Lead Levels in Tap Water at Licensed North Carolina Child Care Facilities, 2020-2021

Identifying and Responding to Lead in Drinking Water in a University Setting

Lead is an established neurotoxicant, and it has known associations with adverse neurodevelopmental and reproductive outcomes. Exposure to lead at any level is unsafe, and the United States (US) has enacted various federal and state legislations to regulate lead levels in drinking water in K-12 schools and childcare facilities; however, no regulations exist for higher education settings. Upon the discovery of lead in drinking water fixtures in the University of North Carolina at Chapel Hill (UNC-CH) campus, a cross-campus water testing network and sampling plan was developed and deployed. The campaign was based on the US Environmental Protection Agency's (EPA) 3Ts (Training, Testing, and Taking Action) guidance. The seven-month campaign involved 5954 tests on 3825 drinking water fixtures across 265 buildings. A total of 502 (8.43%) tests showed lead above the limit of detection (1 part per billion, ppb), which represented 422 (11.03%) fixtures. Fewer than 1.5% of the tests were above the EPA action level for public water systems (15 ppb). In conclusion, systematic testing of all the fixtures across campus was required to identify localized contamination, and each entity in the cross-campus network undertook necessary roles to generate a successful testing campaign. UNC-CH established preventative measures to test drinking water fixtures every three years, which provide a framework for other higher education institutions in responding to lead contamination.

US drinking water quality: exposure risk profiles for seven legacy and emerging contaminants

BACKGROUND

Advances in drinking water infrastructure and treatment throughout the 20th and early 21st century dramatically improved water reliability and quality in the United States (US) and other parts of the world. However, numerous chemical contaminants from a range of anthropogenic and natural sources continue to pose chronic health concerns, even in countries with established drinking water regulations, such as the US.

OBJECTIVE/METHODS

In this review, we summarize exposure risk profiles and health effects for seven legacy and emerging drinking water contaminants or contaminant groups: arsenic, disinfection by-products, fracking-related substances, lead, nitrate, per- and polyfluorinated alkyl substances (PFAS) and uranium. We begin with an overview of US public water systems, and US and global drinking water regulation. We end with a summary of cross-cutting challenges that burden US drinking water systems: aging and deteriorated water infrastructure, vulnerabilities for children in school and childcare facilities, climate change, disparities in access to safe and reliable drinking water, uneven enforcement of drinking water standards, inadequate health assessments, large numbers of chemicals within a class, a preponderance of small water systems, and issues facing US Indigenous communities.

RESULTS

Research and data on US drinking water contamination show that exposure profiles, health risks, and water quality reliability issues vary widely across populations, geographically and by contaminant. Factors include water source, local and regional features, aging water infrastructure, industrial or commercial activities, and social determinants. Understanding the risk profiles of different drinking water contaminants is necessary for anticipating local and general problems, ascertaining the state of drinking water resources, and developing mitigation strategies.

IMPACT STATEMENT

Drinking water contamination is widespread, even in the US. Exposure risk profiles vary by contaminant. Understanding the risk profiles of different drinking water contaminants is necessary for anticipating local and general public health problems, ascertaining the state of drinking water resources, and developing mitigation strategies.

Improved Decision Making for Water Lead Testing in U.S. Child Care Facilities Using Machine-Learned Bayesian Networks

Tap water lead testing programs in the U.S. need improved methods for identifying high-risk facilities to optimize limited resources. In this study, machine-learned Bayesian network (BN) models were used to predict building-wide water lead risk in over 4,000 child care facilities in North Carolina according to maximum and 90th percentile lead levels from water lead concentrations at 22,943 taps. The performance of the BN models was compared to common alternative risk factors, or heuristics, used to inform water lead testing programs among child care facilities including building age, water source, and Head Start program status. The BN models identified a range of variables associated with building-wide water lead, with facilities that serve low-income families, rely on groundwater, and have more taps exhibiting greater risk. Models predicting the probability of a single tap exceeding each target concentration performed better than models predicting facilities with clustered high-risk taps. The BN models' Fβ-scores outperformed each of the alternative heuristics by 118-213%. This represents up to a 60% increase in the number of high-risk facilities that could be identified and up to a 49% decrease in the number of samples that would need to be collected by using BN model-informed sampling compared to using simple heuristics. Overall, this study demonstrates the value of machine-learning approaches for identifying high water lead risk that could improve lead testing programs nationwide.