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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., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

There is little published literature on the efficacy of strategies to reduce exposure to residential well water arsenic. The objectives of our study were to: 1) determine if water arsenic remained a significant exposure source in households using bottled water or point-of-use treatment systems; and 2) evaluate the major sources and routes of any remaining arsenic exposure. We conducted a cross-sectional study of 167 households in Maine using one of these two strategies to prevent exposure to arsenic. Most households included one adult and at least one child. Untreated well water arsenic concentrations ranged from <10 μg/L to 640 μg/L. Urine samples, water samples, daily diet and bathing diaries, and household dietary and water use habit surveys were collected. Generalized estimating equations were used to model the relationship between urinary arsenic and untreated well water arsenic concentration, while accounting for documented consumption of untreated water and dietary sources. If mitigation strategies were fully effective, there should be no relationship between urinary arsenic and well water arsenic. To the contrary, we found that untreated arsenic water concentration remained a significant (p ≤ 0.001) predictor of urinary arsenic levels. When untreated water arsenic concentrations were <40 μg/L, untreated water arsenic was no longer a significant predictor of urinary arsenic. Time spent bathing (alone or in combination with water arsenic concentration) was not associated with urinary arsenic. A predictive analysis of the average study participant suggested that when untreated water arsenic ranged from 100 to 500 μg/L, elimination of any untreated water use would result in an 8%-32% reduction in urinary arsenic for young children, and a 14%-59% reduction for adults. These results demonstrate the importance of complying with a point-of-use or bottled water exposure reduction strategy. However, there remained unexplained, water-related routes of exposure., (Copyright © 2015 Elsevier B.V. All rights reserved.)

BackgroundPrenatal 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.MethodsUsing 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.ResultsWe 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.ConclusionIn 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.

Background: Private water systems are more likely to have nitrate levels above the maximum contaminant level (MCL). Pregnant women are considered vulnerable to the effects of exposure to high levels of nitrates in drinking water due to their altered physiological states. The level of methemoglobin in the blood is the biomarker often used in research for assessing exposure to nitrates. The objective of this study was to assess methemoglobin levels and examine how various factors affected methemoglobin levels during pregnancy. We also examined whether differences in water use practices existed among pregnant women based on household drinking water source of private vs. public supply. Methods: A longitudinal study of 357 pregnant women was conducted. Longitudinal regression models were used to examine changes and predictors of the change in methemoglobin levels over the period of gestation. Results: Pregnant women showed a decrease in methemoglobin levels with increasing gestation although <1% had levels above the physiologic normal of 2% methemoglobin, regardless of the source of their drinking water. The multivariable analyses did not show a statistically significant association between methemoglobin levels and the estimated nitrate intake from tap water among pregnant women around 36 weeks gestation (β = 0.046, p = 0.986). Four women had tap water nitrate levels above the MCL of 10 mg/L. At enrollment, a greater proportion of women who reported using water treatment devices were private wells users (66%) compared to public system users (46%) (p < 0.0001). Also, a greater proportion of private well users (27%) compared to public system users (13%) were using devices capable of removing nitrate from water (p < 0.0001). Conclusion: Pregnant women potentially exposed to nitrate levels primarily below the MCL for drinking water were unlikely to show methemoglobin levels above the physiologic normal. Water use practices such as the use of treatment devices to remove nitrates varied according to water source and should be considered in the assessment of exposure to nitrates in future studies. [ABSTRACT FROM AUTHOR]

Objective—To assess the risk of bladder cancer in dogs from exposure to drinking water disinfection by-products and determine whether dogs could serve as sentinels for human bladder cancer associated with such exposures. Design—Case-control study. Animals—100 dogs with cancer of the urinary bladder and 100 control dogs. Procedures—Case and control dogs were frequency-matched by age (within 2 years) and sex. Owners of dogs enrolled provided verbal informed consent and were interviewed by telephone. The telephone questionnaire included a complete residence history for each dog. Each dog's total exposure history to trihalomethanes was reconstructed from its residence history and corresponding drinking water utility company data. Results—No association was detected between increasing years of exposure to chlorinated drinking water and risk of bladder cancer. Dogs with bladder cancer were exposed to higher total trihalomethanes concentrations than control dogs; however, the difference was not significant. Conclusions and Clinical Relevance—Although humans and their dogs live in the same household, the activity patterns of dogs may lead to lower exposures to household tap water. Thus, although exposure to disinfection by-products in tap water may be a risk factor for human bladder cancer, this may not be true for canine bladder cancer at the concentrations at which dogs are exposed. [ABSTRACT FROM AUTHOR]

The article offers insights on the unregulated drinking water initiative in the U.S. for environmental protection and safeguarding public health. The authors stress the need to assess and protect the drinking water in the country. They suggest adding resources allocation and identifying information available on unregulated drinking water sources.

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 pg/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 pg/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 pg/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. [ABSTRACT FROM AUTHOR]