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Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. [ABSTRACT FROM AUTHOR]

The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. [ABSTRACT FROM AUTHOR]

• Analogy between systems biology and water supply engineering for the first time. • Novel strategy for analyzing the changes in delivered drinking water quality. • System-level understanding of the reaction mechanisms within water pipes. • Multiscale-based modeling inspired by the flux balance analysis concepts. • Achieving higher water quality and resource efficiency in water supply management. The complexity of modeling water quality variations in water distribution systems (WDS), studied for decades, stems from multiple constraints and variables involved and the complexity of the system behavior. The conventional macroscale-based WDS water quality models are founded on continuum mechanics. In attempts to provide a broad picture of the multi-species interactions, these models overlook the stochasticity corresponding to the reaction mechanisms within the WDS domain. Furthermore, owing to the black-box type modeling adopted in simulating the multi-species interactions, the existing state-of-the-art models have limitations in representing intermediates and/or by-products formation. Accordingly, they remain ineffective in describing the water chemistry-stoichiometric interactions within the WDS domain. Only a radically new modeling approach could overcome the limitations of the macroscale-based approaches and enables analyzing the stochastic WDS mechanisms by keeping the true nature of the system behavior. Stimulated by the metabolic network modeling principles in systems biology, this article outlines the prospect of developing an innovative 'water'bolic network modeling approach to provide a new outlook to the existing WDS water quality modeling research. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: This study describes a new methodology for the disinfection booster design, placement, and operation problem in water distribution systems. Disinfectant residuals, which are in most cases chlorine residuals, are assumed to be sufficient to prevent growth of pathogenic bacteria, yet low enough to avoid taste and odor problems. Commonly, large quantities of disinfectants are released at the sources outlets for preserving minimum residual disinfectant concentrations throughout the network. Such an approach can cause taste and odor problems near the disinfectant injection locations, but more important hazardous excessive disinfectant by-product formations (DBPs) at the far network ends, of which some may be carcinogenic. To cope with these deficiencies booster chlorination stations were suggested to be placed at the distribution system itself and not just at the sources, motivating considerable research in recent years on placement, design, and operation of booster chlorination stations in water distribution systems. The model formulated and solved herein is aimed at setting the required chlorination dose of the boosters for delivering water at acceptable residual chlorine and TTHM concentrations for minimizing the overall cost of booster placement, construction, and operation under extended period hydraulic simulation conditions through utilizing a multi-species approach. The developed methodology links a genetic algorithm with EPANET-MSX, and is demonstrated through base runs and sensitivity analyses on a network example application. Two approaches are suggested for dealing with water quality initial conditions and species periodicity: (1) repetitive cyclical simulation (RCS), and (2) cyclical constrained species (CCS). RCS was found to be more robust but with longer computational time. [Copyright &y& Elsevier]

Abstract: This study describes a decision support system, alerts for contamination events in water distribution systems. The developed model comprises a weighted support vector machine (SVM) for the detection of outliers, and a following sequence analysis for the classification of contamination events. The contribution of this study is an improvement of contamination events detection ability and a multi-dimensional analysis of the data, differing from the parallel one-dimensional analysis conducted so far. The multivariate analysis examines the relationships between water quality parameters and detects changes in their mutual patterns. The weights of the SVM model accomplish two goals: blurring the difference between sizes of the two classes' data sets (as there are much more normal/regular than event time measurements), and adhering the time factor attribute by a time decay coefficient, ascribing higher importance to recent observations when classifying a time step measurement. All model parameters were determined by data driven optimization so the calibration of the model was completely autonomic. The model was trained and tested on a real water distribution system (WDS) data set with randomly simulated events superimposed on the original measurements. The model is prominent in its ability to detect events that were only partly expressed in the data (i.e., affecting only some of the measured parameters). The model showed high accuracy and better detection ability as compared to previous modeling attempts of contamination event detection. [Copyright &y& Elsevier]

