Human pathogens have been observed in municipal drinking water systems. Microbial intrusions due to distribution system deficiencies are a recognized cause of acute gastrointestinal illness (AGI) outbreaks. Yet, their importance as a cause of sporadic and endemic AGI is poorly understood. The WAHTER (Water And Health Trial for Enteric Risk) study sought to characterize health impacts associated with virus intrusions into drinking water distribution systems, as well as with virus contamination of source groundwater.

The WAHTER study was designed as a cohort epidemiological trial with cross-over intervention, taking place in 14 small Wisconsin communities relying on untreated groundwater. Six human enteric virus types were enumerated by RT-qPCR in the source groundwater and household tap water of the study communities during four three-month periods in 2006-2007. AGI incidence in the communities was reported through weekly health questionnaires. Each community received intervention in the form of UV disinfection at active wellheads for one of the study years. During UV intervention viral contamination in source groundwater was virtually eliminated, so that viruses detected at taps could be ascribed to intrusions into distribution systems.

Within the context of the WAHTER study, this dissertation focuses on: the validation of a method to concentrate viruses from water using glass wool filtration (Chapter 1); a method to estimate virus numbers in environmental samples using integrated cell culture-qPCR (Chapter 2); the association between distribution system maintenance or failure events and viral contamination in distribution systems (Chapter 3); and estimating non-epidemic AGI incidence associated with virus intrusions into distribution systems (Chapter 4).

The association between virus levels at household taps and distribution system events was tested using generalized linear regression. The occurrence of pipe installations was significantly associated with increased virus levels in distribution systems, while hydrant flushing was significantly associated with reduced virus loads (Chapter 3).

AGI associated with distribution systems was estimated using a Monte Carlo-based static risk assessment framework. Results suggest that virus intrusions into drinking water distribution systems can be a significant contributor to non-epidemic AGI. Distribution system risk was highly variable, reflecting the sporadic nature of contamination events (Chapter 4).

Microalgal-bacterial aggregates (MBAs) have recently attracted significant attention as a potential replacement for conventional, suspended-growth wastewater treatment. Proponents of MBAs often claim several key benefits: 1) elimination or reduction of external aeration requirements, which would greatly reduce energy consumption, 2) improved resource recovery through the production of value-added products from microalgal biomass, and 3) removal of nitrogen, phosphorus, and bioavailable chemical oxygen demand (bCOD) that is competitive with conventional technologies (e.g., activated sludge). This article briefly reviews the development of MBAs and evaluates their feasibility for full-scale implementation. The results suggest that MBAs and similar suspended-growth processes are functionally identical to well-mixed wastewater treatment ponds, which have been used and studied for decades and require a substantial amount of surface area to treat typical municipal wastewater. We estimate that photosynthesis and atmospheric diffusion would provide at most only 2.7% of the oxygen required for bCOD removal in a nitrifying activated sludge process. Thus, MBAs and similar treatment processes are not viable alternatives to conventional wastewater treatment processes when space is limited. However, attached-growth bioreactors designed to maximize atmospheric diffusion of oxygen relative to their footprint are promising for compact, low-energy wastewater treatment with microalgal-bacterial consortia.

An analysis of potable water main failures, which are responsible for water loss, are detrimental to public health, greatly inconvenience many people and businesses, and increase operational costs for water utilities, is presented in Chapter 1. Specifically, self-excitation and seasonality in main breaks for a large water utility in Northern California are examined and inference results for a wide range of covariates are included. A demonstration of how leak detection and repair and pressure management programs, which are typically operated separately, can be jointly optimized using hydraulic and economic modeling is presented in Chapter 2. An analysis of the potential for photosynthetic oxygen production by microalgal-bacterial aggregates is given in Chapter 3 while the origins and primary challenges of microalgal-bacterial aggregates are outlined in Chapter 4.

Shifting energy consumption into times when renewable energy is abundant can increase renewable integration and reduce greenhouse gas emissions. As large energy users with flexibility in their operations, water and wastewater utilities can participate in this type of energy demand management. However, the primary goal of the water sector is to provide clean, safe, and adequate drinking water and protect human health and the environment by treating wastewater to appropriate standards. Water or wastewater treatment and delivery systems are both complex, with their own operational challenges; optimizing energy use must not interfere with the system's primary goals. In this dissertation, I present research focused on exploring techniques that allow water and wastewater utilities to perform energy demand management while still prioritizing key operation performance metrics such as water quality or system reliability. I also leverage data analytics, machine learning, and visualization to allow users, including water and wastewater utility optimizers, to better characterize and improve optimization problem formulations and methods. The first investigation focuses on testing load shifting strategies at a full-scale wastewater treatment plant to participate as a demand resource. During these test events, the facility shifted energy load by modifying select operations with little to no impact on water quality. A cost-benefit analysis showed that the facility achieved cost savings of up to 4.8% by participating in the proxy demand response program, which allows users to bid on the California Wholesale energy market. From this case study, we identified two main barriers to wastewater utilities participating as a demand resource: the difficulty of correctly timing energy reductions to demand response periods and the inability of the standard baseline methodology to measure demand reduction for this complex system. In the second research effort, we present a new optimization problem formulation for the water distribution system pump optimization problem: secondary time-based controls. This proposed pump control uses a hierarchal structure to allow utilities to prioritize maintaining sufficient water reserves while responding to time-based energy incentives. The formulation was tested on three case studies and compared to two baseline pump control decision variable representations. A Monte Carlo sensitivity analysis was also conducted to determine the robustness of the control structures to uncertain water demands. Results show that this formulation similarly or further reduces energy costs than the two benchmark decision variable formulations without reducing average storage or violating operational constraints. In addition, the secondary time-based controls more consistently maintain water reserves and prevent constraint violations with uncertain demands, allowing water utilities to more comfortably manage energy demand to support renewable energy growth. In the final study, we developed a visual analytic framework that characterizes the optimization fitness landscape to help users improve the optimization problem formulation and search efficiency. We also present a corresponding optimization method that guides the optimization using similar interpretable machine learning methods. Both the framework and optimization method were designed for use in the water distribution pump operation problem, but many of the analytic and visualization techniques could be applied to a broad range of complex optimization applications. We used the framework to examine the differences in fitness landscape between two different decision variable problem formulations on a benchmark water distribution system. We then tested the performance of several existing heuristic optimization methods either with or without guidance from the visual analytic framework and compared them to the proposed optimization method. The existing methods informed by the visual analytic framework and the new optimization method showed improvement over standard optimization.

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