Manganese exposure from spring and well waters in the Shenandoah Valley: interplay of aquifer lithology, soil composition, and redox conditions.

Manganese (Mn) is of particular concern in groundwater, as low-level chronic exposure to aqueous Mn concentrations in drinking water can result in a variety of health and neurodevelopmental effects. Much of the global population relies on drinking water sourced from karst aquifers. Thus, we seek to assess the relative risk of Mn contamination in karst by investigating the Shenandoah Valley, VA region, as it is underlain by both karst and non-karst aquifers and much of the population relies on water wells and spring water. Water and soil samples were collected throughout the Shenandoah Valley, to supplement pre-existing well water and spring data from the National Water Information System and the Virginia Household Water Quality Program, totaling 1815 wells and 119 springs. Soils were analyzed using X-ray fluorescence and Mn K-Edge X-ray absorption near-edge structure spectroscopy. Factors such as soil type, soil geochemistry, and aquifer lithology were linked with each location to determine if correlations exist with aqueous Mn concentrations. Analyzing the distribution of Mn in drinking water sources suggests that water wells and springs within karst aquifers are preferable with respect to chronic Mn exposure, with < 4.9% of wells and springs in dolostone and limestone aquifers exceeding 100 ppb Mn, while sandstone and shale aquifers have a heightened risk, with > 20% of wells exceeding 100 ppb Mn. The geochemistry of associated soils and spatial relationships to various hydrologic and geologic features indicates that water interactions with aquifer lithology and soils contribute to aqueous Mn concentrations. Relationships between aqueous Mn in spring waters and Mn in soils indicate that increasing aqueous Mn is correlated with decreasing soil Mn(IV). These results point to redox conditions exerting a dominant control on Mn in this region.

Incidence of per- and polyfluoroalkyl substances (PFAS) in private drinking water supplies in Southwest Virginia, USA.

Per- and polyfluoroalkyl substances (PFAS) are a class of man-made contaminants of human health concern due to their resistance to degradation, widespread environmental occurrence, bioaccumulation in living organisms, and potential negative health impacts. Private drinking water supplies may be uniquely vulnerable to PFAS contamination in impacted areas, as these systems are not protected under federal regulations and often include limited treatment or remediation, if contaminated, prior to use. The goal of this study was to determine the incidence of PFAS contamination in private drinking water supplies in two counties in Southwest Virginia, USA (Floyd and Roanoke) that share similar bedrock geologies, are representative of different state Department of Health risk categories, and to examine the potential for reliance on citizen-science based strategies for sample collection in subsequent efforts. Samples for inorganic ions, bacteria, and PFAS analysis were collected on separate occasions by participants and experts at the home drinking water point of use (POU) for comparison. Experts also collected outside tap samples for analysis of 30 PFAS compounds. At least one PFAS was detectable in 95 % of POU samples collected (n = 60), with a mean total PFAS concentration of 23.5 ± 30.8 ppt. PFOA and PFOS, two PFAS compounds which presently have EPA health advisories, were detectable in 13 % and 22 % of POU samples, respectively. On average, each POU sample contained >3 PFAS compounds, and one sample contained as many as 8 compounds, indicating that exposure to a mixture of PFAS in drinking water may be occurring. Although there were significant differences in total PFAS concentrations between expert and participant collected samples (Wilcoxon, alpha = 0.05), collector bias was inconsistent, and may be due to the time of day of sampling (i.e. morning, afternoon) or specific attributes of a given home. Further research is required to resolve sources of intra-sample variability.

Developing early warning systems to predict water lead levels in tap water for private systems.

Lead is a chemical contaminant that threatens public health, and high levels of lead have been identified in drinking water at locations across the globe. Under-served populations that use private systems for drinking water supplies may be at an elevated level of risk because utilities and governing agencies are not responsible for ensuring that lead levels meet the Lead and Copper Rule at these systems. Predictive models that can be used by residents to assess water quality threats in their households can create awareness of water lead levels (WLLs). This research explores and compares the use of statistical models (i.e., Bayesian Belief classifiers) and machine learning models (i.e., ensemble of decision trees) for predicting WLLs. Models are developed using a dataset collected by the Virginia Household Water Quality Program (VAHWQP) at approximately 8000 households in Virginia during 2012-2017. The dataset reports laboratory-tested water quality parameters at households, location information, and household and plumbing characteristics, including observations of water odor, taste, discoloration. Some water quality parameters, such as pH, iron, and copper, can be measured at low resolution by residents using at-home water test kits and can be used to predict risk of WLLs. The use of at-home water quality test kits was simulated through the discretization of water quality parameter measurements to match the resolution of at-home water quality test kits and the introduction of error in water quality readings. Using this approach, this research demonstrates that low-resolution data collected by residents can be used as input for models to estimate WLLs. Model predictability was explored for a set of at-home water quality test kits that observe a variety of water quality parameters and report parameters at a range of resolutions. The effects of the timing of water sampling (e.g., first-draw vs. flushed samples) and error in kits on model error were tested through simulations. The prediction models developed through this research provide a set of tools for private well users to assess the risk of lead contamination. Models can be implemented as early warning systems in citizen science and online platforms to improve awareness of drinking water threats.

