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. 2023 Nov 3;57(45):17452–17464. doi: 10.1021/acs.est.3c05192

Investigation of Sources of Fluorinated Compounds in Private Water Supplies in an Oil and Gas-Producing Region of Northern West Virginia

Helen G Siegel †,*, Sara L Nason ‡, Joshua L Warren §, Ottavia Prunas ∥, Nicole C Deziel §, James E Saiers †
PMCID: PMC10653085  PMID: 37923386

Abstract

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Per- and polyfluoroalkyl substances (PFASs) are a class of toxic organic compounds that have been widely used in consumer applications and industrial activities, including oil and gas production. We measured PFAS concentrations in 45 private wells and 8 surface water sources in the oil and gas-producing Doddridge, Marshall, Ritchie, Tyler, and Wetzel Counties of northern West Virginia and investigated relationships between potential PFAS sources and drinking water receptors. All surface water samples and 60% of the water wells sampled contained quantifiable levels of at least one targeted PFAS compound, and four wells (8%) had concentrations above the proposed maximum contaminant level (MCL) for perfluorooctanoic acid (PFOA). Individual concentrations of PFOA and perfluorobutanesulfonic acid exceeded those measured in finished public water supplies. Total targeted PFAS concentrations ranged from nondetect to 36.8 ng/L, with surface water concentrations averaging 4-fold greater than groundwater. Semiquantitative, nontargeted analysis showed concentrations of emergent PFAS that were potentially higher than targeted PFAS. Results from a multivariate latent variable hierarchical Bayesian model were combined with insights from analyses of groundwater chemistry, topographic characteristics, and proximity to potential PFAS point sources to elucidate predictors of PFAS concentrations in private wells. Model results reveal (i) an increased vulnerability to contamination in upland recharge zones, (ii) geochemical controls on PFAS transport likely driven by adsorption, and (iii) possible influence from nearby point sources.

Keywords: PFAS, groundwater, nontargeted analysis, rural communities, public health, exposure

Short abstract

We investigated the distribution of PFAS in groundwater and surface water in northern West Virginia using targeted and nontargeted analysis and identified factors influencing PFAS concentrations in private water wells.

Introduction

Per- and polyfluoroalkyl substances (PFASs) are a family of over 10,000 synthetic organic compounds,1 known for their persistence in the environment and superior oil-, grease-, and water-resistant properties.2 Utilized in a diverse range of consumer and industrial applications, PFAS have become a near ubiquitous environmental pollutant, particularly in drinking water.2−-5 Mounting evidence of adverse human-health impacts associated with certain PFAS at sub 10 ng/L levels6−8 has underscored the importance of understanding sources of PFAS to drinking water supplies4 and factors influencing their transport in the environment.

Numerous anthropogenic activities may contribute PFAS to surface waters and groundwaters alike. Disperse sources of PFAS contamination include stack emissions from fluoropolymer manufacturing facilities9,10 and agricultural applications of biosolids.11,12 Point sources of PFAS include discharges of fire-fighting foams (AFFFs) at airports, military bases, and power plants,13−16 as well fluid discharges from chemical manufacturing and metal-plating facilities.14,17−19 Industrial and consumer waste streams14,20−22 associated with sewage treatment facilities, combined sewer overflows, and landfills represent additional point sources that may contribute PFAS directly or indirectly to surface water and groundwater. Moreover, PFASs have been used in the oil and gas industry for decades as corrosion inhibitors on pipes and drilling equipment, to enhance oil recovery, and as additives to prevent evaporation of stored fuels.14,23,24 Limited scientific attention has focused on potential environmental impacts from the use of PFAS and their precursors in the oil and gas industry,25−27 despite documented cases of water quality impairments related to unconventional oil and gas (UOG) development for other contaminants (e.g., brines, diesel range organics, radium)28−36 that could co-occur with PFAS.

While numerous studies have examined PFAS occurrence in public water systems,3,4,37−45 research on PFAS in private water supplies is just beginning.3,4,9,45−48 Within rural communities of the northern Appalachian Basin (USA), more than one-fourth of households rely on private wells for drinking water and other domestic purposes.49 These wells, like most private wells, are infrequently tested,45,50 but they may be at a particular risk from PFAS contamination owing to this region’s legacy of PFAS-generating industrial activities coupled with the recent acceleration in UOG development.51 According to a recent study, 24% of 279 public water systems tested in the northern Appalachian state of West Virginia exhibited concentrations in source waters of at least one PFAS compound above the study’s minimum reporting level (10 ng/L)41; however, the sources of PFAS could not be determined, and PFAS concentration in private well waters were not measured. Consequently, questions remain regarding PFAS levels in private water wells and whether these concentrations are reflective of those observed in public water systems. Moreover, measurements and approaches suitable for elucidating sources of PFAS in private wells in West Virginia and elsewhere are scarce,45 and relationships between PFAS occurrence and observable factors, such as private well setting, hydrogeologic features, and compound-specific sorptive characteristics, are poorly known.

