulants, polyaluminium chloride PAXXL10 with an alkalinity of 70%, as well as polyaluminium chloride PAXXL1911 with an alkalinity of 85% the composition of which also contained iron, were tested. The analysis of the obtained results showed that iron compounds and organic substances were removed to the greatest extent by the coagulation process, which also had a significant influence on the final efficiency of water treatment. The effectiveness of water treatment was determined by the type of tested polyaluminum chloride, which influenced the formation of iron-organic complexes. The reason behind the formation of colored iron-organic complexes during coagulation using PAXXL1911 coagulant was the high pH (approx. 8), at which the functional groups of organic substances, due to their dissociation, are more reactive in relation to iron, and possibly the fact of introducing additional iron ions along with the coagulant.

Aluminium Drinking Water Treatment Residuals and Their Toxic Impact on Human Health.

Aluminium exerts undeniable human health effects, so its concentration should be controlled in water treatment plants. The article presents and discusses the results of studies on the influence of selected properties of aluminium coagulants on the concentration of aluminium remaining in the purified water. The coagulants used were classical hydrolysing aluminium salts: aluminium sulphate (VI) and sodium aluminate as well as pre-hydrolysed polyaluminium chlorides: Flokor 105B and PAX XL10 that had different the alkalinity coefficient r = [OH-]/[Al3+]. The Al species distribution in the coagulants samples were analysed by the Ferron complexation timed spectrophotometry. On the basis of their reaction rates with ferron reagent, the aluminium species were divided into three categories: monomeric (Ala), medium polymerised (Alb) and colloidal (Alc). The usefulness of the tested aluminium coagulants due to the concentration of residual aluminium and dissolved aluminium, which is easily assimilated by the human body, was increased according to the following series: sodium aluminate (Ala = 100%, Alb = 0) < aluminium sulphate (VI) (Ala = 91%, Alb = 9%) < PAX XL 10 (Ala = 6%, Alb = 28%, r = 2.10) < Flokor 105B (Ala = 3%, Alb = 54%, r = 2.55).

Health risk assessment of trihalomethanes in water treatment plants in Jiangsu Province, China.

Probabilistic lifetime cancer risks and non-cancer risks of trihalomethanes (THMs) through ingestion, dermal contact, and inhalation exposure in 88 drinking water treatment plants (WTPs) with raw waters from five water systems (WSs) in Jiangsu Province were analyzed and compared. Concentrations of THMs in finished water of study WTPs varied, ranging from 18.81 to 38.96 µg/L, which are lower than the maximum of 80 µg/L recommended by USEPA. The results of health risk assessment indicated that cancer risk as well as non-cancer risks of THMs in WTPs sourced from five water systems decreased in the order of WS3 > WS5 > WS2 > WS1 > WS4. The comparison among multiple exposure routes indicated that when non-boiled drinking water is consumed, ingestion has the highest exposure route, with exposure values greater than dermal contact and inhalation for WTPs with raw water from all five water systems. However, when drinking boiled water, dermal contact is the major risk source for WTPs with raw water from WS1 and WS2, instead of dermal contact, inhalation becomes the major risk source for WTPs with raw water from WS3, WS4, and WS5. In WTPs with raw water from water systems WS1, WS3, WS4, and WS5, dibromochloromethane (DBCM) in THMs has the highest contribution to cancer risk, while chloroform in THMs has the highest contribution to non-cancer risk. However, in WTPs with raw water from water system WS2, bromodichloromethane (BDCM) has the highest contribution to both cancer risk and non-cancer risk. The results also indicated that females are prone to cancer risk induced by THMs since Chinese people are accustomed to drinking boiled water. The results supply valuable information for health departments to put forward more specific and efficient policies to control water borne diseases.

Machine learning for energy cost modelling in wastewater treatment plants.