Developing a framework for classifying water lead levels at private drinking water systems: A Bayesian Belief Network approach.

The presence of lead in drinking water creates a public health crisis, as lead causes neurological damage at low levels of exposure. The objective of this research is to explore modeling approaches to predict the risk of lead at private drinking water systems. This research uses Bayesian Network approaches to explore interactions among household characteristics, geological parameters, observations of tap water, and laboratory tests of water quality parameters. A knowledge discovery framework is developed by integrating methods for data discretization, feature selection, and Bayes classifiers. Forward selection and backward selection are explored for feature selection. Discretization approaches, including domain-knowledge, statistical, and information-based approaches, are tested to discretize continuous features. Bayes classifiers that are tested include General Bayesian Network, Naive Bayes, and Tree-Augmented Naive Bayes, which are applied to identify Directed Acyclic Graphs (DAGs). Bayesian inference is used to fit conditional probability tables for each DAG. The Bayesian framework is applied to fit models for a dataset collected by the Virginia Household Water Quality Program (VAHWQP), which collected water samples and conducted household surveys at 2,146 households that use private water systems, including wells and springs, in Virginia during 2012 and 2013. Relationships among laboratory-tested water quality parameters, observations of tap water, and household characteristics, including plumbing type, source water, household location, and on-site water treatment are explored to develop features for predicting water lead levels. Results demonstrate that Naive Bayes classifiers perform best based on recall and precision, when compared with other classifiers. Copper is the most significant predictor of lead, and other important predictors include county, pH, and on-site water treatment. Feature selection methods have a marginal effect on performance, and discretization methods can greatly affect model performance when paired with classifiers. Owners of private wells remain disadvantaged and may be at an elevated level of risk, because utilities and governing agencies are not responsible for ensuring that lead levels meet the Lead and Copper Rule for private wells. Insight gained from models can be used to identify water quality parameters, plumbing characteristics, and household variables that increase the likelihood of high water lead levels to inform decisions about lead testing and treatment.

Evaluating Geologic Sources of Arsenic in Well Water in Virginia (USA).

We investigated if geologic factors are linked to elevated arsenic (As) concentrations above 5 μg/L in well water in the state of Virginia, USA. Using geologic unit data mapped within GIS and two datasets of measured As concentrations in well water (one from public wells, the other from private wells), we evaluated occurrences of elevated As (above 5 μg/L) based on geologic unit. We also constructed a logistic regression model to examine statistical relationships between elevated As and geologic units. Two geologic units, including Triassic-aged sedimentary rocks and Triassic-Jurassic intrusives of the Culpeper Basin in north-central Virginia, had higher occurrences of elevated As in well water than other geologic units in Virginia. Model results support these patterns, showing a higher probability for As occurrence above 5 μg/L in well water in these two units. Due to the lack of observations (<5%) having elevated As concentrations in our data set, our model cannot be used to predict As concentrations in other parts of the state. However, our results are useful for identifying areas of Virginia, defined by underlying geology, that are more likely to have elevated As concentrations in well water. Due to the ease of obtaining publicly available data and the accessibility of GIS, this study approach can be applied to other areas with existing datasets of As concentrations in well water and accessible data on geology.

Associations between fecal indicator bacteria prevalence and demographic data in private water supplies in Virginia.

Over 1.7 million Virginians rely on private water sources to provide household water. The heaviest reliance on these systems occurs in rural areas, which are often underserved with respect to available financial resources and access to environmental health education. This study aimed to identify potential associations between concentrations of fecal indicator bacteria (FIB) (coliforms, Escherichia coli) in over 800 samples collected at the point-of-use from homes with private water supply systems and homeowner-provided demographic data (household income and education). Of the 828 samples tested, 349 (42%) of samples tested positive for total coliform and 55 (6.6%) tested positive for E. coli. Source tracking efforts targeting optical brightener concentrations via fluorometry and the presence of a human-specific Bacteroides marker via quantitative real-time polymerase chain reaction (qPCR) suggest possible contamination from human septage in over 20 samples. Statistical methods implied that household income has an association with the proportion of samples positive for total coliform, though the relationship between education level and FIB is less clear. Further exploration of links between demographic data and private water quality will be helpful in building effective strategies to improve rural drinking water quality.

Quantitative analysis of microbial contamination in private drinking water supply systems.

Over one million households rely on private water supplies (e.g. well, spring, cistern) in the Commonwealth of Virginia, USA. The present study tested 538 private wells and springs in 20 Virginia counties for total coliforms (TCs) and Escherichia coli along with a suite of chemical contaminants. A logistic regression analysis was used to investigate potential correlations between TC contamination and chemical parameters (e.g. NO3(-), turbidity), as well as homeowner-provided survey data describing system characteristics and perceived water quality. Of the 538 samples collected, 41% (n = 221) were positive for TCs and 10% (n = 53) for E. coli. Chemical parameters were not statistically predictive of microbial contamination. Well depth, water treatment, and farm location proximate to the water supply were factors in a regression model that predicted presence/absence of TCs with 74% accuracy. Microbial and chemical source tracking techniques (Bacteroides gene Bac32F and HF183 detection via polymerase chain reaction and optical brightener detection via fluorometry) identified four samples as likely contaminated with human wastewater.