In this work, we explore a newly collected data set of 45 private groundwater samples and 8 surface water samples from five UOG producing counties (Doddridge, Marshall, Ritchie, Tyler, and Wetzel Counties) in northern West Virginia. Through analyses of these data, we seek to answer the following questions: (i) Which PFAS (historic and emergent) are present in private groundwater supplies? (ii) How do measured PFAS concentrations compare to safe drinking water standards and those observed in public water supplies? (iii) Do PFAS detections trend discernibly with groundwater-chemical characteristics, geologic and topographic features, or anthropogenic activities? and (iv) Can these relationships (if any) be used as a basis for elucidating probable PFAS sources to private wells? To address these questions, we evaluated measurements of 21 targeted PFAS and semiquantitative nontargeted results derived from specialized PFAS annotation software packages52 in the context of an extensive inorganic water quality data set (e.g., major cations, major anions, and trace metals),53 measured PFAS concentrations in public drinking water, and relevant topographic, geochemical, and anthropogenic factors. A multivariate latent-variable hierarchical Bayesian model with spatial random effects was applied to assess the significance of proposed predictors and reveal controls on the PFAS concentrations. Potential PFAS sources were considered for their feasibility by considering regression model results and proposed mechanisms of contamination.

Methods

Geologic Setting and Topographic Controls on Groundwater Chemistry

The study area, which covers parts of Doddridge, Marshall, Ritchie, Tyler, and Wetzel Counties, is predominantly rural and forested (76.7% deciduous forest; 8.4% mixed forest 8.4), with small pockets of development and an industrialized corridor to the west along the Ohio River (Figures 1 and S1).54 Within this portion of the Appalachian Plateau Physiographic Province, flat-lying (∼6 m/km eastward dip) to gently folded sedimentary bedrock is interbedded with coal and includes oil and natural gas bearing formations.55−57 An estimated 20,000 individuals in the study area are solely reliant on private water supplies for hygienic and drinking water purposes.49

Figure 1.

Figure 1

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Overview of the study area and locations of (A) groundwater and surface water sample collection in relation to (B) areal extents of the Marcellus Shale and Utica-Point Pleasant Shale. Samples were collected from five counties (delineated in purple) in northern West Virginia (C).

Groundwater chemistry within the region varies with the topographic position, with groundwater gradually transitioning from Ca–HCO3 type waters in upland areas to mixed Ca–Na–HCO3 and Na–HCO3 water types in valley areas. The Ca-dominated water types of upland areas exhibit noble gas signatures (He, 4He/20Ne, and 3He/4He ratios) and redox conditions (elevated NO3 and DO%) that are indicative of relatively recent recharge.53 Ca-dominated upland wells appear more prone to contamination from surface activities (e.g., road salting and agricultural practices) than private wells situated in valleys.53 Valley wells yield groundwaters that are characteristic of reducing conditions and that have extensively interacted with aquifer solids. These waters have noble gas ratios more closely resembling crustal sources than atmospheric sources and, owing to the effects of ion-exchange reactions, tend to be depleted in Ca and Mg and enriched in Na. Moreover, groundwaters from some valley wells exhibit Cl/Br ratios that are consistent with mixing between shallow meteoric water and deeper basin brines.53

Anthropogenic Activities and Potential PFAS Sources

The study area is underlain by the Marcellus and Utica-Point Pleasant shale formations and operations supporting UOG development have expanded rapidly over the past decade.51 At the time of sampling, there were over 3,000 completed UOG wells in production, 13% of which were completed between 2018 and 202058 (Figure S2). On average, private wells sampled in this study were located 2.6 km away from a UOG well, with 14 water wells located within one km of an active well. No private wells were situated more than 5 km from an active UOG well. In addition to UOG well pads, over 2200 km of pipelines and gathering lines, 167 freshwater and wastewater retention ponds, and 2 brine disposal sites actively support the UOG industry within the study counties59 (Figure S2). Oil and gas production from conventional formations in West Virginia has declined over the past century, but commercial conventional oil and gas (COG) wells remain active in the region, primarily targeting Mississippian- and Lower Devonian-age formations. Over 14,000 COG wells are reported as active in the study region,58 and on average, sampled groundwater wells were located only 600 m from an active COG well, with 11 water wells within 1 km of an active COG well.

Two municipal solid waste landfills60 operate in the study region, both within 5 km of the Ohio River and none within two kilometers of a sample location (Figure S3). Fifty locations are permitted to manage industrial and municipal wastewater61 in the study region. On average, samples were located 6.5 km from a permitted wastewater treatment facility, with only two samples located within one kilometer of wastewater treatment operations and four samples located within two kilometers. Combined sewer overflows (CSOs) are largely absent in the study area, with no samples located within 5 km of a known CSO discharge location.62 No major airports63 or military bases64 reside within the study area. Two smaller airports and three national guard and army stations that may store AFFF are located within the study area, but only one sampling location falls within five km of these facilities.

More than 330 national pollutant discharge elimination system (NPDES) permits have been issued for the study area,65 with 40% of these regulating point-source discharges into the Ohio River or surface waterways within 1 km of the Ohio river (Figure S4). Industries located along the Ohio River that hold NPDES permits include metal plating, chemical manufacturing, coal and natural gas–fired power generation, natural gas processing, and shipping. At distances greater than 1 km from the Ohio River, oil and gas development is the dominant discharging industry, with 55% of NPDES permits related to oil and gas activities (Figure S4). Eight samples fall within 1 km of an industrial NPDES permit, with distances ranging from just under 300 m from a discharge point to over 9.5 km.

