Predicting the spread of contamination in water distribution networks laid on sloping terrains

The growing prevalence of pharmaceutical compounds in the environment raises significant concerns due to their potential impacts on ecological and human health. This present manuscript focuses on the methods used to extract and determine these pharmaceuticals in water samples. It provides a comprehensive analysis of the extraction techniques and analytical approaches employed for the identification and quantification of pharmaceuticals in environmental water. Due to their chemical properties and widespread use, pharmaceuticals persist in the environment and contaminate water bodies, soil, and sediments. The presence of pharmaceuticals in the aquatic environment has been linked to several adverse effects on aquatic organisms, including the disruption of physiological processes and reproductive impairment. Furthermore, pharmaceuticals in the environment can affect human health through food and drinking water contamination and contribute to antibiotic resistance. The analysis of pharmaceutical contaminants in water samples presents several challenges due to the complex matrix and low concentrations of target substances. Various sample preparation techniques and protocols, including solid-phase extraction (more than 76% of the studied literature) and QuEChERS (quick, easy, cheap, effective, rugged, and safe), coupled with liquid chromatography–tandem-mass spectrometry, are commonly used for their determination. These methods offer high sensitivity, selectivity, and efficiency in identifying and quantifying pharmaceuticals in environmental samples. It is, therefore, essential that ongoing research is conducted in order to develop more efficient analytical methods and mitigation strategies to address pharmaceutical contamination in the environmental water effectively. It is also crucial that increased awareness and regulatory measures are put in place in order to minimize the environmental and human health risks associated with pharmaceutical pollutants.

Protection of the water system is paramount due to the negative consequences of contaminated water on the public health. Water resources are one of the critical infrastructures that must be preserved from deliberate and accidental attacks. Water qualities are examined at the treatment plant. However, its quality can substantially be contaminated during transportation from the plant to the consumers’ taps. Contamination in water distribution networks (WDNs) is a danger that can have severe consequences on public health as well as an economic and social instability. Water distribution networks are immensely susceptible to deliberate or accidental attacks due to the complex nature of the system. Hence, contamination source identification (CSI) is a topical issue in water distribution systems that require immediate attention of researchers in order to protect mankind from the adverse effect of consuming contaminated water. Usually, a contaminant event can be detected by the water quality monitoring sensors or the contaminant warning system (CWS) installed on the network. Nevertheless, how to derive the source of the contamination from the collected information is a difficult task that must be tackled in order to evaluate the spread of the contamination and for immediate remedial strategies. In the past two decades, considerable efforts and advancement have been made by researchers applying various techniques in order to locate the source of the contamination in WDNs. Each of the techniques has certain limitations and applicability as reported in the literature. This paper presents a comprehensive review of the existing techniques with emphasis on their importance and technical challenges. Despite a series of investigations in this domain, the field is yet to be unified. Hence, open research areas are still available to explore. Consequently, improvement on the existing techniques is necessary and hereby suggested. More importantly, practical application of these techniques offer a major research gap that must be addressed.

In the present technical note, a simple methodology for identifying the source location of an accidental contamination in a water distribution network is formulated. Starting from measured solute concentration data and using the pollution matrix concept, the methodology first selects a group of candidate nodes, which are possible intrusion points. Among all candidate nodes, the source location is then identified, minimizing the differences between simulated and measured concentrations. The source location procedure, which incorporates a hydraulic network simulation model, is formulated as an optimization problem, linearized using the water fraction matrix concept. An example illustrates methodology effectiveness. An uncertainty analysis is performed in order to demonstrate the methodology robustness with respect to uncertainties in some input data, like concentration measurements and water demands.

Drinking water network is vulnerable to toxic chemicals. Anomaly detection-based event detection can provide reliable indication of contamination by analyzing the real-time water quality data, collected by online-distributed sensors in water network. This article reviews the water quality event detection methodologies based on the correlation of water quality parameters and contaminants. Further, we review how to reduce the impact of contamination in water distribution network, including sensor placement optimization and contamination source determination.

