Improved Decision-Making: A Sociotechnical Utility-Based Framework for Drinking Water Investment.

To achieve the goals of the Safe Drinking Water Act, state and local water authorities need to make decisions about where to direct limited funding for infrastructure improvements and currently do so in the absence of adequate evaluative metrics. We developed a framework grounded in utility theory that compares trade-offs explicitly and broadens the factors considered in prioritizing resource allocations. Relevant existing indices were reviewed to identify data applicable to drinking water decision-making. A utility-theory-based decision analysis framework was developed and applied to evaluate how different objectives affect funding decisions for lead service line replacement (LSLR) programs in Pennsylvania and Michigan, United States. The decision framework incorporates drinking water quality characteristics with community and environmental quality attributes. We compare additive and multiplicative model structures, different weights, and spatial scales. Our decision framework showed that the inclusion of additional data beyond what is usually considered in LSLR decisions could change the top 10 counties or public water systems prioritized. Further, the counties or water systems in the top 10 were influenced by the model structure and weights. Prioritization changed based on which data were included, and has implications for the use of evaluative metrics beyond traditional water system data.

Using Compliance Data to Understand Uncertainty in Drinking Water Lead Levels in Southwestern Pennsylvania.

The historical use of lead in potable water plumbing systems has caused significant public health challenges. The Lead and Copper Rule requires utilities to take action if the 90th percentile lead concentration exceeds the action level (AL) of 15 ppb. Assessment of the AL is based on a sample of homes representing a relatively small fraction of connections. Due to the intentional nonrepresentative sampling approach, the full set of conditions influencing lead concentrations in a large distribution system may be poorly characterized. Further, there is uncertainty in assessing statistical parameters such as the 90th percentile concentration. This work demonstrates methods to compute the uncertainty in the 90th percentile statistic and assesses the associated effect on compliance outcomes. The method is demonstrated on four utilities in southwest Pennsylvania (referred to as A, B, C, and D). For Utility A, evaluation of the 90th percentile showed an increase over time in observed and estimated values and the value's uncertainty. This type of change in the uncertainty might have served as an early warning of the exceedance that followed. This could have triggered more timely review of operational changes in order to avoid the effects of noncompliance on utility costs and consumer confidence.