Understanding the energy cost structure of wastewater treatment plants is a relevant topic for plant managers due to the high energy costs and significant saving potentials. Currently, energy cost models are generally generated using logarithmic, exponential or linear functions that could produce not accurate results when the relationship between variables is highly complex and non-linear. In order to overcome this issue, this paper proposes a new methodology based on machine-learning algorithms that perform better with complex datasets. In this paper, machine learning was used to generate high-performing energy cost models for wastewater treatment plants, using a database of 317 wastewater treatment plants located in north-west Europe. The most important variables in energy cost modelling were identified and for the first time, the energy price was used as model parameter and its importance evaluated.

Determination of the priority substances regulated by 2000/60/EC and 2008/105/EC Directives in the surface waters supplying water treatment plants of Athens, Greece.

An investigation into the occurrence of priority substances regulated by 2000/60/EC Water Framework Directive and 2008/105/EC Directive was conducted for a period of one year in the surface water sources supplying the water treatment plants (WTPs) of Athens and in the raw water of WTPs. Samples from four reservoirs and four water treatment plants of Athens were taken seasonally. The substances are divided into seven specific groups, including eight volatile organic compounds (VOCs), diethylhexylphthalate, four organochlorine pesticides (OCPs), three organophosphorus/organonitrogen pesticides (OPPs/ONPs), four triazines and phenylurea herbicides, pentachlorophenol, and four metals. The aforementioned substances belong to different chemical categories, and different analytical methods were performed for their determination. The results showed that the surface waters that feed the WTPs of Athens are not burdened with significant levels of toxic substances identified as European Union (EU) priority substances. Atrazine, hexachlorocyclohexane, endosulfan, trifluralin, anthracene and 4-nonylphenol were occasionally observed at very low concentrations. Their presence in a limited number of cases could be attributed to waste disposal, agricultural activities, and to a limited industrial activity in the area nearby the water bodies.

Chloramine demand estimation using surrogate chemical and microbiological parameters.

A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (Fm) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection.

Monitoring and removal of cyanobacterial toxins from drinking water by algal-activated carbon.

Microcystins (MCs) are the most potent toxins that can be produced by cyanobacteria in drinking water supplies. This study investigated the abundance of toxin-producing algae in 11 drinking water treatment plants (DWTPs). A total of 26 different algal taxa were identified in treated water, from which 12% were blue green, 29% were green, and 59% were diatoms. MC levels maintained strong positive correlations with number of cyanophycean cells in raw and treated water of different DWTPs. Furthermore, the efficiency of various algal-based adsorbent columns used for the removal of these toxins was evaluated. The MCs was adsorbed in the following order: mixed algal-activated carbon (AAC) ≥ individual AAC > mixed algal powder > individual algal powder. The results showed that the AAC had the highest efficient columns capable of removing 100% dissolved MCs from drinking water samples, thereby offering an economically feasible technology for efficient removal and recovery of MCs in DWTPs.

Optimization of conventional water treatment plant using dynamic programming.

In this research, the mathematical models, indicating the capability of various units, such as rapid mixing, coagulation and flocculation, sedimentation, and the rapid sand filtration are used. Moreover, cost functions were used for the formulation of conventional water and wastewater treatment plant by applying Clark's formula (Clark, 1982). Also, by applying dynamic programming algorithm, it is easy to design a conventional treatment system with minimal cost. The application of the model for a case reduced the annual cost. This reduction was approximately in the range of 4.5-9.5% considering variable limitations. Sensitivity analysis and prediction of system's feedbacks were performed for different alterations in proportion from parameters optimized amounts. The results indicated (1) that the objective function is more sensitive to design flow rate (Q), (2) the variations in the alum dosage (A), and (3) the sand filter head loss (H). Increasing the inflow by 20%, the total annual cost would increase to about 12.6%, while 20% reduction in inflow leads to 15.2% decrease in the total annual cost. Similarly, 20% increase in alum dosage causes 7.1% increase in the total annual cost, while 20% decrease results in 7.9% decrease in the total annual cost. Furthermore, the pressure decrease causes 2.95 and 3.39% increase and decrease in total annual cost of treatment plants.

