Fit-for-Purpose Water Quality Models to Improve Drinking Water System Decision-Making
Creators: name: Schwetschenau, Sara
Publisher: name: Carnegie Mellon University
Year: 2019
Subjects: subject: 160510 Public Policy
subject: FOS: Political science
subject: FOS: Political science

description: water infrastructure is critical to human health, protecting customers from waterborne diseases and other contaminants. Systems are comprised of two parts: treatment and distribution. Water quality is excellent immediately after treatment; however, it changes with time in the distribution network. Post-treatment residual contaminants interact chemically and biologically within the distribution network causing consumed drinking water quality to vary spatially and temporally across the network. As a result, maintaining post-treatment water quality throughout the extensive piped distribution network in the face of many complex chemical and biological relationships is a challenge for water utilities. Drinking water treatment and distribution system challenges are exacerbated by changes in water demands, associated with changes in population and industry, and the age and long-term deferred maintenance of distribution systems.<br>Regulatory and public health requirements for water systems change, and as a result these systems have to be modified, upgraded or expanded over time. Meeting the existing and future challenges of drinking water needs requires a significant amount of resources and may require utilities to consider a broader set of engineering alternatives. The requirement for significant financial investment and the need to meet multiple objectives (e.g., cost, water quality) creates complex decision problems that benefits from a systems-based approach.<br>Effective analysis of system conditions and challenges requires models. The model selected needs to be “fit-for-purpose” and tailored specifically to the system, decision context, and decision-maker to be considered useful in the decision-making context. This work applies models to two different stages of decision-making: problem framing and planning. A framing model provides an initial analysis of the system or topic area (e.g., prevalence of lead in drinking water), this type of model assesses available datasets to identify relevant ones, and aids decision-makers in identifying the target engineering question to investigate in subsequent analyses. A planning model is designed to address a specific set of engineering questions (e.g., where to place treatment in an existing system) and evaluates a wide range of alternatives to identify a set of promising options to be considered further by decision-makers. In this work, first, an integrated simulation and optimization planning model is used to identify the location and capacity of treatment plants within an existing distribution network. Second, a statistical framing model is applied to evaluate the prevalence of lead release within drinking water systems.<br>The research objectives of this work are as follows:<br>1. Develop a new approach to integrate optimization and physicochemical simulation models to improve selection or prioritization of engineering infrastructure design options in drinking water systems;<br>2. Develop a multi-objective framework to evaluate the role of regulatory limits and the tradeoffs between cost and public health in locating and sizing drinking water treatment infrastructure;<br>3. Evaluate the spatial distribution and relationship among water-lead and non-water lead hazards and density of sensitive populations to identify regions most likely exposed to elevated lead levels; and<br>4. Define the extents of the uncertainty associated with the Lead and Copper Rule compliance evaluation to inform lead in drinking water decision-making beyond regulatory assessment. <br>