Biofilms in drinking water distribution systems (DWDS) challenge water quality, infrastructure and public health. Current monitoring methods often disrupt biofilms or lack spatial coverage. This study explores two novel, non-intrusive techniques to measure biofilm thickness: one based on heat resistance, the other on changes in hydraulic residence time. Experiments were conducted in a lab-scale DWDS simulator replicating realistic pipe conditions. Both methods were evaluated against traditional destructive sampling to assess accuracy. Results show that the residence time method yields consistent, reliable estimates closely matching physical samples, while the heat resistance approach shows greater variability and requires refinement. Sensitivity analyses further demonstrate that the residence time method is more robust under varying operational conditions. These findings highlight its potential for field deployment, offering a scalable and minimally invasive solution for real-time biofilm monitoring. This advancement could support improved water quality management through targeted interventions in actual DWDS environments.

Rapid and effective decision-making is crucial during drinking water contamination events to ensure public safety. This paper examines a case study where a water utility, responding to customer complaints, suspected wastewater contamination in its network. We compare the traditional expert judgement approach to a model-based approach using the PathoINVEST tool. The tool performs simulations of contamination events informed by sensor measurements, identifies contamination sources using sampling results, and suggests optimal valve closures for mitigation. Our findings show that the model-based approach significantly enhances response efficiency and accuracy. It identified the contamination source with four samples in 1.3 h, compared to 11 samples in 3.7 h for the traditional approach, and resulted in a lower infection risk (12% versus 20%) at the time of source identification. Regarding valve closure, the model-based approach performed better, resulting in a 3%-point reduction in infection risk compared to the traditional approach. Modeling uncertainty is addressed by considering valve settings uncertainty; despite a 0.7% discrepancy in valve settings compared to the model, the tool accurately pinpointed the contamination vicinity 75% of the time. These findings support the claim that integrating modeling and sensor tools into emergency response protocols for drinking water contamination events can improve early identification and mitigation, potentially safeguarding public health in urban water supply systems.

The quality of water in households can be affected by plumbing design and materials, water usage patterns, and source water quality characteristics. These factors influence stagnation duration, disinfection residuals, metal release, and microbial activity. In particular, stagnation can degrade water quality and increase lead release from lead service lines. This study employs numerical modeling to assess how combined corrosion control and flushing strategies affect lead levels in household taps with lead service lines under reduced water use. To estimate potential health risks, the U.S. EPA model is used to predict the percentage of children likely to exceed safe blood lead levels. Lead exceedances are assessed based on various regulatory requirements. Results show that exceedances at the kitchen tap range from 3 to 74% of usage time for the 5 µg/L standard, and from 0 to 49% for the 10 µg/L threshold, across different scenarios. Implementing corrosion control treatment in combination with periodic flushing proves effective in lowering lead levels under the studied low-consumption scenarios. Under these conditions, the combined strategy limits lead exceedances above 5 µg/L to only 3% of usage time, with none above 10 µg/L. This demonstrates its value as a practical short-term strategy for households awaiting full pipe replacement. Targeted flushing before peak water use reduces the median time that water remains stagnant in household pipes from 8 to 3 h at the kitchen tap under low-demand conditions. Finally, the risk model indicates that the combined approach can reduce the predicted percentage of children with blood lead levels exceeding 5 μg/dL from 61 to 6% under low water demand.

This study addresses complex multi-objective optimization challenges in large-scale, real-world water distribution networks (WDNs). The primary objectives are to improve a water quality index (water age) and network resilience by optimizing pipe diameters and network topology as decision variables. The proposed approaches leverage the non-dominated sorting genetic algorithm II (NSGA-II) producing Pareto optimal alternatives for water utility decision-makers. To enhance computational convergence runtime and solution quality of optimization, novel techniques are employed. These include advanced NSGA-II constraint handling, search space reduction, graph theory-based formulation of decision variables, constraints, and objective functions, as well as multi-stage and hydraulic-free optimization strategies. Furthermore, soft constraints are relaxed and integrated into Pareto decision-making spaces to provide a comprehensive, multi-criteria decision-making framework. The approaches are applied to a real case study, and the results demonstrate optimization performance improvements, with efficiency increasing by approximately 20% (in terms of convergence speed). Additionally, water age is reduced by 52% while achieving favorable results in the hydraulic and topological criteria. These findings highlight the effectiveness of the proposed methodologies in addressing WDN optimization challenges.