An Ultrasonic Multi-Beam Concentration Meter with a Neuro-Fuzzy Algorithm for Water Treatment Plants.

Ultrasonic concentration meters have widely been used at water purification, sewage treatment and waste water treatment plants to sort and transfer high concentration sludges and to control the amount of chemical dosage. When an unusual substance is contained in the sludge, however, the attenuation of ultrasonic waves could be increased or not be transmitted to the receiver. In this case, the value measured by a concentration meter is higher than the actual density value or vibration. As well, it is difficult to automate the residuals treatment process according to the various problems such as sludge attachment or sensor failure. An ultrasonic multi-beam concentration sensor was considered to solve these problems, but an abnormal concentration value of a specific ultrasonic beam degrades the accuracy of the entire measurement in case of using a conventional arithmetic mean for all measurement values, so this paper proposes a method to improve the accuracy of the sludge concentration determination by choosing reliable sensor values and applying a neuro-fuzzy learning algorithm. The newly developed meter is proven to render useful results from a variety of experiments on a real water treatment plant.

Water treatment plant site location using rough set theory.

Currently, advanced methods have been developed to select an appropriate site for an engineering project. The ability to make a good decision in site selection can help the engineers to reduce the expensive costs, which are very important in large construction projects. In this paper, a new approach for site selection is presented. This method is based on rough set theory which is a mathematical theory presented by professor Pawlak. In this study, the results of the rough set decision-making are compared with the results of the regression method in a practical case study for the site location of a water treatment plant in Ardabil Province in the northwest of Iran, to demonstrate that the rough set theory provides a useful method for site selection. The results of practical studies indicate that using this method for site selection decision-making can reduce costs and prevent hazards that may happen due to civil engineering uncertainties.

The occurrence and removal of selected fluoroquinolones in urban drinking water treatment plants.

Fluoroquinolones (FQs) are a widely prescribed group of antibiotics. They enter the aqueous environment, where they are frequently detected, and can lead to a threat to human health. Drinking water treatment plants (DWTPs) play a key role in removing FQs from potable water. This study investigated the occurrence and removal of four selected FQs (norfloxacin (NOR), ciprofloxacin (CIP), enrofloxacin (ENR), and ofloxacin (OFL)) in three urban DWTPs in China. The treatment efficacy for each system was simultaneously evaluated. Two of the examined DWTPs used conventional treatment processes. The third used conventional processes followed by additional treatment processes (ozonation-biologically activated carbon (ozonation-BAC) and membrane technology). The average concentrations of the four FQs in the source water and the finished water ranged from 51 to 248 ng/L and from <5 to 46 ng/L, respectively. Based on residual concentrations, the conventional treatment system had a low removal of FQs. In contrast, the addition of advanced treatment processes such as the ozonation-BAC and membranes, substantially improved the removal of FQs. The finding of this study has important implications: even though coagulation-sedimentation and chlorination treatment processes can remove most target FQs, the typical practice of advanced treatment processes is necessary for the further removal.

Microcystins in potable surface waters: toxic effects and removal strategies.

In freshwater, harmful cyanobacterial blooms threaten to increase with global climate change and eutrophication of surface waters. In addition to the burden and necessity of removal of algal material during water treatment processes, bloom-forming cyanobacteria can produce a class of remarkably stable toxins, microcystins, difficult to remove from drinking water sources. A number of animal intoxications over the past 20 years have served as sentinels for widespread risk presented by microcystins. Cyanobacterial blooms have the potential to threaten severely both public health and the regional economy of affected communities, particularly those with limited infrastructure or resources. Our main objectives were to assess whether existing water treatment infrastructure provides sufficient protection against microcystin exposure, identify available options feasible to implement in resource-limited communities in bloom scenarios and to identify strategies for improved solutions. Finally, interventions at the watershed level aimed at bloom prevention and risk reduction for entry into potable water sources were outlined. We evaluated primary studies, reviews and reports for treatment options for microcystins in surface waters, potable water sources and treatment plants. Because of the difficulty of removal of microcystins, prevention is ideal; once in the public water supply, the coarse removal of cyanobacterial cells combined with secondary carbon filtration of dissolved toxins currently provides the greatest potential for protection of public health. Options for point of use filtration must be optimized to provide affordable and adequate protection for affected communities.

