Geology topics

Matthew O. Gribble

Publications and source records attributed to Matthew O. Gribble.

4 recordsLinked to original sources

USGS2026

Drinking water arsenic, urinary arsenic biomarkers, and cognitive impairment in the REGARDS study

Background There are several pathways by which inorganic arsenic (iAs) exposure can affect cognition among adults. Few epidemiologic studies evaluate iAs in water and inter-individual differences in urinary arsenic toxicokinetics. We aimed to estimate the association between individual-level urinary arsenic biomarkers, county-level iAs in drinking water, and cognitive impairment in a cohort of Black and White Americans. Methods We evaluated the association between county-level iAs in water and urinary iAs metabolites with incident cognitive impairment in REasons for Geographic and Racial Differences in Stroke (REGARDS). Participants were linked to county-level concentrations of arsenic in public water (n = 15,516) and county-level probabilities of private well arsenic exceeding 10 μg/L (n = 20,448). In addition, urinary concentrations of iAs, monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) were measured in a subset of participants (n = 1,013). Cognitive impairment was determined by the Six Item Screener (SIS) and the Enhanced Cognitive Battery (ECB). We used multivariable logistic regression to estimate odds ratios. Results Approximately 9 % of participants in REGARDS developed incident cognitive impairment on the SIS over 10 years of follow-up. County-level public water iAs concentrations and private well iAs probabilities were not associated with increased odds of cognitive impairment. Higher concentrations of MMA (OR: 1.74 [95 % CI: 1.22, 2.49]) and iAs (OR: 1.58 [95 % CI: 1.12, 2.22]) in urine were associated with greater odds of cognitive impairment on the SIS. Results for the ECB were similar. Conclusions iAs exposure may be associated with cognitive impairment. Our findings highlight the importance of considering multiple measures of iAs exposure.

Environmental Research↗

USGS2023

Water, water everywhere, but every drop unique: Emerging challenges in the science to understand the role of contaminants in management of drinking water supplies

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

GeoHealth↗

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↗