Biofilms in drinking water distribution systems (DWDS) pose a critical challenge to water quality. If left unchecked, they can compromise the biological stability of delivered water and ultimately public health. Existing biofilm sensing techniques primarily focus on metabolic or genetic indicators of activity, often using local and destructive methods. While rich in information, such data are difficult to apply in developing practical biofilm growth models. Biofilm thickness, however, is a more representative and scalable metric for this purpose. Yet, limited research exists on non-invasive thickness sensing in DWDS. This study introduces two non-destructive methods for measuring biofilm thickness by leveraging changes in heat resistance and residence time. Heat resistance was evaluated using ambient and water temperature measurements, while residence time was assessed with a conservative tracer. Both techniques were tested in the Slimer experimental setup (50 m long, 13.2 mm diameter PVCp pipe) under realistic hydraulic conditions. Results showed a strong correlation between biofilm thickness and residence time drift, indicating flow disturbance as a reliable indicator of biofouling. In contrast, heat resistance sensing exhibited considerable natural variability, limiting its analytic value. The findings highlight residence time analysis as a promising, non-invasive approach for estimating biofilm thickness. This method offers continuous, non-destructive monitoring, enabling early detection of biofilm-related anomalies and providing valuable input for both laboratory and field applications aimed at enhancing DWDS resilience.

Biofilm formation in drinking water distribution systems (DWDSs) poses challenges to water quality and system integrity. Traditional measurement methods often involve intrusive techniques, disrupting the biofilm ecosystem, while non-intrusive methods offer promising alternatives. This paper explores the feasibility of using non-intrusive temperature sensing to monitor biofilm growth in PVC pipes. Through experiments using the SLIMER 2.0 setup, the biofilm accumulation’s impact on the heat transfer properties is investigated. Preliminary results show successful biofilm growth under controlled conditions, with temperature measurements revealing alterations in heat resistance, hence providing a basis for biofilm monitoring. This study contributes to advancing biofilm monitoring techniques, offering insights for improved water quality management in DWDSs.

Understanding the residential end uses of water is helpful for the sustainable management of water resources and the implementation of water conservation strategies. In this study, over one hundred studies were systematically reviewed to provide a comprehensive overview of the state-of-the-art research on end-use water consumption. Each study was reviewed, clustered, and subjected to a multilevel analysis aimed at quantitatively comparing the characteristics of the end uses of water available in the literature. The findings of this work support water utilities, researchers, policy makers, and consumers in identifying the key aspects of water end uses and exploring their main features across different geographical, socioeconomic, and cultural regions of the world.

The analysis of the spatial distribution of drinking water temperature (DWT) in the drinking water distribution system (DWDS) can allow for the detection of hotspots and the identification of suitable mitigation interventions to enhance the climate resilience. For this purpose, a water temperature model is implemented in EPANET-MSX and coupled with the hydraulic model of the DWDS in the town of Almere (the Netherlands). This model is then used to assess the effectiveness of a range of interventions against the unwanted water warming under a climate scenario of an extreme air temperature increase in a Dutch summer. Finally, a solution scenario is suggested to comply with the Dutch legislative limit of 25 °C on DWT at the tap.

Drinking water temperatures above 25 °C have been measured more often since Dutch drinking water companies are required to take Random Day Time (RDT) samples. The objective of this study was to obtain more information from the required temperature measurements. A total of 34,595 drinking water temperature measurements between 2012 and 2021 were analyzed and compared with the temperature prediction from a soil temperature model (STM), developed by Blokker and Pieterse-Quirijns (2013) and Agudelo-Vera et al. (2015). More than 300 exceedances of the modeled urban soil temperature were found (ca. 1%). While there were only four measurements with temperatures higher than 25 °C. By looking at the locations of the temperature exceedances, drinking water companies can further investigate whether there are other heat sources near these locations. Using the STM calculations as a reference for the measured drinking water temperature has provided more options for locating hotspots.

In a desktop exercise, a water utility’s emergency response to suspected wastewater contamination in a drinking water network was compared with a model-based approach using PathoINVEST. This tool simulates contamination scenarios and assists with locating the source of contamination using sampling results. The sampling procedure used a portable sensor that offers rapid (20 min time-to-result) screening of fecal contamination. Preliminary results show that the model-based approach is able to find the contamination source faster and with fewer samples than current practices. Integrating modeling and rapid sensor tools in emergency responses improves decision-making and public health protection in drinking water networks.