One of the major elements affecting the working of a water distribution network (WDN) is the natural topography of the area on which it is laid, and when such a scenario is coupled with a pipe leak, the results are not known as yet. The effects of a sloping terrain on pressure distribution and transient state due to leakage in pipes are studied in detail in this research by considering three common types of pipe leaks. Significant results are obtained after the hydraulic simulation, and critical points are identified in the WDN. Statistical analysis is performed based on the data obtained through simulation results to develop a model for predicting the amount of flow through a leak in a WDN laid on sloping terrain. The model is found to be dependent on water demand, the pressure of water during the transient state, and the slope of a particular node from the main source of water. The model is validated by comparing the simulated values with the calculated ones and shows a good match. The research proves to be beneficial for better management of water distribution through pipe networks and overcoming water loss through pipe leaks.

Fungal and bacterial evaluation in drinking water distribution network and their association with physicochemical parameters

An intrusion of contaminants into the water distribution network (WDN) can occur through storage tanks (via animals, dust-carrying bacteria, and infiltration) and pipes. A sensor network could yield useful observations that help identify the location of the source, the strength, the time of occurrence, and the duration of contamination. This paper proposes a methodology for identifying the contamination sources in a water distribution system, which identifies the key characteristics of contamination, such as location, starting time, and injection rates at different time intervals. Based on simplified hypotheses and associated with a high computational efficiency, the methodology is designed to be a simple and easy-to-use tool for water companies to ensure rapid identification of the contamination sources, The proposed methodology identifies the characteristics of pollution sources by matching the dynamic patterns of the simulated and measured concentrations. The application of this methodology to a literature network and a real WDN are illustrated with the aid of an example. The results showed that if contaminants are transported from the sources to the sensors at intervals, then this method can identify the most possible ones from candidate pollution sources. However, if the contamination data is minimal, a greater number of redundant contamination source nodes will be present. Consequently, more data from different sensors obtained through network monitoring are required to effectively use this method for locating multi-sources of contamination in the WDN.

Monitoring drinking water is an important public health problem because the safe drinking water is essential for human life. Many procedures have been developed for monitoring water quality in water treatment plants for years. Monitoring of water distribution systems has received less attention. The goal of this communication is to study the problem of drinking water safety by ensuring the monitoring of the distribution network from water plant to customers. The system is based on the observation of residual chlorine concentrations which are provided by the sensor network. The complexity of the detection problem is due to the water distribution network complexity and dynamic profiles of water consumptions. The onset time and geographic location of water contamination are unknown. Its duration is also unknown but finite. Moreover, the residual chlorine concentrations, which are modified by the contamination, are also time dependent since they are functions of water consumptions Two approaches for detection are presented. The first one, namely the parametric approach, exploits the hydraulic model to compute the nominal residual chlorine concentrations. The second one, namely the nonparametric approach, is a statistical methodology exploiting historical data. Finally, the probable area of introduction of the pollutant and the propagation of the pollution are computed and displayed to operational users.

Analysis of the Resilience of Intermittent Water Supply Systems and the Disruption-Dynamics of Stakeholders

Resilient water distribution system is crucial for sustainable urban water management. Evaluating the inherent resilience of the buried water infrastructure is key to ensuring reliable water distribution. The water distribution network maintains water quality and supplies sufficient water to users. Evaluating the system’s resilience under varying failure conditions is crucial to guarantee continued service delivery. This study investigates the resilience of the water distribution network for the University City, Sharjah, United Arab Emirates subjected to failure conditions caused by pipe failure, water contamination, and water excess demand. This research quantifies the corresponding performance under these stressors and develops an innovative resilience index by using the global resilience analysis (GRA) approach. The corresponding strain is in the form of node failure, chlorine decay, and pressure failures among all the pipes throughout the network. A survey was conducted with the water company to identify recovery time for the designated water distribution network. Another survey was conducted among the experts to evaluate the relative significance of all the strains in contribution towards resilience. Based on the resilience index, four levels (high, moderate, low, and very low) of resilience were defined. The study revealed Sharjah water distribution network has up to 40% of its stress categorized as low resilience and 60% of its stress categorized as very low resilience. The study also presented a management plan for the improvement of the designated water distribution network.