The study area is northeast of the Chemours Washington Works Plant in Parkersburg, West Virginia, a site of historic perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) releases and where stack emissions and surface water discharges of hexafluoropropylene oxide-dimer acid (HFPO–DA) continue to occur.10,17,66 Soil and surface water contamination is most substantial in immediate proximity to the facility, but airborne transport has been shown to have far reaching effects on both soil and water (Figure S5).10 The private wells sampled in this study are located between 60 and 115 km downwind from the facility.

Sample Collection and Analysis

Well water samples from 55 households in northern West Virginia and surface water samples from 13 sites on the Ohio River and its tributaries were collected in October 2020 (Figure 1). Sample collection, chemical-analysis methods, and quality control protocols and checks are described in detail in the Supporting Information (Methods 1; Table S1–S5) and summarized here. All well water samples were collected in 125 mL high-density polyethylene (HDPE) bottles using polypropylene tubing at accessible spigots upstream of any home treatment system. In-line measurements of temperature, pH, dissolved oxygen, and specific conductivity were allowed to stabilize prior to sample collection to ensure a representative sample. Samples for the determination of inorganic (major anions, major cations, and trace metals) constituents were collected in tandem with those for PFAS at each private groundwater well. Surface water samples were collected directly from water sources in 125 mL HDPE bottles. A full set of field blanks was collected near sample locations every day using Milli-Q water for a total of 21 sets of field blanks.

Samples for PFAS analysis were kept below 4 °C until extraction using a weak anion exchange (WAX) solid phase extraction (SPE) cartridge following a modified version of US Environmental Protection Agency (EPA) Method 533.67 Extracted samples were analyzed using an Ultimate 3000 liquid chromatograph equipped with a Hypersil Gold C18 column coupled with a Thermo Scientific Q-Exactive Orbitrap mass spectrometer with negative electrospray ionization in Full MS/data-dependent MS2 (ddMS2) acquisition mode as in our previous work.13,68 For each batch of 20 extracted samples, fortified and unfortified laboratory blanks were used to control for any possible contamination introduced by the extraction methods. Targeted data analysis included 25 perfluoroalkyl carboxylic acids (PFCAs), perfluorosulfonic acids (PFSAs), and perfluoroalkyl ethers (Table S1) and was completed in Tracefinder software (Thermo Scientific) version 4.1. Quantification was based on isotope dilution as in EPA 53367 (Table S2). The limit of detection (LOD) of each analyte was defined by 3.3 x the standard deviation of 7 replicate injections of the 0.5 ng/L spike, and the limit of quantification (LOQ) was defined by 10 x the standard deviation of 7 replicates.69

Twenty-one targeted PFAS, 45 groundwater, and 8 surface water samples met quality control standards for further analysis. Three target analytes (4:2 FtS, 6:2 FtS, and 8:2 FtS) were excluded from further analysis based on concentrations present in field and laboratory blanks, two target analytes (HFPO–DA and 6:2 FtS; Table S5) were excluded because they exceeded EPA 53367 recovery targets (between 70 and 130% based on peak area) during initial method recovery experiments, and five surface water samples and 10 groundwater samples were excluded based on low internal standard recoveries (below 70% based on peak area). Six analytes (PFHxA, PFHpA, PFOA, perfluorobutanesulfonic (PFBS) acid, NaDONA, and 11Cl-PF3OUdS) were identified in a subset of field blanks at levels equal to or greater than one-third of the reported LOQ and subsequent determinations of these six analytes in paired samples (n = 20) were excluded from further analysis according to EPA 533.67 Two analytes (NaDONA and PFHpA) were identified in one laboratory blank below LOQ.

Nontargeted Workflow and Semiquantitation

The full MS and ddMS2 scans were used to identify additional nontargeted compounds that exhibit features consistent with PFAS. Initial peak-picking was conducted in Compound Discover version 3.1 (Thermo Scientific) followed by compound annotation using FluoroMatch Modular52 (version 2.6). Homologous series with more than three members, at least one exact mass match in the EPA ToxCast70 database (downloaded June 21, 2022), and an increasing m/z-retention time trend were selected for further investigation using TraceFinder version 4.1 (Thermo Scientific). Series and fragments were inspected and vetted manually for structural consistency, inconsistent annotations, and peak shape. Series with one or more homologues that did not meet the quality control criteria were excluded. Peak areas were calculated in TraceFinder and semiquantitative concentrations for each annotated compound were calculated using an average value from calibration curves from a panel of three surrogate standards.71,72 Surrogate standards were chosen based on the closest mass, retention time, and carbon-chain length match and results were averaged to reduce the inherent uncertainty of the semiquantitative results.71−74 Concentrations of nontargeted compounds were compared to levels identified in field and laboratory blanks and excluded if present at levels less than 3-fold blank responses.75 A full description of nontargeted workflow is described in the supplement (Supplementary Methods 2).

