Key Takeaways Modeling water age is a simple approach, by means of a readily measurable parameter, to rapidly assess a water distribution system's (WDS's) general water quality. Used correctly, the US Environmental Protection Agency's EPANET program can model water age with accuracy, and the results can help in designing, improving, or operating a WDS. The newest edition of EPANET added mass balance calculations and has improved computational time, mass balance performance, and overall model stability.

Conservative chemicals (such as sodium chloride) have been utilized to perform tracer studies within drinking water distribution systems. The resulting signals from a tracer study can provide significant quantitative information to assess the ability of a given network model to represent the underlying hydraulic and transport characteristics of the network. Often, however, the resulting observed water quality time-series data are simply visually inspected to assess the ability of the network model to accurately predict water quality transport. The use of standard quantitative metrics, such as arrival times, sum of squared errors (SSE), and correlation analysis at different time lags to assess the differences between the observed and predicted time-series, can provide some useful information but are not sufficient for paired data signals. In this study, the use of dynamic time warping (DTW)-an approach for estimating the similarity between two time series of data-is presented as a method for quantitative analysis of observed and model-predicted conservative chemical time-series data. DTW uses dynamic programming to match the elements of two time series, in a sequential approach, to minimize the SSE of the two signals. Whereas the SSE provides one goodness-of-fit metric, the resulting length of the warping path also provides additional information as to the degree of the alignment between the two data streams.

EPANET is widely employed to simulate water quality in water distribution systems. However, in general, the time-driven simulation approach used to determine concentrations of water-quality constituents provides accurate results only for short water-quality time steps. Overly long time steps can yield errors in concentration estimates and can result in situations in which constituent mass is not conserved. The use of a time step that is sufficiently short to avoid these problems may not always be feasible. The absence of EPANET errors or warnings does not ensure conservation of mass. This paper provides examples illustrating mass imbalances and explains how such imbalances can occur because of fundamental limitations in the water-quality routing algorithm used in EPANET. In general, these limitations cannot be overcome by the use of improved water-quality modeling practices. This paper also presents a preliminary event-driven approach that conserves mass with a water-quality time step that is as long as the hydraulic time step. Results obtained using the current approach converge, or tend to converge, toward those obtained using the preliminary event-driven approach as the water-quality time step decreases. Improving the water-quality routing algorithm used in EPANET could eliminate mass imbalances and related errors in estimated concentrations. The results presented in this paper should be of value to those who perform water-quality simulations using EPANET or use the results of such simulations, including utility managers and engineers.

The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs). The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quantified previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.

CANARY Contamination event detection system CMMS Computerized maintenance management systems CWS Contamination warning system DHS Department of Homeland Security DSL Digital subscriber lines EDS Event detection system EPA U.S. Environmental Protection Agency GA Genetic algorithm GAO Government Accountability Office h Hours HMI Human–machine interface ICS Industrial control system IT Information technology LIMS Laboratory information management system LAN Local area network MCMC Marko chain Monte Carlo MILP Mixed integer linear program

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When a water distribution system (WDS) is contaminated, short-term inhalation exposures to airborne contaminants could occur as the result of domestic water use. The most important domestic sources of such exposures are likely to be showering and the use of aerosol-producing humidifiers, i.e., ultrasonic and impeller (cool-mist) units. A framework is presented for assessing the potential effects of short-term, system-wide inhalation exposures that could result from such activities during a contamination event. This framework utilizes available statistical models for showering frequency and duration, available exposure models for showering and humidifier use, and experimental results on both aerosol generation and the volatilization of chemicals during showering. New models for the times when showering occurs are developed using time-use data for the United States. Given a lack of similar models for how humidifiers are used, or the information needed to develop them, an analysis of the sensitivity of results to assumptions concerning humidifier use is presented. The framework is applied using network models for three actual WDSs. Simple models are developed for estimating upper bounds on the potential effects of system-wide inhalation exposures associated with showering and humidifier use. From a system-wide, population perspective, showering could result in significant inhalation doses of volatile chemical contaminants, and humidifier use could result in significant inhalation doses of microbial contaminants during a contamination event. From a system-wide perspective, showering is unlikely to be associated with significant doses of microbial contaminants. Given the potential importance of humidifiers as a source of airborne contaminants during a contamination event, an improved understanding of the nature of humidifier use is warranted.