In recent years, there has been a need to seek adequate preventive measures to deal with contamination in water distribution networks that may be related to the accidental contamination and the deliberate injection of toxic agents. Therefore, it is very important to create a sensor system that detects contamination events in real time, maintains the reliability and efficiency of measurements, and limits the cost of the instrumentation. To this aim, two problems have to be faced: practical difficulties connected to the experimental verification of the optimal sensor configuration efficiency on real operating systems and challenges related to the reliability of the network modelling approaches, which usually neglect the dispersion and diffusion phenomena. The present study applies a numerical optimization approach using the NSGA-II genetic algorithm that was coupled with a new diffusive-dispersive hydraulic simulator. The results are compared with those of an experimental campaign on a laboratory network (Enna, Italy) equipped with a real-time water quality monitoring system and those of a full-scale real distribution network (Zandvoort, Netherlands). The results showed the importance of diffusive processes when flow velocity in the network is low. Neglecting diffusion can negatively influence the water quality sensor positioning, leading to inefficient monitoring networks.

Contamination in water distribution networks (WDNs) can occur at any time and location. One protection measure in WDNs is the placement of water quality sensors (WQSs) to detect contamination and provide information for locating the potential contamination source. The placement of WQSs in WDNs must be optimally planned. Therefore, a robust sensor-placement strategy (SPS) is vital. The SPS should have clear objectives regarding what needs to be achieved by the sensor configuration. Here, the objectives of the SPS were set to cover the contamination event stages of detection, consumption, and source localization. As contamination events occur in any form of intrusion, at any location and time, the objectives had to be tested against many possible scenarios, and they needed to reach a fair value considering all scenarios. In this study, the particle swarm optimization (PSO) algorithm was selected as the optimizer. The SPS was further reinforced using a databasing method to improve its computational efficiency. The performance of the proposed method was examined by comparing it with a benchmark SPS example and applying it to DMA-sized, real WDNs. The proposed optimization approach improved the overall fitness of the configuration by 23.1% and showed a stable placement behavior with the increase in sensors.

The positioning of quality detection points as well as the frequency of sampling is a crucial aspect for the implementation of Water Safety Plans (WSPs), which have been proposed worldwide to ensure water quality and to minimize the risk from contamination in water distribution networks (WDNs). In this regard, some international legislations and best practices about quality of drinking water suggest very fine sampling frequencies, but they do not specify where the detection points should be located in a WDN. In this paper, three different approaches, based on empiricism, optimization and topology, respectively, were applied to locate detection quality points in a WDN. The comparison highlighted that empirical approach commonly adopted by water utility practitioners is unsatisfactory. The optimization-based approach, although performing significantly better, is difficult to apply, since it requires a calibrated hydraulic model. The topological approach, based on the use of the betweenness centrality and not requiring any hydraulic information and simulation, proves to be effective, and it can be easily adopted by water utilities to identify the location for quality detection points, due to its simplicity compared with the optimization-based approach.

This paper proposes a combined management strategy for monitoring water distribution networks (WDNs). This strategy is based on the application of water network partitioning (WNP) for the creation of district metered areas (DMAs) and on the installation of sensors for water quality monitoring. The proposed methodology was tested on a real WDN, showing that boundary pipes, at which flowmeters are installed to monitor flow, are good candidate locations for sensor installation, when considered along with few other nodes detected through topological criteria on the partitioned WDN. The option of considering only these potential locations, instead of all WDN nodes, inside a multi-objective optimization process, helps in reducing the search space of possible solutions and, ultimately, the computational burden. The solutions obtained with the optimization are effective in reducing affected population and detection time in contamination scenarios, and in increasing detection likelihood and redundancy of the monitoring system. Last but most importantly, these solutions offer benefits in terms of management and costs. In fact, installing a sensor alongside the flowmeter present between two adjacent DMAs yields managerial advantages associated with the closeness of the two devices. Furthermore, economic benefits due to the possibility of sharing some electronical components for data acquisition, saving, and transmission are derived.

Leakage detection and calibration of hydraulic models are important issues for the management of water and other distribution networks. An inverse transient model based on a hybrid search technique is presented here. The inverse model is developed mainly for the detection of leaks in water distribution networks. The inverse transient procedure is formulated as a constrained optimisation problem of weighted least-squares type. Two optimisation techniques are tested: the genetic algorithm (GA) and the Levenberg-Marquardt (LM) method. After examining their performance, a new hybrid genetic algorithm (HGA) is developed to exploit the advantages of combining the two methods. The resulting HGA-based inverse transient model is compared with the GA and LM-based inverse transient models using two case studies. The HGA-based inverse transient model proved to be more stable than the LM-based model and it is more accurate and much faster than the GA-based inverse transient model.