Statistical Analyses and Regression Model Development

A multivariate, latent-variable regression model with spatial random effects was created to estimate associations between the concentrations of the 21 targeted PFAS and multiple predictors of interest while accounting for (i) censored concentrations (i.e., below the limits of quantification and detection), (ii) correlation between compounds collected from the same geographic location, and (iii) residual spatial correlation. A latent-variable model was chosen to address the high rate of censoring in the data, as the model allows for the imputation of missing data, which may increase the power to detect significant associations in the analysis. The high rate of censoring further limits the ability to fit compound-specific regressions, and thus, information from all 21 targeted PFAS was combined to fit a single regression under the assumption that each compound has the same association with a selected predictor (i.e., one set of regression parameters is shared across all compounds). This assumption allows for consideration of more general patterns between multiple covariates and PFAS concentrations in private well waters without the use of an overly simplistic imputation strategy but prevents the model from making inferences at the compound-specific level (despite using compound-specific data). The results are regression parameter estimates that are similar to those that would be observed using the sum or average of PFAS concentrations at a specific location as the dependent variable but with a more robust handling of censored values. At each unique geographic location, a spatially correlated random effect parameter is included in the framework and is modeled using a Gaussian process with exponential correlation structure76 such that concentrations from locations separated by shorter distances are more correlated a priori. Finally, an unstructured covariance matrix is used to describe the correlation between compounds measured at the same spatial location. Correctly characterizing the correlation in the data is important for obtaining an accurate statistical inference for the regression parameters.

We fitted the newly developed model in a Bayesian setting using Markov chain Monte Carlo sampling techniques. We specified weakly informative prior distributions for all model parameters. Convergence was assessed by visual inspection of individual parameter trace plots as well as Geweke’s diagnostic.77 Effective sample size was calculated to determine if an adequate number of posterior samples had been collected before making inference. Full details on the model and prior distributions are provided in the supplement (Supplementary Discussion 3).

Predictors considered for inclusion in the model included inorganic constituents, water quality indicators, distance metrics, PFAS size, surrounding land cover classification, and topographic position. Nearest neighbor distances between sample locations and potential PFAS sources were calculated using ArcGIS Pro v.3.0.2 from publicly available data sources (Table S6). Potential sources include those related to industrial and consumer waste streams, industrial activities, general infrastructure, and storage of AFFFs (Table S7). All shapefiles were transformed to NAD83 UTM 17N prior to distance calculations. The inverse distance to upstream sources (idups) was calculated for all potential sources using a tool developed by Soriano et al.78 to evaluate the influence of upgradient sources on PFAS detections. Directional distance metrics such as idups better account for relevant contaminant transport mechanisms and can improve inferences on contaminant source-receptor relationships.78,79 PFAS size (short- vs long-chain) was assigned by carbon-chain length as defined by the United Nations Environment Programme80 and the Interstate Technology Regulatory Council (ITRC).81 A full list of predictors considered is presented in Table S7.

Predictors retained in the final model were selected based on their prevalence in the study area (within 2 km of at least two sample locations) and results from initial bivariate analyses exploring the correlation between the sum of all targeted PFAS concentrations and potential predictors. Correlations were calculated using nonparametric methods (Spearman and Kruskal–Wallis) and assuming detections below the LOQ were equal to zero. Predictors that exhibited significant (p-value < 0.05) associations with the sum of all targeted PFAS were considered for the final model. In the case of a strong correlation between predictors (Spearman’s Rank > ±0.5, p-value < 0.05), only the predictor with the most potential explanatory power was retained in the final model. All statistical analyses were conducted using R v.4.1.3.

Results and Discussion

Targeted PFAS Measurements

Of the 21 targeted PFAS, all were identified above their respective LOD (Table S8) in at least one sample, and eight were measured at concentrations above their respective LOQ (Table S8) in at least one sample (Figure 2 and Figure S6). Detections (>LOD) of at least one targeted PFAS occurred in all samples, with PFOA and PFBS being detected most frequently in both surface water and well water. That PFAS were detected so frequently below quantification levels, emphasizes the importance of advancing analytical capabilities for PFAS quantification, particularly with progressively decreasing health guidelines for PFOA and PFOS.

Figure 2.

Figure 2

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Frequency of targeted PFAS observations above the respective LOQ (solid) and above the respective LOD but below the respective LOQ (horizontal and diagonal lines) in well water and surface water samples. PFBA, PFPeA, PFOA, PFBS, and PFOS were detected above LOQs in all surface water samples. Long-chain PFCAS (PFNA, PFDA, PFUnA, PFDoA) as defined by the United Nations Environment Programme80 and the ITRC81 were not measured above the LOQ in any samples.

Quantifiable concentrations (>LOQ) of one or more targeted PFAS were present in 60% (n = 27) of well water samples, with PFOA, NaDONA, PFBA, and PFHxA measured most frequently in groundwater. All surface water samples contained quantifiable (>LOQ) concentrations of at least six targeted PFAS, mainly a mixture of PFBA, PFPeA, PFHxA, PFOA, PFBS, and PFOS (Figure 3). All samples collected from the Ohio River display a consistent mixture of PFBA, PFPeA, PFHxA, PFOA, PFBS, and PFOS with some minor additions of NaDONA. Surface water samples collected from remote tributaries and private water wells show more heterogeneity in composition, even in spatial proximity, likely indicating a diversity of sources across the study area and the influence of transport mechanisms on PFAS occurrence throughout the region. Neither groundwater nor surface water samples had quantifiable concentrations of more than 7 targeted compounds (see Supplementary Discussion 1).

Figure 3.

Figure 3

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Relative proportions of quantifiable PFAS concentrations in surface water (A) and groundwater (B). Quantifiable concentrations of at least one targeted PFAS were present in all surface water samples and 27 groundwater samples. Mixtures of targeted PFAS were more diverse and markedly different from those observed in surface water, even in spatial proximity.