Abstract: The deployment of fixed online water quality sensors in water distribution systems has been recognized as one of the key components of contamination warning systems for securing public health. This study proposes to explore how the inclusion of mobile sensors for inline monitoring of various water quality parameters (e.g., residual chlorine, pH) can enhance water distribution system security. Mobile sensors equipped with sampling, sensing, data acquisition, wireless transmission and power generation systems are being designed, fabricated, and tested, and prototypes are expected to be released in the very near future. This study initiates the development of a theoretical framework for modeling mobile sensor movement in water distribution systems and integrating the sensory data collected from stationary and non-stationary sensor nodes to increase system security. The methodology is applied and demonstrated on two benchmark networks. Performance of different sensor network designs are compared for fixed and combined fixed and mobile sensor networks. Results indicate that complementing online sensor networks with inline monitoring can increase detection likelihood and decrease mean time to detection. [Copyright &y& Elsevier]

• Distribution network as an indirect PFASs contamination and exposure risks source. • Mechanistic model for predicting PFOA fate and transport in the distribution system. • PFASs exposure risks by comparing the simulated PFOA levels with guideline values. • Child population group was found susceptible to PFOA exposure beyond 3 ng/kg/day. • Reducing chlorine dose decreased the PFOA exposure risks in the distribution network. Recent studies identified fluoroalkyl amides (FAs) transformation to perfluorooctanoic acid (PFOA) during disinfection as an indirect source of PFASs contamination of drinking water. This paper discerns the position of water disinfection systems (WDSs) as a PFOA exposure pathway. A new mechanistic model incorporating the derived knowledge about the zwitterionic/cationic FAs transformation to PFOA with the unsteady-state hydraulic characteristics of WDSs was developed. The simulation outputs from model application to a WDS from the USA established the significant role of delivery via distribution network in the PFOA formation in drinking water. PFOA exposure risk assessment studies predicted >95% of the system nodes to be at high risk when the existing stringent health-based guideline values are adopted. The 1 to 3 years and 4 to 8 years old age groups were found susceptible to PFOA exposure through drinking water beyond the tolerable limit of 3 ng/kg/day. The model predicted that reducing the chlorine dose from 2±0.2 to 1±0.1 mg/L at the treatment units drops the share of 1 to 3 years old and 4 to 8 years old consumers falling to PFOA exposure from 4.32 to 0.45% and 0.32 to <0.01%, respectively. Besides, 24.9% more, including ∼x223C10% of the consumers of 1 to 3 years old age group, were found exposed to PFOA risks when the organic loading of water was reduced by 60%. [Display omitted] [ABSTRACT FROM AUTHOR]

• A pragmatic solution to regulating water pressures and leakage by introducing PRVs. • Optimal selection of positions and setpoints of PRVs in a distribution network. • Graph theory-based algorithm employing the depth-first search method. • Pipes leading to the most extended downstream networks are ideal locations for PRVs. • The algorithm maintains the minimum pressure at the minimum required service level. Optimal pressure management is a standard strategy for water loss minimization in water distribution systems (WDS). A pragmatic solution to regulating water pressures and leakage is introducing pressure-reducing valves (PRVs). This paper presents a valve positioning algorithm for optimally deciding the positions and setpoints of PRVs in a WDS. The algorithm derives the hydraulic solution of a WDS as a directed graph, established on the flow directions, using EPANET 2.2 and develops the downstream network supplied by water flowing out of every pipe in the network by applying the depth-first search method. The algorithm later recognizes the pipes leading to the most extended downstream networks, with pressures above the minimum required service pressure, and prioritizes them as the ideal locations for PRV placement. In this way, the proposed algorithm overcomes the limitations of the state-of-the-art in realistically conceptualizing the leakage reduction for optimally positioning the PRVs in WDS. Four studies with varying complexities were selected to demonstrate the algorithm's applicability for deriving pressure management solutions. The solution time for PRV positioning was in seconds for the first three networks and several minutes for the extensive fourth case study. The results corroborate the algorithm's ability to pinpoint the critical nodes with the most increased potential for downstream pressure control and for maintaining the pressure at the least required service pressure level through optimally allocating the PRVs, with acceptable setpoint values, within the pipe network. [Display omitted]. [ABSTRACT FROM AUTHOR]