Network model detail can influence the accuracy of results from analyses of water distribution systems. Previous work has shown the limitations of skeletonized network models when considering water quality and hydraulic effects. Loss of model detail is potentially less important for aggregated effects such as the systemwide health effects associated with a contamination event, but has received limited attention. The influence of model detail on such effects is examined here by comparing results obtained for contamination events using three large network models and several skeletonized versions of the models. Loss of model detail decreases the accuracy of estimated aggregated adverse effects related to contamination events. It has the potential to have a large negative influence on the results of consequence assessments and the design of contamination warning systems. However, the adverse influence on analysis results can be minimized by restricting attention to high percentile effects (i.e., 95th percentile or higher).

Contamination warning systems (CWSs) for drinking water distribution systems (WDSs) are used to reduce the potential adverse effects of intentional or accidental WDS contamination. They are designed on the basis of possible contamination events but often address only a narrow range in event conditions. The influence on their performance of conditions different from those assumed in the design generally is not considered. Using results from simulations done with network models for 11 actual WDSs, it is shown here that CWS performance for high-consequence events can degrade substantially (by an order of magnitude) when conditions such as contaminant toxicity and injection time differ from those used in the design. Generally, increasing the number of sensors does not reduce this sensitivity to changed conditions. The significance of uncertain conditions varies substantially among WDSs. As a consequence of performance changes that occur when conditions change, mean-case designs generally outperform worst-case designs when the objective is to minimize worst-case adverse effects over a range of conditions. The results of this work can be used to implement more robust designs for CWSs, while reducing computational requirements. (C) 2014 American Society of Civil Engineers.

The Legionella species have been identified as important waterborne pathogens in terms of disease morbidity and mortality. Microbial exposure assessment is a tool that can be utilized to assess the potential of Legionella species inhalation exposure from common water uses. The screening-level exposure assessment presented in this paper developed emission factors to model aerosolization, quantitatively assessed inhalation exposures of aerosolized Legionella species or Legionella species surrogates while evaluating two generalized levels of assumed water concentrations, and developed a relative ranking of six common in-home uses of water for potential Legionella species inhalation exposure. Considerable variability in the calculated exposure dose was identified between the six identified exposure pathways, with the doses differing by over five orders of magnitude in each of the evaluated exposure scenarios. The assessment of exposure pathways that have been epidemiologically associated with legionellosis transmission (ultrasonic and cool mist humidifiers) produced higher estimated inhalation exposure doses than pathways where epidemiological evidence of transmission has been less strong (faucet and shower) or absent (toilets and therapy pool). With consideration of the large uncertainties inherent in the exposure assessment process used, a relative ranking of exposure pathways from highest to lowest exposure doses was produced using culture-based measurement data and the assumption of constant water concentration across exposure pathways. In this ranking, the ultrasonic and cool mist humidifier exposure pathways were estimated to produce the highest exposure doses, followed by the shower and faucet exposure pathways, and then the toilet and therapy pool exposure pathways.

In this paper, an overview of a strategy for automatic meter reading (AMR) data interpretation and aggregation is presented along with the proposed stochastic models adequate for representing the intrinsic characteristics of the data. Water demand measurements from single user accounts are obtained from an AMR system that continuously monitors consumption in different zones of Cincinnati, Ohio. The data represent volumetric measurements characterized by fixed increments, which depend on the sensitivity of the instruments used and occur at irregular times due to the polling method of the AMR system. Given the nature of the data, a nonhomogeneous Poisson process is proposed to model the arrivals of the increments within a selected time interval of 350days. An exponential-polynomial-trigonometric rate function with multiple periodicities (EPTMP) is assumed to describe both trends and periodicities in the observed data. A specific methodology for estimating the parameters of the EPTMP rate function is presented, based on the method of maximum likelihood. In order to evaluate the estimation technique, a performance evaluation is carried out on synthetic data generated in simulation. Finally, the estimation method is applied and tested on samples of the complete AMR data set, which is obtained from aggregating randomly selected subsets of different magnitude. The results provide significant evidence of the numerical stability and accuracy of the modeling procedure and encourage the use in simulation and prediction of water demands at network nodes from available AMR data.