Total concentrations of targeted PFAS ranged from nondetect to 36.8 ng/L, with total targeted concentrations in groundwater five times lower on average (x̅ = 4.41 ng/L) than in surface water (x̅ = 24.61 ng/L) (Figure S7). The sample exhibiting the highest total targeted PFAS concentrations (36.8 ng/L) was collected from a public fishing spot on a minor tributary to the Ohio River (surface water) and contained 1.7 times more total PFAS than the next most-concentrated sample. Land use surrounding the sample consisted mainly of deciduous and mixed forested land uses (86%). That such high concentrations exist in surface waters within relatively undisturbed regions highlights the importance of routine monitoring of PFAS and improved source attribution efforts to identify and remediate major contributing sources in these areas.

Four (8.8%) well water samples and two (25%) surface water samples had concentrations above proposed maximum contaminant levels (MCLs) for PFOA (4 ng/L) announced in March 2023,82 and two additional surface water samples (25%) had PFOS concentrations above the proposed MCLs for PFOS (4 ng/L). Detection limits for PFOA (0.30 ng/L) and PFOS (0.13 ng/L) in this study were above interim lifetime health advisory (LHA) levels released by the EPA8 in June 2022, and thus, all detections and possibly some nondetect samples are above LHAs as well. All surface water samples and 41 (93%) well water samples had detectable levels of PFOA, and PFOS was detectable in all surface water samples and 24 (53%) well water samples. PFBS concentrations in well water ranged from nondetect to 12.53 ng/L, with no samples exceeding the final EPA LHA (2,000 ng/L) for PFBS.

Efforts to test PFAS concentrations in finished, public drinking water throughout West Virginia are ongoing, but of the 17 public water supply systems within the study area, four have conducted testing for PFOA, PFOS, HFDO–DA, PFBS, PFNA, and PFHxS.83 Of the four, only one public water supply well had concentrations of PFOA in finished water above the detection limit at 3.57 ng/L.83 Concentrations of PFOA collected from private water wells in this study ranged from nondetect to 22.48 ng/L, suggesting that potential exposures to PFOA may be slightly higher in some private drinking water sources than finished water from public systems. Likewise, PFBS concentrations measured in two public water systems (5.32 and 3.20 ng/L)83 were lower than the maximum concentration identified in groundwater in this study (12.53 ng/L). PFOS concentrations (8.56 and 7.87 ng/L)83 in the same water systems were higher than any concentrations observed in private water supplies in this study, indicating that exposure risk from specific PFAS may differ between finished, public water, and private supplies and that further monitoring of private water supplies should be undertaken to fully understand differences in exposure.

Nontargeted Results and Investigation of Emerging Compounds

In addition to identifying several compounds that overlapped with the targeted analysis, we identified 14 unique poly fluorinated homologous series (Table S9) that met selection criteria of at least three members, an increasing m/z vs retention time trend, and at least one exact mass match in the EPA CompTox70 database. The above selection criteria provide confidence that the identified molecules are very likely to be PFAS, though other true identities are still possible. The predicted structures are considered tentative based on annotation scores (<B) generated by Fluoromatch52 and may differ slightly from those presented. Available patents for tentative structures include those for herbicides and pesticides,84,85 fluorosurfactants,86 and pharmaceutical uses87−90 among others. Although this curated annotation list is not exhaustive and identified compounds may reflect some extent of functional group degradation following sampling, the nontargeted analysis results provide a more comprehensive picture of public exposures and help identify and track emerging compounds of concern.13,91,92

Nontargeted detections were more frequent in surface water than water well samples, similar to detections of targeted PFAS (Figure S8). To estimate the abundance of each nontargeted PFAS in the surface and groundwater samples, we used previously published methods by Jacob et al.72 to calculate semiquantitative concentration estimates for nontargeted compounds. While these estimation methods72 were developed to reduce the inherent uncertainty of quantitative nontargeted results, we acknowledge that true concentrations may differ from these estimates by an order of magnitude or more.71,73

Estimated nontargeted PFAS concentrations were generally low (<5 ng/L) to negligible (nondetect) in both surface water and groundwater, with the notable exception of four nontargeted PFAS. Concentrations of 613_A, 702_A, 732_A, and 742_A exceeded 100 ng/L in individual well water and surface water samples (Tables S10 and S11) and the highest estimated concentrations of 742_A in groundwater reached upward of 800 ng/L. Concentrations of this magnitude are 30 to 300 times greater than even the highest concentrations of targeted compounds measured in this study, emphasizing the importance of nontargeted analysis in routine monitoring efforts for accurate estimates of public exposures to PFAS.

Evaluation of Variable Significance Using a Multivariate Latent-Variable Regression Model

To elucidate geochemical and anthropogenic factors influencing concentrations of the 21 targeted PFAS in private water wells, we applied a multivariate latent-variable regression model with spatial random effects. Because most compounds identified by the nontargeted analysis were not done so with high confidence and only semiquantitative peak area data are available, only targeted results were included in subsequent modeling analyses. Posterior means are presented as the parameter estimates, and uncertainty was quantified using 90% (*) and 95% (**) quantile-based credible interval levels (CRLs). CRLs that excluded one suggest that the corresponding parameter is statistically significant.