Placement of water quality sensors in a water distribution system is a common approach for minimizing contamination intrusion risks. This study incorporates detailed chemistry of organophosphate contaminations into the problem of sensor placement and links quantitative measures of the affected population as a result of such intrusions. The suggested methodology utilizes the stoichiometry and kinetics of the reactions between organophosphate contaminants and free chlorine for predicting the number of affected consumers. This is accomplished through linking a multi-species water quality model and a statistical dose–response model. Three organophosphates (chlorpyrifos, malathion, and parathion) are tested as possible contaminants. Their corresponding by-products were modeled and accounted for in the affected consumers impact calculations. The methodology incorporates a series of randomly generated intrusion events linked to a genetic algorithm for minimizing the contaminants impact through a sensors system. Three example applications are explored for demonstrating the model capabilities through base runs and sensitivity analyses. [ABSTRACT FROM AUTHOR]

As a complementary step towards solving the general event detection problem of water distribution systems, injection of the organophosphate pesticides, chlorpyrifos (CP) and parathion (PA), were simulated at various locations within example networks and hydraulic parameters were calculated over 24-h duration. The uniqueness of this study is that the chemical reactions and byproducts of the contaminants' oxidation were also simulated, as well as other indicative water quality parameters such as alkalinity, acidity, pH and the total concentration of free chlorine species. The information on the change in water quality parameters induced by the contaminant injection may facilitate on-line detection of an actual event involving this specific substance and pave the way to development of a generic methodology for detecting events involving introduction of pesticides into water distribution systems. Simulation of the contaminant injection was performed at several nodes within two different networks. For each injection, concentrations of the relevant contaminants' mother and daughter species, free chlorine species and water quality parameters, were simulated at nodes downstream of the injection location. The results indicate that injection of these substances can be detected at certain conditions by a very rapid drop in Cl2, functioning as the indicative parameter, as well as a drop in alkalinity concentration and a small decrease in pH, both functioning as supporting parameters, whose usage may reduce false positive alarms. [ABSTRACT FROM AUTHOR]

Water distribution system contamination events caused by intentional, negligent, or accidental intrusion of biological, chemical, or radioactive contaminants have significant impacts on the health of the populations that it services. Therefore, it is important to have an effective plan that can be readily implemented to minimize the impact of these contamination events. However, limited research has been focused on strategic planning of the decontamination process of the contaminated infrastructure. This paper proposed a framework for assembling a disinfection plan in real-time by (1) partitioning a WDS into a number of district metered areas (DMAs), (2) generating a solution region for each of the DMAs, and (3) assemble an effective decontamination plan using solution region generated. This framework has been applied to three contamination events. The results show that, when planning for the decontamination stage of a contamination event, the use of the proposed framework can (1) significantly reduce the response time, (2) improve the quality of the decontamination plan, and (3) provide a model for optimizing the resource allocation. Image 1 • A framework for planning water distribution system decontamination is introduced. • A decontamination plan can be readily assembled in real-time. • Resources can be allocated dynamically subject to objectives and constraints. • Decontamination can be planned to be performed in parallel, serially, or hybridly. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a dynamic thresholds scheme is developed and demonstrated for contamination event detection in water distribution systems. The developed methodology is based on a recently published article of the authors (Perelman et al., 2012). Event detection in water supply systems is aimed at disclosing abnormal hydraulic or water quality events by exploring the time series behavior of routine hydraulic (e.g., flow, pressure) and water quality measurements (e.g., residual chlorine, pH, turbidity). While event detection raises alerts to the possibility of an event occurrence, it does not relate to origins, thus an event may be hydraulically-driven, as a consequence of problems like sudden leakages or pump/pipe malfunctions. Most events, however, are related to deliberate, accidental, or natural contamination intrusions. The developed methodology herein is based on off-line and on-line stages. During the off-line stage, a genetic algorithm (GA) is utilized for tuning five decision variables: positive and negative filters, positive and negative dynamic thresholds, and window size. During the on-line stage, a recursively Bayes'' rule is invoked, employing the five decision variables, for real time on-line event detection. Using the same database, the proposed methodology is compared to Perelman et al. (2012), showing considerably improved detection ability. Metadata and the computer code are provided as Supplementary material. [Copyright &y& Elsevier]