James G. Uber

A reactive species model for chlorine decay and THM formation under rechlorination conditions

Chlorine is typically used within drinking water distribution systems to maintain a disinfectant residual and minimize biological regrowth. Typical distribution system models describe the loss of disinfectant due to reactions within the water matrix as first order with respect to chlorine concentration, with the reactants in excess. Recent work, however, has investigated relatively simple dynamic models that include a second, hypothetical reactive species. This work extends these latter models to account for discontinuities associated with rechlorination events, such as those caused by booster chlorination and by mixing at distribution system junction nodes. Mathematical arguments show that the reactive species model will always represent chlorine decay better than, or as well as, a first-order model, under single dose or rechlorination conditions; this result is confirmed by experiments on five different natural waters, and is further shown that the reactive species model can be significantly better under some rechlorination conditions. Trihalomethane (THM) formation was also monitored, and results show that a linear relationship between total THM (TTHM) formation and chlorine demand is appropriate under both single dose and rechlorination conditions. This linear relationship was estimated using the modeled chlorine demand from a calibrated reactive species model, and using the measured chlorine demand, both of which adequately represented the TTHM formation.

Development and autocalibration of an input-output model of chlorine transport in drinking water distribution systems

Chlorine concentrations within drinking water distribution systems (DWDS) must be maintained between an Environmental Protection Agency enforced minimum and maximum values driven by formation of harmful disinfectant byproducts. The DWDS input-output (I-O) model developed expresses the chlorine concentration at a given pipe junction and time as a weighted average of exponentially decayed values of the concentrations at all adjacent upstream junctions. The upstream junction concentrations are known if they are a chlorine treatment point, or can be calculated in the same manner as the original unknown junction concentration. This is the basis for a recursive procedure with which the I-O model backtracks through the DWDS until all paths from consumption to treatment are found. Since the I-O model finds all paths from treatment to a given measurement, the reaction rate associated with chlorine decay at the pipe wall can be adjusted to improve predicted chlorine concentrations.

Real-time identification of possible contamination sources using network backtracking methods.

In case of contamination intrusion in water distribution systems, water quality sensor data can be used to determine the location and time of the contamination source. One approach to contamination source identification is finding the source location that minimizes the difference between modeled and measured water quality. However, this is an inherently ill-posed mathematical problem, due to the shortage of measurements compared to source parameters, and regularization methods are required to force identification of a unique solution. An alternative practical method is developed in this paper to identify all possible locations and times that explain contamination incidents detected by the water quality sensors. Since sensors cannot detect the quantitative concentration of a contaminant, this method only requires a binary sensor status over time. A particle backtracking algorithm is used to identify the water flow paths and travel times leading to each sensor measurement. Those locations and times that are...

Feedback control of water quality

Drinking water distribution networks (DWDN) are complex, large-scale systems designed to supply clean water to industrial and domestic users. To reduce the risk of human exposure to pathogens, drinking water is required to contain a small disinfectant residual. The most common disinfectant used in DWDN is chlorine because it is inexpensive and effectively controls a number of disease-causing organisms. The article formulates the water quality control problem and proposes a design approach based on parameter estimation and adaptive control techniques.

Reducing MAUP bias of correlation statistics between water quality and GI illness

Abstract This research investigates the role of spatial aggregation and the modifiable area unit problem (MAUP) on the correlation between drinking water quality and gastrointestinal (GI) illness. Using water quality estimates from hydraulic modeling of a water distribution system and a linear dose–response model, we simulate illness point patterns with a theoretically determined correlation to average pathogen concentrations. We then assess the sensitivity of the Pearson’s correlation statistic ( r ) to different aggregation units. Because public health data are often geocoded illness events, we assess the importance of their network-clustered structure by comparing two spatial scenarios. The first scenario uses a random spatial distribution for illness point patterns. Randomly located points are then compared to network-clustered illness event patterns where the set of possible illness locations is limited to network nodes. We then analyze multiple illness simulations with various sets of commonly used areal units, such as census units, regular grids, and Voronoi tessellations. A systematic bias on r due to the MAUP is estimated by showing an average reduction in r of 0.65. Consideration of the spatial network constraint of illness data during aggregation reduces this MAUP bias estimate 41% from 0.65 to 0.38.