Drinking water temperatures are expected to increase in the Netherlands due to climate change and the installation of district heating networks as part of the energy transition. To determine effective measures to prevent undesirable temperature increases in drinking water, a model was developed. This model describes the temperature in the drinking water distribution network as a result of the transfer of heat from the climate and above and underground heat sources through the soil. The model consists of two coupled applications. The extended soil temperature model (STM+) describes the soil temperatures using a two-dimensional finite element method that includes a drinking water pipe and two hot water pipes coupled with a micrometeorology model. The extended water temperature model (WTM+) describes the drinking water temperature as a function of the surrounding soil temperature (the boundary temperature resulting from the STM+), the thermal sphere of influence where the drinking water temperature influences the soil temperature, and the hydraulics in the drinking water network. Both models are validated with field measurements. This study describes the WTM+. Previous models did not consider the cooling effect of the drinking water on the surrounding soil, which led to an overestimation of the boundary temperature and how quickly the drinking water temperature reaches this boundary temperature. The field measurements show the improved accuracy of the WTM+ when considering one to two times the radius of the drinking water pipe as the thermal sphere of influence around the pipe.

Urbanization increases the land surface temperature through surface mineralization, adversely affecting vegetation and enhancing the urban heat island (UHI) effect. Global climate change has intensified this warming effect with more frequent and intense heatwaves during hot seasons. While these transformations influence soil temperature, their consequences on drinking water temperature within the drinking water distribution system (DWDS) remains poorly understood. Literature proposes to increase pipe burial depths to mitigate drinking water heating during summer. In this study, we monitored drinking water temperatures in a DWDS in Montreal, Canada with deeply buried pipes (average 1.8 m) during the summer of 2022, focusing on two contrasting zones in terms of UHI and green coverage. Monitoring revealed a 8°C heating effect compared to the water treatment plant, attributed to low green coverage and anthropogenic heat. Conversely, the greener zone exhibited cooler drinking water temperatures, reaching a maximum cooling effect of 8°C as compared to the temperature at the exit of the water treatment plant. Utilizing a soil and water temperature model, we predicted drinking water temperatures within the DWDS with acceptable accuracy. Soil temperature modeling results aligned well with measured water temperatures, highlighting DWDS water temperature approaching its surrounding soil temperature fairly quickly. Despite heatwaves, no immediate correlation emerged between air temperature records and measured water temperatures, emphasizing soil temperature as a superior indicator. An increase in water age displayed no correlation with an increase in measured water temperature, underscoring the dominant influence of UHI and green coverage on water temperature. These findings highlight the cooling advantages of green spaces during summer, providing valuable insights for sustainable urban planning.

An accurate estimation of residential end uses of water is helpful in developing efficient water systems. If not obtainable through direct metering, this information can be gathered by disaggregating and classifying household-level water-use data. However, most automated techniques require fine-resolution data (e.g., 1 s) and end-use parameters which may be unavailable to water utilities. To fill the above gap, this study presents a method for the automated disaggregation and classification of indoor water-use data collected at the 1-min temporal resolution, and by exclusively relying on the end-use parameter values available in the literature. Specifically, the features of each water-use event detected at the household level are compared against the most common event features for the selected end-use category. The results obtained by testing the method with real data collected at 14 households in two different countries (Italy and the Netherlands) confirm its potential in disaggregating and classifying water end-use events with an average accuracy higher than 90% and an average (normalized) root-mean-square lower than 0.06 despite the lack of information about end uses in individual households. This demonstrates that end-use detection is possible even with data whose resolution is closer to that of most commercial water meters.

In the water industry, an accurate estimation of end-use water consumption is helpful for the implementation of efficient water systems and water-saving technologies. This study aimed to explore the characteristics of water consumption at nine households north of Amsterdam (the Netherlands), subjected to water consumption monitoring at high temporal resolution (i.e. 1 s). Overall, 36,297 water-use events monitored over about 447 days were automatically segmented into 44,115 individual events by means of a new rule-based filtering algorithm, and then labelled by expert analysts. A multi-stage analysis was then conducted in order to evaluate daily per capita end-use water consumption, daily end-use profiles, average end-use parameter average, and their statistical distributions. The results achieved provide insight into the features of end-use consumption, confirming that the largest components are typically related to showers/bathtubs, toilets, and washing machines, whereas different end-use parameter distributions can emerge.

