Geology topics

Lorraine C. Backer

Publications and source records attributed to Lorraine C. Backer.

5 recordsLinked to original sources

USGS2022

Arsenic in private well water and birth outcomes in the United States

Background Prenatal exposure to drinking water with arsenic concentrations >50 μg/L is associated with adverse birth outcomes, with inconclusive evidence for concentrations ≤50 μg/L. In a collaborative effort by public health experts, hydrologists, and geologists, we used published machine learning model estimates to characterize arsenic concentrations in private wells—federally unregulated for drinking water contaminants—and evaluated associations with birth outcomes throughout the conterminous U.S. Methods Using several machine learning models, including boosted regression trees (BRT) and random forest classification (RFC), developed from measured groundwater arsenic concentrations of ∼20,000 private wells, we characterized the probability that arsenic concentrations occurred within specific ranges in groundwater. Probabilistic model estimates and private well usage data were linked by county to all live birth certificates from 2016 (n = 3.6 million). We evaluated associations with gestational age and term birth weight using mixed-effects models, adjusted for potential confounders and incorporated random intercepts for spatial clustering. Results We generally observed inverse associations with term birth weight. For instance, when using BRT estimates, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 5 μg/L was associated with a −1.83 g (95% CI: −3.30, −0.38) lower term birth weight after adjusting for covariates. Similarly, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 10 μg/L was associated with a −2.79 g (95% CI: −4.99, −0.58) lower term birth weight. Associations with gestational age were null. Conclusion In this largest epidemiologic study of arsenic and birth outcomes to date, we did not observe associations of modeled arsenic estimates in private wells with gestational age and found modest inverse associations with term birth weight. Study limitations may have obscured true associations, including measurement error stemming from a lack of individual-level information on primary water sources, water arsenic concentrations, and water consumption patterns.

Environment International↗

USGS2021

Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies

Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.

Environmental Science and Technology↗

USGS2020

Methods of collection and quality assessment of arsenic data in well-water supplies in Maine, 2001–2 and 2006–7

The U.S. Geological Survey, in cooperation with the U.S. Centers for Disease Control and Prevention and the Maine Center for Disease Control and Prevention, assessed the chemical characteristics and the occurrence, distribution, and oxidation state of inorganic arsenic in drinking water from selected domestic well-water supplies in Maine in 2001–2 and 2006–7. The data collected provide support for evaluating arsenic-removal efficiencies of household water-purification systems and provide information to State and local officials that can be used in determining a water-treatment approach for the removal of arsenic from drinking water.

Maine↗

USGS2020

Reduction in drinking water arsenic exposure and health risk through arsenic treatment among private well households in Maine and New Jersey, USA

Over 2 million people in the United States (U.S.) drink water from private wells that contain arsenic (As) exceeding the U.S. Environmental Protection Agency (USEPA) Maximum Contaminant Level (MCL) of 10 micrograms per liter (μg/L). While there are a number of commercially available treatment technologies for removing As from drinking water, it is up to the private well households to decide whether to treat for As or not. However, how well existing treatment technologies perform in real world situations, and to what extent they reduce health risks, are not well understood. This study evaluates the effectiveness of household As treatment systems in southern-central Maine (ME, n=156) and northern New Jersey (NJ, n=94) and ascertains how untreated well water chemistry and other factors influence As removal. Untreated and treated water samples, as well as a treatment questionnaire, were collected. Most ME households in this study had point-of-use reverse-osmosis systems (POU RO), while in NJ, dual-tank point-of-entry (POE) whole house systems were popular. Arsenic treatment systems reduced well water arsenic concentrations ([As]) by up to two orders of magnitude, i.e. from a median of 71.7 to 0.8 μg/L and from a mean of 105 to 14.3 μg/L in ME, and from a median of 8.6 to 0.2 μg/L and a mean of 15.8 to 2.1 μg/L in NJ. More than half (53%) of the systems in ME reduced water [As] to below 1 µg/L, compared to 69% in NJ. The treatment system failure rates were 19% in ME (> USEPA MCL 10 µg/L) and 16% in NJ (> NJ standard 5 μg/L). In both states, the higher the untreated well water [As] and the As(III)/As ratio, the higher the rate of treatment failure. POE systems failed less than POU systems, as did the treatment systems installed and maintained by vendors than those by homeowners. The 7-fold reduction of [As] in the treated water reduced skin cancer risk alone from 3,765 to 514 in 1 million in ME, and from 568 to 75 in 1 million in NJ.

Maine, New Jersey↗

USGS2017

Estimating the high-arsenic domestic-well population in the conterminous United States

Arsenic concentrations from 20 450 domestic wells in the U.S. were used to develop a logistic regression model of the probability of having arsenic >10 μg/L (“high arsenic”), which is presented at the county, state, and national scales. Variables representing geologic sources, geochemical, hydrologic, and physical features were among the significant predictors of high arsenic. For U.S. Census blocks, the mean probability of arsenic >10 μg/L was multiplied by the population using domestic wells to estimate the potential high-arsenic domestic-well population. Approximately 44.1 M people in the U.S. use water from domestic wells. The population in the conterminous U.S. using water from domestic wells with predicted arsenic concentration >10 μg/L is 2.1 M people (95% CI is 1.5 to 2.9 M). Although areas of the U.S. were underrepresented with arsenic data, predictive variables available in national data sets were used to estimate high arsenic in unsampled areas. Additionally, by predicting to all of the conterminous U.S., we identify areas of high and low potential exposure in areas of limited arsenic data. These areas may be viewed as potential areas to investigate further or to compare to more detailed local information. Linking predictive modeling to private well use information nationally, despite the uncertainty, is beneficial for broad screening of the population at risk from elevated arsenic in drinking water from private wells.

Environmental Science & Technology↗