Drinking water turbidity and emergency department visits for gastrointestinal illness in Atlanta, 1993–2004

The extent to which drinking water turbidity measurements indicate the risk of gastrointestinal illness is not well understood. Despite major advances in drinking water treatment and delivery, infectious disease can still be transmitted through drinking water in the United States, and it is important to have reliable indicators of microbial water quality to inform public health decisions. The objective of our study was to assess the relationship between gastrointestinal illness, quantified through emergency department visits, and drinking water quality, quantified as raw water and filtered water turbidity measured at the treatment plant. We examined the relationship between turbidity levels of raw and filtered surface water measured at eight major drinking water treatment plants in the metropolitan area of Atlanta, Georgia, and over 240,000 emergency department visits for gastrointestinal illness during 1993–2004 among the population served by these plants. We fit Poisson time-series statistical regression models that included turbidity in a 21-day distributed lag and that controlled for meteorological factors and long-term time trends. For filtered water turbidity, the results were consistent with no association with emergency department visits for gastrointestinal illness. We observed a modest association between raw water turbidity and emergency department visits for gastrointestinal illness. Our results suggest that source water quality may contribute modestly to endemic gastrointestinal illness in the study area. The association between turbidity and emergency department visits for gastrointestinal illness was only observed when raw water turbidity was considered; filtered water turbidity may not serve as a reliable indicator of modest pathogen risk at all treatment plants.

Real Time Water Demand Estimation in Water Distribution System

Accurate modeling of chemical transport in water distribution systems depends on accurate knowledge of temporally and spatially variable water demands. Typical network models would include water demands that are allocated from billing or census data, and thus may not be appropriate for specific operational analysis, such as emergency events arising from intentional or accidental contamination. During such an event, water consumption patterns may be significantly different from those assumed when developing the hydraulic model, and may change significantly over short time periods due to the unusual circumstances of the event. To allow accurate hydraulic and water quality prediction in real-time, the water demands should be updated continuously to reflect current conditions. The development of such a real-time water demand calibration method poses many complex issues such as identifiability and uncertainty of the water demand estimates. Given the sparsity of data that are likely to be available in real time, prior statistical information about water demands must be incorporated in the calibration procedure. In this paper, a method and algorithms are proposed for a real time water demand calibration process. A predictor-corrector methodology is proposed to predict statistical hydraulic behavior based on prior estimation of water demands, and then correct this prediction using new, real-time measurements. The problem is solved using the extended Kalman filter, which is a linear algorithm that calculates the estimate of water demands and their uncertainty. As part of the Kalman filter calculation, we calculate direct sensitivities of system hydraulic responses with respect to water demands. Results of numerical experiments illustrate the impacts of statistical demand variability, hydraulic measurement accuracy and sampling design on demand estimation. This paper was presented at the 8th Annual Water Distribution Systems Analysis Symposium which was held with the generous support of Awwa Research Foundation (AwwaRF).

Greedy Heuristic Methods for Locating Water Quality Sensors in Distribution Systems

Monitoring and surveillance systems for drinking water distribution networks are intended to provide real time warning of drinking water contamination events and mitigate their public health consequences. Drinking water distribution networks often serve large populations over vast areas. There exist a large number of access points where contaminants could be introduced, and these are spread throughout the service area. Transport of contaminants from these access points to consumers would occur through a multitude of pathways, and be dominated by water flows that change magnitude and direction in response to frequent changes in water use and system operation. The above features of drinking water distribution networks dictate that design of a successful monitoring and surveillance system is comprised of three interrelated sub-tasks:

Adaptive control of water quality in water distribution networks

Based on investigating the spatially distributed input-output relationship of disinfectant residual in water distribution networks, this brief paper formulates the water quality control problem of multiple nodes in a decomposed adaptive control framework, with special consideration on the periodic variation of parameter uncertainty due to varying consumer demands. The water distribution network is decomposed to subnetworks based on the effect of the decay of chlorine concentration. The periodic parametric uncertainty is represented by a Fourier series with on-line parameter estimation of the unknown coefficients. A simulation example is provided to illustrate the performance of the algorithm in a real water distribution network.

Drinking water residence time in distribution networks and emergency department visits for gastrointestinal illness in Metro Atlanta, Georgia.

We examined whether the average water residence time, the time it takes water to travel from the treatment plant to the user, for a zip code was related to the proportion of emergency department (ED) visits for gastrointestinal (GI) illness among residents of that zip code. Individual-level ED data were collected from all hospitals located in the five-county metro Atlanta area from 1993 to 2004. Two of the largest water utilities in the area, together serving 1.7 million people, were considered. People served by these utilities had almost 3 million total ED visits, 164,937 of them for GI illness. The relationship between water residence time and risk for GI illness was assessed using logistic regression, controlling for potential confounding factors, including patient age and markers of socioeconomic status (SES). We observed a modestly increased risk for GI illness for residents of zip codes with the longest water residence times compared with intermediate residence times (odds ratio (OR) for Utility 1 = 1.07, 95% confidence interval (CI) = 1.03, 1.10; OR for Utility 2 = 1.05, 95% CI = 1.02, 1.08). The results suggest that drinking water contamination in the distribution system may contribute to the burden of endemic GI illness.