Statistically significant associations (Table S7) were observed between PFAS concentrations in well waters and the following predictors: major ion chemistry (Ca vs Na dominant), topographic position (upslope vs valley), PFAS size (long vs short-chain), idups to NPDES permit, idups to UOG wells, and the density of COG (Table 1). These associations suggest that the occurrence of PFAS within water wells tested in this study reflect a combination of hydrologic controls, physical characteristics of PFAS, and water well proximity to industrial activities, including oil and gas operations. The parameter estimates presented in Table 1 can be interpreted as the percent change (when multiplied by 100) in targeted PFAS concentrations with one standard deviation change in continuous predictors or in relation to the reference group for categorical predictors. For example, as the number of COG wells within 2 km of a private water well increases by one standard deviation (10 COG wells, Table S7), the observed targeted PFAS concentrations increase by ∼20% and PFAS concentrations in Ca–Na–HCO3 and Na–HCO3 type waters are ∼32 and ∼27% lower than those in Ca–HCO3 type waters. The direction of multivariate, latent-variable model results agree with results from the initial bivariate correlation analysis using the sum of all targeted PFAS (assuming values below the LQ are equal to zero), although with a more robust handling of censored values and correlation.

Table 1. Exponentiated Regression Parameter Estimates for Significant Predictors (90a and 95%b)c.

predictor variable estimate standard error significance
idups NPDES permit 1.13 0.10 a
idups Active UOG well 1.11 0.03 b
count COG wells within 2 km 1.20 0.07 b
size PFAS (long vs short-chain) 0.64 0.07 b
water type (Ca–Na–HCO3 vs Ca–HCO3) 0.68 0.18 b
water type (Na–HCO3 vs Ca–HCO3) 0.73 0.12 b
topographic position index (valley vs upland) 0.62 0.14 b
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a

Significant at the 90% Bayesian CRL.

b

Significant at the 95% Bayesian CRL.

c

Parameter estimates can be interpreted as the percent change (when multiplied by 100) in targeted PFAS concentrations with one standard deviation change in the continuous predictors (Table S7). Categorical predictors are interpreted with respect to the reference group (i.e., “short”, “Ca–HCO3”, “Upland”). The standard error is given as the posterior standard deviation.

Importance of Compound Size on Mobility in the Environment

Latent-variable regression results suggest the importance of carbon-chain length and compound size on the concentrations observed in private water wells. This relationship may partly reflect a greater abundance of short-chain PFAS sources following the global phase out of many long-chain PFAS over the past decade.93−95 Nevertheless, other factors may be at play. In particular, the presence of the long-chain PFNA and PFDA in regional precipitation (∼4 ng/L) coupled with the absence of these compounds in well water (Figure 2; Table S8) suggests that long-chain PFAS are being retained or transformed to shorter-chain compounds in the near-surface environment.

Studies have shown that long-chain PFAS molecules adsorb more readily to soils and aquifer materials than short-chain PFAS.96−100 Adsorption is particularly strong in the presence of high organic content materials, such as the interbedded shales and coal seams common in the Permian- and Pennsylvanian-age aquifer systems of the study region.55−57,96−99 All the PFCAs with carbon-chains of 9 or greater in this study have laboratory-derived organic carbon-normalized partitioning coefficients (log Koc) greater than 2 L/kg,101−103 which suggests their mobility within organic-rich aquifer materials would be limited. Partial degradation of long-chained PFAS under anaerobic or aerobic conditions is possible,104 though generally considered slow in comparison to adsorption.105 Likewise, estimates of PFAS uptake by plants are an order of magnitude less than that observed through soil sorption105,106 and have been found to be more effective for short-chain PFAS than their long- chain counterparts. Therefore, adsorption likely plays a greater role than either biochemical degradation or plant uptake in limiting the occurrence of long-chained PFAS, such as PFNA and PFDA, in the private wells tested in this study.

Increased Occurrence of PFAS in Private Wells of Groundwater Recharge Zones

Of the 45 groundwater samples collected, 6 were located in upland regions, and 39 were situated in valley regions. In concurrence with the regression model results, increases in total PFAS concentrations were associated with upland regions (p-value = 0.01, Kruskal–Wallis) and Ca-dominated water types (p-value = 0.03, Kruskal–Wallis). Likewise, PFAS detections above the LOQ were more frequent in water wells of upland regions (p-value = 0.02, Chi-squared), where major ion chemistry was dominated by Ca–HCO3 or a mixed Ca–Na–HCO3 groundwater (p-value = 0.04, Chi-squared). Topography influences groundwater flow patterns within the northern Appalachian Basin,53,107−109 and upland areas dominated by Ca–HCO3 groundwater types correspond with recharge zones, where comparatively young groundwater is moving downward, away from the water table. Water wells located within these recharge zones have been shown to be more susceptible to contamination from surface activities, including agricultural runoff and road salt application,53 and they may be equally vulnerable to contamination by PFAS derived from atmospheric deposition or surface releases (e.g., spills) of PFAS-containing wastes.