Draft genome of pathogenic heterotrophic bacterium Bacillus altitudinis 2R-9.

Human activity affects the quality of potable water sources and their associated bacterial communities. Here, we discuss the heterotrophic Bacillus altitudinis 2R-9 draft isolated from the raw source of a drinking water distribution system in South Africa.

The Use of Microfiltration for the Pretreatment of Backwash Water from Sand Filters.

Tests of microfiltration efficiency used for the pretreatment of backwash water from sand filters were conducted at two water treatment plants treating surface water and infiltration water. Microfiltration efficiency was evaluated for three membrane modules: two with polymeric membranes and one with a ceramic membrane. This study showed that the contaminants that limit the reuse of backwash water from both plants by returning them to the water treatment line are mostly microorganisms, including pathogenic species (Clostridium perfringens). Additionally, in the case of backwash water from infiltration water treatment, iron and manganese compounds also had to be removed before its recirculation to the water treatment system. Unexpectedly, organic carbon concentrations in both types of backwash water were similar to those present in intake waters. Microfiltration provided for the removal of organic matter, ranging from 19.9% to 44.5% and from 7.2% to 53.9% for backwash water from the treatments of surface water and infiltration water, respectively. Furthermore, the efficiency of the iron removal from backwash water from infiltration water treatment was sufficient to ensure good intake water quality. On the other hand, manganese concentrations in the backwash water, from infiltration water treatment, pretreated using the microfiltration process exceeded the levels found in the intake water and were, therefore, an additional limiting factor for the reuse of the backwash water. In both types of backwash water, the number of microorganisms, including Clostridium perfringens (a pathogenic one), was a limiting parameter for backwash water reuse without pretreatment. The results of the present study showed the possibility for using microfiltration for the pretreatment of backwash water, regardless of its origin but not as the sole process. More complex technological systems are needed before recirculating backwash water into the water treatment system. The polyvinylidene fluoride (PVDF) membrane proved to be the most effective for DOC and microorganism removal from backwash water.

Microplastic distribution and their abundance along rivers are determined by land uses and sediment granulometry.

Microplastics in freshwater ecosystems have gained attention for their potential impact on biodiversity. Rivers are complex and dynamic ecosystems that transport particles and organic matter from the headwaters through watersheds to the ocean. Changes in land use and the presence of wastewater treatment plants (WWTPs) increase the risk of plastic contamination. Simultaneously, hydromorphological features of the watershed can influence the dispersion and retention of microplastics. This study assesses the impact of urban land uses and river hydromorphology on microplastic abundance and spatial distribution in two watersheds with contrasting land uses. Unexpectedly, our findings show that microplastics were widespread throughout watersheds both in water (3.5 ± 3.3 particles/L) and sediments (56.9 ± 39.9 particles/g). The concentration of microplastics in sediments significantly increased in granulometry ranging from 0.5 to 1 mm. Microplastics in running waters are significantly correlated with increasing urban land use coverage. However, the presence and distance of WWTPs did not affect microplastic distribution. In conclusion, contrasting patterns were observed for suspended and sedimented microplastic particles: suspended microplastics were associated with an anthropogenic effect, whereas the concentration of microplastics in sediments was determined by riverbed granulometry. Our results suggest that the interaction of anthropogenic and environmental factors shapes microplastic distribution along the rivers and their subsequent transport toward the coastal ocean. Finally, a review of the current literature reveals the absence of standardization in field and laboratory assessment techniques and measurement units, representing a challenge for intercomparisons of river microplastic studies.