Intentional or accidental releases of contaminants into a water distribution system (WDS) have the potential to cause significant adverse health effects among individuals consuming water from the system. A flexible analysis framework is presented here for estimating the magnitude of such potential effects and is applied using network models for 12 actual WDSs of varying sizes. Upper bounds are developed for the magnitude of adverse effects of contamination events in WDSs and evaluated using results from the 12 systems. These bounds can be applied in cases in which little system‐specific information is available. The combination of a detailed, network‐specific approach and a bounding approach allows consequence assessments to be performed for systems for which varying amounts of information are available and addresses important needs of individual utilities as well as regional or national assessments. The approach used in the analysis framework allows contaminant injections at any or all network nodes and uses models that (1) account for contaminant transport in the systems, including contaminant decay, and (2) provide estimates of ingested contaminant doses for the exposed population. The approach can be easily modified as better transport or exposure models become available. The methods presented here provide the ability to quantify or bound potential adverse effects of contamination events for a wide variety of possible contaminants and WDSs, including systems without a network model.

Water distribution network models for large municipalities have tens of thousands of interconnecting pipes and junctions with complex hydraulic controls. Many water security applications, including sensor placement optimization, require detailed simulation of potential contamination incidents. The postsimulation optimization problem can easily exceed memory on standard desktop computers. Large networks can be skeletonized to reduce computation; however, this alters network hydraulics, and therefore sensor placement. The objective of this paper is to evaluate a two-tiered sensor placement approach that combines hydraulic and water quality simulations using all-pipes, or original, network models with subsequent geographic aggregation of time and impact values to reduce memory requirements. The two-tiered approach first places sensors on aggregated regions, then refines the solution to actual nodes in the original model. The two-tiered sensor placement approach is compared to results using the original network and skeletonized networks based on solution quality, memory use, and runtime. Results show that skeletonized networks introduce error in sensor placement. Two-tiered sensor placement using geographic aggregation replicates the original model solution to within 5% in most cases. (C) 2013 American Society of Civil Engineers.

Properly designing contamination warning systems requires an understanding of potential public health impacts for a range of contaminated water systems and a wide range of contaminants. To address this need, we determined potential impacts for 12 actual systems serving populations ranging from similar to 10(4) to over 10(6) persons by simulating contamination events for the systems. We found several consistent patterns in the estimated impacts (defined as the size of the population receiving an ingestion dose above a given level). Significant impacts, those similar to worst-case impacts, result from injections of contaminants at only a minority of nodes. For contaminants with high thresholds for adverse effects, significant exposures are concentrated near the injection location, and impacts are not sensitive to population served. However, for contaminants with low thresholds, significant exposures are present over a significant fraction of the system, and impacts are sensitive to population. When exposures are concentrated near the injection node, the area affected tends to decrease with increasing population density. Accounting for all possible exposures and events may complicate the design of contamination warning systems.

We report on results of recent EPA/Argonne studies that examine potential public health impacts associated with hypothetical intentional contamination events in 12 real, diverse water distribution systems. Hydraulic and water quality simulations were performed using each water system's network model. Impacts are defined as the number of people who receive a contaminant dose above a certain level. We evaluated the equivalent of a wide range of contaminants and determined the sensitivity of impacts to contaminant injection duration, time of injection, injection mass, population distribution, and the model for tap water ingestion. Impacts are examined and ranked (percentile) by their injection location. We also examined the consistency of high percentile injection locations for the different contamination events. The hardening of water systems or the design of contamination warning systems (CWS) requires an understanding of how impacts from contamination events are influenced by the major factors that define the contamination event. We found that contaminant injection time, duration, and mass can have a significant influence on the magnitude of public health impacts. We also found that uncertainties in the population distribution and the model used to estimate dose can be important. For highly toxic contaminants, we found impacts are most sensitive to injection duration; for less toxic contaminants, impacts are most sensitive to injection mass. The overlap of high percentile injection locations decreases with the decreasing toxicity of the contaminant and increasing impact, thereby complicating the hardening of water systems or the design of CWS.