That PFAS concentrations tend to be greater in upland wells likely reflects the fact that these wells tap upgradient portions of groundwater flow paths that are proximal to PFAS surface sources. The short flow paths to the upland wells limit the amount that adsorption and hydrodynamic dispersion reduce PFAS concentrations. In contrast, water wells in valley locations primarily capture groundwater near the termini of long flow paths. This groundwater has interacted more extensively with aquifer solids, thereby promoting greater PFAS adsorption and PFAS dilution by hydrodynamic dispersion. Our findings are consistent with those of McMahon et al., who reported that groundwater age (as estimated by tritium) is a good predictor of PFAS occurrence in regional aquifers along the east coast, with younger (i.e., recently recharged) groundwater exhibiting higher rates of PFAS detection.3

Potential PFAS Sources Affecting Private Groundwater Sources

Atmospheric Deposition and Subsequent Groundwater Recharge

Six private wells in this study were located in heavily forested regions (>90% forested land), more than a kilometer from any known point sources, biosolid applications, or urban development with extensive impervious surfaces. Two of these six wells were located in upland settings and four in valley regions. Targeted PFAS concentrations measured in the upland wells were 5.18 and 11.27 ng/L, respectively, while no targeted detections were made in the four valley wells, highlighting the role of hydrologic setting (i.e., recharge areas) in PFAS occurrence. That PFAS was present in water wells so far removed from known PFAS sources implicates atmospheric wet deposition and recharge as a possible source of PFAS to private water wells. However, rather than being restricted to remote locations, atmospheric deposition could reasonably be expected to contribute PFAS to groundwater across our study area.

To determine if atmospheric deposition could feasibly account for PFAS concentrations observed in private water supplies, we compare concentrations of individual PFAS to those measured in regional precipitation from locations with no known PFAS emission sources.110 Concentrations of PFAS in 93% (n = 42) of our well water samples fall within the range of expected values for regional precipitation in rural areas110 (Figure 4). These findings suggest that, based on magnitude alone, wet deposition and subsequent recharge could account for a major portion of PFAS observed in private well waters in our study area. While additional PFAS sources are contributing to private water supplies in the region (see below), these results suggest that wet deposition may serve as a regionally significant, yet overlooked, source of PFAS.

Figure 4.

Figure 4

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Comparison of targeted PFAS concentrations (dark gray diamonds, green stars) and ranges reported for regional precipitation in rural areas of Ohio without purported atmospheric point sources of PFAS from Pike et al.110 (light gray bars) (A). The majority of samples fall within expected values from regional wet deposition, with three notable exceptions (green stars) for PFOA (HC1 and HC2) and PFBS (HC3). Targeted PFAS compounds considered in this study without available measurements in regional precipitation (perfluoropentanoic acid (PFPeA), perfluoroheptanoic acid (PFHpA), perfluoroundecanoic acid (PFUnA), perfluorododecanoic acid (PFDoA), perfluoropentanesulfonic acid (PFPeS), perfluoroheptanesulfonic acid (PFHpS), perfluoro-4-methoxybutanoic acid (PFMBA), perfluoro(2-ethoxyethane)sulfonic acid (PFEESA), nonafluoro-3,6-dioxaheptanoic acid (NFDHA), perfluoro-3-methoxypropanoic acid (PFMPA), 4,8-dioxa-3H-perfluorononanoic acid (NaDONA), 9-chlorohexadecafluoro-3-oxanonane-1-sulfonic acid (9Cl-PF3ONS), and 11-chloroeicosafluoro-3-oxaundecane-1-sulfonic acid (11Cl-PF3ONS)) were excluded from this comparison. PFBS and PFHxS were not detected above baseline values in regional wet depositional sources110 or from historic stack emissions at the Chemours Washington Works facility,10 and thus, our detections are likely from alternate sources. Topographic relief maps (right) detailing potential point-source activities surrounding the three high-concentration samples (HC1, HC2, and HC3) in this study are presented to the right (B, C, D). Dotted lines outline areas topographically upgradient of each private well.

Historic Stack Emissions and North–South Trends in PFOA Concentrations

Higher concentrations of PFOA were observed in private wells in the southern portion of the study region proximal to historic PFOA stack emissions and discharges to streams at the Chemours Washington Works facility in Parkersburg, West Virginia (Figures S5 and S9). While results from the latent-variable modeling efforts in this study did not identify proximity to the Chemours facility as a significant predictor of PFAS concentrations, when considered individually, increases in PFOA detections are associated with distance to the facility (p-value = 0.04, Kruskal–Wallis). These findings suggest possible influences from the Chemours facility in the southern portion of the study area that primarily impact PFOA concentrations.

To evaluate if historic releases from the Chemours facility could account for the PFOA concentrations in groundwater, we extrapolated log–linear trends in PFOA concentrations developed from surface water samples collected by Galloway et al.10 This extrapolation suggests that PFOA derived from the Chemours facility would occur in groundwater at concentrations between 0.001 and 1 ng/L (Figure S10), and it is reasonable to expect that this range could actually be lower given that generally PFAS concentrations in surface water measured here and by Galloway et al.10 exceeded those in groundwater.

Seventeen (37%) of the well water samples tested in this study contained PFOA at concentrations exceeding the estimated upper threshold attributable to the Chemours facility (Figure 4). Thus, while legacy stack emissions at the Chemours Washington Works facility may have contributed PFOA to groundwaters analyzed in this study, other sources clearly play an equal or greater role in PFOA contamination, particularly in well waters with the highest PFOA concentrations (i.e., >10 ng/L).

Proximity to Oil and Gas Activity

Multivariate latent-variable regression model results identified idups to active UOG well pads and density of COG activity within 2 km as significant predictors of targeted PFAS concentrations in private water wells in the region, suggesting that, in addition to probable atmospheric sources of PFAS, proximity to oil and gas related activities may contribute PFAS to private water wells, though at low levels the effects of industrial sources may be difficult to distinguish from atmospheric contributions.

