Lead Concentrations in US School Drinking Water: Testing Programs, Prevalence, and Policy Opportunities, 2016‒2018

A Comprehensive Examination of the Contaminants in Drinking Water in Public Schools in California, 2017-2022

OBJECTIVES

Reports of unsafe school drinking water in the United States highlight the importance of ensuring school water is safe for consumption. Our objectives were to describe (1) results from our recent school drinking water sampling of 5 common contaminants, (2) school-level factors associated with exceedances of various water quality standards, and (3) recommendations.

METHODS

We collected and analyzed drinking water samples from at least 3 sources in 83 schools from a representative sample of California public schools from 2017 through 2022. We used multivariate logistic regression to examine school-level factors associated with lead in drinking water exceedances at the American Academy of Pediatrics (AAP) recommendation level (1 part per billion [ppb]) and state action-level exceedances of other contaminants (lead, copper, arsenic, nitrate, and hexavalent chromium).

RESULTS

No schools had state action-level violations for arsenic or nitrate; however, 4% had ≥1 tap that exceeded either the proposed 10 ppb action level for hexavalent chromium or the 1300 ppb action level for copper. Of first-draw lead samples, 4% of schools had ≥1 tap that exceeded the California action level of 15 ppb, 18% exceeded the US Food and Drug Administration (FDA) bottled water standard of 5 ppb, and 75% exceeded the AAP 1 ppb recommendation. After turning on the tap and flushing water for 45 seconds, 2%, 10%, and 33% of schools exceeded the same standards, respectively. We found no significant differences in demographic characteristics between schools with and without FDA or AAP exceedances.

CONCLUSIONS

Enforcing stricter lead action levels (<5 ppb) will markedly increase remediation costs. Continued sampling, testing, and remediation efforts are necessary to ensure drinking water meets safety standards in US schools.

Prenatal and childhood lead exposure is prospectively associated with biological markers of aging in adolescence

Few studies have related early life lead exposure to adolescent biological aging, a period characterized by marked increases in maturational tempo. We examined associations between prenatal and childhood lead exposure and adolescent biological age (mean 14.5 years) utilizing multiple epigenetic clocks including: intrinsic (IEAA), extrinsic (EEAA), Horvath, Hannum, PhenoAge, GrimAge, Skin-Blood, Wu, PedBE, as well as DNA methylation derived telomere length (DNAmTL). Epigenetic clocks and DNAmTL were calculated via adolescent blood DNA methylation measured by Infinium MethylationEPIC BeadChips. We constructed general linear models (GLMs) with individual lead measures predicting biological age. We additionally examined sex-stratified models and lead by sex interactions, adjusting for adolescent age and lead levels, maternal smoking and education, and proportion of cell types. We also estimated effects of lead exposure on biological age using generalized estimating equations (GEE). First trimester blood lead was positively associated with a 0.14 increase in EEAA age in the GLMs though not the GEE models (95%CI 0.03, 0.25). First and 2nd trimester blood lead levels were associated with a 0.02 year increase in PedBE age in GLM and GEE models (1st trimester, 95%CI 0.004, 0.03; 2nd trimester, 95%CI 0.01, 0.03). Third trimester and 24 month blood lead levels were associated with a -0.06 and -0.05 decrease in Skin-Blood age, respectively, in GLM models. Additionally, 3rd trimester blood lead levels were associated with a 0.08 year decrease in Hannum age in GLM and GEE models (95%CI -0.15, -0.01). There were multiple significant results in sex-stratified models and significant lead by sex interactions, where males experienced accelerated biological age, compared to females who saw a decelerated biological age, with respect to IEAA, EEAA, Horvath, Hannum, and PedBE clocks. Further research is needed to understand sex-specific relationships between lead exposure and measures of biological aging in adolescence and the trajectory of biological aging into young adulthood.

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.

Data-Sparse Prediction of High-Risk Schools for Lead Contamination in Drinking Water: Examples from Four U.S. States

Childhood lead exposure through drinking water has long-term effects on cognition and development, and is a significant public health concern. The comprehensive lead testing of public schools entails high expense and time. In prior work, random forest modeling was used successfully to predict the likelihood of lead contamination in the drinking water from schools in the states of California and Massachusetts. In those studies, data from 70% of the schools was used to predict the probability of unsafe water lead levels (WLLs) in the remaining 30%. This study explores how the model predictions degrade, as the training dataset forms a progressively smaller proportion of schools. The size of the training set was varied from 80% to 10% of the total samples in four US states: California, Massachusetts, New York, and New Hampshire. The models were evaluated using the precision-recall area under curve (PR AUC) and area under the receiver operating characteristic curve (ROC AUC). While some states required as few as 10% of the schools to be included in the training set for an acceptable ROC AUC, all four states performed within an acceptable ROC AUC range when at least 50% of the schools were included. The results in New York and New Hampshire were consistent with the prior work that found the most significant predictor in the modeling to be the Euclidean distance to the closest school in the training set demonstrating unsafe WLLs. This study further supports the efficacy of predictive modeling in identifying the schools at a high risk of lead contamination in their drinking water supply, even when the survey data is incomplete on WLLs in all schools.