A detailed characterization of residential water consumption is essential for ensuring urban water systems' capability to cope with changing water resources availability and water demands induced by growing population, urbanization, and climate change. Several studies have been conducted in the last decades to investigate the characteristics of residential water consumption with data at a sufficiently fine temporal resolution for grasping individual end uses of water. In this paper, we systematically review 114 studies to provide a comprehensive overview of the state-of-the-art research about water consumption at the end-use level. Specifically, we contribute with: (1) an in-depth discussion of the most relevant findings of each study, highlighting which water end-use characteristics were so far prioritized for investigation in different case studies and water demand modelling and management studies from around the world; and (2) a multi-level analysis to qualitatively and quantitatively compare the most common results available in the literature, i.e. daily per capita end-use water consumption, end-use parameter average values and statistical distributions, end-use daily profiles, end-use determinants, and considerations about efficiency and diffusion of water-saving end uses. Our findings can support water utilities, consumers, and researchers (1) in understanding which key aspects of water end uses were primarily investigated in the last decades; and (2) in exploring their main features considering different geographical, cultural, and socio-economic regions of the world.

Discoloured drinking water, caused by elevated concentrations of organic and inorganic particles, is unacceptable. It occurs due to accumulation and subsequent mobilisation of material from within drinking water distribution infrastructure. Discolouration is currently partially explained by either the theories of cohesive layers or gravitational sedimentation. It is proposed and shown here how the processes behind these two theories both occur and how to integrate them to better explain observed behaviour and inform operational interventions to reduce discolouration. Deficiencies in understanding regarding the process and factors that influence material accumulation are highlighted. Future research addressing these deficiencies will enable determination of long term sustainable management strategies balancing capital investment and operational maintenance to safeguard distribution of high quality drinking water.

Climate change demands for sustainable options for heating and cooling of buildings. Low-temperature thermal energy can be abstracted from the drinking water distribution system (DWDS); this is called thermal energy from drinking water (TED). The possible use of TED as a secondary function of the DWDS raises the question whether this secondary function can exist alongside the primary function (supplying safe and reliable drinking water) and, if so, under what conditions. Using various cases, the potential downstream effects of TED related to drinking water temperature (and hence, downstream increase of cost and CO₂emissions for water heating) and microbiological drinking water quality were studied.

Socioeconomic characteristics are influencing the temporal and spatial variability of water demand, which are the biggest source of uncertainties within water distribution system modeling. Improving current knowledge of these influences can be utilized to decrease demand uncertainties. This paper aims to link smart water meter data to socioeconomic user characteristics by applying a novel clustering algorithm that uses a dynamic time warping metric on daily demand patterns. The approach is tested on simulated and measured single-family home data sets. It is shown that the novel algorithm performs better compared with commonly used clustering methods, both in finding the right number of clusters as well as assigning patterns correctly. Additionally, the methodology can be used to identify outliers within clusters of demand patterns. Furthermore, this study investigates which socioeconomic characteristics (e.g., employment status and number of residents) are prevalent within single clusters and, consequently, can be linked to the shape of the cluster’s barycenters. In future, the proposed methods in combination with stochastic demand models can be used to fill data gaps in hydraulic models.

In this research, we investigated the influence of the heating of drinking water in the connection pipe under the influence of nearby district heating and the effect this has on water temperatures throughout the domestic drinking water system (DDWS) of a typical Dutch domestic property. We found that stagnant water in the connection pipe warms up fast, reaching the surrounding ground temperature in about 15 min, and these temperatures can be found throughout the house at taps such as the shower and the kitchen tap. Flowing water in the connection pipe is also, depending on the pipe length, heated up several degrees. The prevention of high temperatures in the soil around the connection pipe is the best measure to prevent high drinking water temperatures at the taps.

In response to droughts, various media campaigns and water-saving instructions are released. However, these often only have temporary water conservation effects. A promising development in this regard is Digital Water Meters (DWM), which can provide near real-time water-use feedback. Despite extensive DWM experience in some water-stressed regions, a profound understanding of the initial attitude towards DWM and message-tailoring opportunities are rarely empirically explored. This study aims to obtain insights into the attitude towards the introduction of DWM and explore opportunities for message tailoring, a topic of extra relevance as we may be on the threshold of a large-scale DWM implementation in many world regions. Messages tailored to (i) normative beliefs and attitudes on drinking water, (ii) water-use activity and (iii) phase of decision-making, seem particularly compatible with DWM. Through a survey (n = 1037) in the Netherlands, we observe that 93% of respondents have no objections if their utility invests in DWM and that 78% would accept a (free) DWM because of improved leakage detection, lower costs and environmental considerations. Finally, instead of sociodemographic factors, we observe that an attitude-based customer segmentation approach is an especially useful predictor of respondent’s motivation to endorse DWM and forms a promising basis for water conservation message-tailoring strategies.