Comparative analysis of chlorine-resistant bacteria after chlorination and chloramination in drinking water treatment plants.

Chlorine-resistant bacteria (CRB) in drinking water treatment plants (DWTPs) jeopardize water quality and pose a potential risk to human health. However, the specific response of CRB to chlorination and chloramination remains uncharacterized. Therefore, we analyzed 16 S rRNA sequencing data from water samples before and after chlorination and chloramination taken between January and December 2020. Proteobacteria and Firmicutes dominated all finished water samples. After chloramination, Acinetobacter, Pseudomonas, Methylobacterium, Ralstonia, and Sphingomonas were the dominant CRB, whereas Ralstonia, Bacillus, Acinetobacter, Pseudomonas, and Enterococcus were prevalent after chlorination. Over 75% of the CRB e.g. Acinetobacter, Pseudomonas, Bacillus, and Enterococcus were shared between the chlorination and chloramination, involving potentially pathogens, such as Acinetobacter baumannii and Pseudomonas aeruginosa. Notably, certain genera such as Faecalibacterium, Geobacter, and Megasphaera were enriched as strong CRB after chloramination, whereas Vogesella, Flavobacterium, Thalassolituus, Pseudoalteromonas, and others were enriched after chlorination according to LEfSe analysis. The shared CRB correlated with temperature, pH, and turbidity, displaying a seasonal pattern with varying sensitivity to chlorination and chloramination in cold and warm seasons. These findings enhance our knowledge of the drinking water microbiome and microbial health risks, thus enabling better infectious disease control through enhanced disinfection strategies in DWTPs.

Heavy metals in influent and effluent from 146 drinking water treatment plants across China: Occurrence, explanatory factors, probabilistic health risk, and removal efficiency.

Heavy metals (HMs) in drinking water have drawn worldwide attention due to their risks to public health; however, a systematic assessment of the occurrence of HMs in drinking water treatment plants (DWTPs) at a large geographical scale across China and the removal efficiency, human health risks, and the correlation with environmental factors have yet to be established. Therefore, this study characterised the occurrence patterns of nine conventional dissolved HMs in the influent and effluent water samples from 146 typical DWTPs in seven major river basins across China (which consist of the Yangtze River, the Yellow River, the Songhua River, the Pearl River, the Huaihe River, the Liaohe River and the Haihe River) for the first time and removal efficiency, probabilistic health risks, and the correlation with water quality. According to the findings, a total of eight HMs (beryllium (Be), antimony (Sb), barium (Ba), molybdenum (Mo), nickel (Ni), vanadium (V), cobalt (Co) and titanium (Ti)) were detected, with detection frequencies in influent and effluent water ranging from 2.90 (Mo) to 99.30% (Ba) and 1.40 (Ti) to 97.90% (Ba), respectively. The average concentration range was 0.41 (Be)- 77.36 (Sb) µg/L. Among them, Sb (exceeding standard rate 8%), Ba (2.89%), Ni (21.43%), and V (1.33%) were exceeded the national standard (GB5749-2022). By combining Spearman's results and redundancy analysis, our results revealed a close correlation among pH, turbidity (TURB), potassium permanganate index (CODMn), and total nitrogen (TN) along with the concentration and composition of HMs. In addition, the concentration of HMs in finished water was strongly affected by the concentration of HMs in raw water, as evidenced by the fact that HMs in surface water poses a risk to the quality of finished water. Metal concentration was the primary factor in assessing the health risk of a single metal, and the carcinogenic risk of Ba, Mo, Ni, and Sb should be paid attention to. In DWTPs, the removal efficiencies of various HMs also vary greatly, with an average removal rate ranging from 16.30% to 95.64%. In summary, our findings provide insights into the water quality and health risks caused by HMs in drinking water.