Three well water samples (HC1, HC2, HC3) exhibited concentrations of targeted PFAS (PFOA and PFBS) that could not be explained by regional wet deposition110 alone, even when supplemented by historic deposition from the Washington Works facility10 (Figure 4; Supplementary Discussion 2). The high individual concentrations observed in HC1, HC2, and HC3 and the distance between the three samples (>10 km; Figure S11) implicate the action of three distinct point sources in contrast to a diffuse regional source. All three sites fall within 1 km of UOG well pads with recent drilling operations (2019–2020) and multiple active or abandoned COG wells (Figure 4B–D). Industrial operations related to oil and gas development were the only identifiable sources within 2 km of HC1, HC2, or HC3, even considering agricultural activity and possible biosolids applications. Within the larger data set, PFOA detections and concentrations in private water wells were significantly associated with proximity to recently drilled (2018–2020) UOG well pads (p-value = 0.04, Kruskal–Wallis; p-value = 0.01, Spearman’s Rank). However, with the exception of samples HC1 and HC2, observed concentrations were indistinguishable from levels plausibly attributable to atmospheric sources. Statistical relationships between PFBS and specific PFAS sources in the larger data set could not be identified because of the degree of censoring present for PFBS.

Findings for PFOA corroborate similar associations between PFOA and the density of oil and gas operations that were observed by Breitmeyer et al. in Pennsylvania surface waters. While not the largest contributor of PFAS to surface water identified in Pennsylvania, the authors provide some evidence of potential impacts from upstream oil and gas activities, possibly delivered to streams through discharges from combined sewer overflows.25 None of the groundwater sample locations in this study are located within 5 km of a documented combined sewer overflow111 that could serve as a conveyance mechanism to surface water and only four are located in proximity (2 km) to a known and documented wastewater treatment facility suggesting that, if oil and gas operations are a viable source of PFAS to groundwater wells in this study, another pathway must be at play. Other pathways of contamination (i.e., spills or accidental releases of low-salinity PFAS-containing fluids, atmospheric releases and dry or wet deposition, on-site herbicide applications, or emergency AFFF discharges) remain plausible, though could not be confirmed or rejected in light of the limited evidence available. Additional sampling efforts in regions of dense oil and gas development and further work to identify possible mechanisms of contamination to private groundwater sources are necessary to provide additional clarity on the likelihood of the impact of PFAS use in the oil and gas industry on private water sources.

Industrial Point Sources

The latent-variable model results identified idups to industrial NPDES permits as another significant predictor of PFAS concentrations in private wells. Significant associations were observed between the inverse distance to upstream industrial NPDES source and the number of targeted PFAS detections made in private water wells (p-value = 0.04, Kruskal–Wallis). While the dominant groundwater flow regimes underneath the gaining reach of streams make it unlikely that NPDES discharges have directly impacted groundwater supplies, discharges to losing streams may pose a risk to groundwater sources. Local atmospheric emission and deposition or direct groundwater contamination from site activities are also possible and may contribute to PFAS to nearby water wells. No individual-level associations with specific PFAS were able to be identified, indicating that while proximity to NPDES sources may be an important predictor of PFAS in private water wells, PFAS mixtures from these locations may be more variable.

Key Findings

This study investigates the distribution of PFAS in both private well water and surface water of northern West Virginia. We find that concentrations of targeted and nontargeted PFAS are higher in surface water than in groundwater and observed the highest PFAS concentrations in a remote tributary with a lower industrial footprint than locales along the Ohio River. These findings are contrary to initial observations made in the state-wide study of PFAS in West Virginia,41 which found PFAS concentrations were generally higher in groundwater than surface water sources and at sites along the western boundary of the state near the Ohio River, emphasizing the importance of high-resolution sampling efforts from a variety of sources and locales.

Although measured concentrations of PFAS are lower overall in groundwater supplies, our results reveal that 8% of wells exceed proposed MCLs for PFOA and, in some instances, well water concentrations of individual PFAS (PFOA and PFBS) surpass those found in nearby surface waters and finished drinking water from public supplies, emphasizing the need for expanded groundwater monitoring efforts, particularly in regions reliant on private groundwater sources. Individual semiquantitative concentrations from nontargeted analysis may exceed maximum targeted concentrations by two to 3 orders of magnitude, highlighting the need for inclusion of nontargeted screening in monitoring programs.

Model results reveal that well setting is an important predictor of PFAS concentrations, with private wells located in upland recharge zones being more vulnerable to PFAS contamination, an observation that has implications for other major aquifers in the US where topographically driven flows dominate. Wet deposition is likely a frequent contributor to private wells situated in recharge zones and expanded monitoring efforts should include targeted and nontargeted PFAS concentrations in precipitation as a crucial step toward understanding risks to private wells nationally. PFAS size is also a significant predictor of PFAS concentrations, with larger molecules exhibiting apparently lower mobility in the organic-rich sediments of the study region, as supported by other investigations into PFAS mobility in groundwater.3,112

In addition to atmospheric sources, historic releases of PFOA at the Chemours Washington Works facility may have impacted private wells in the southern portion of the study region, though projected contributions are estimated to be low (<1 ng/L). Nevertheless, the extent of soil contamination from historic deposition and related impacts on groundwater sources remains unclear. Future work should expand monitoring of soil and groundwater in the region surrounding the Parkersburg facility to determine the full extent of historic contamination and the impact of continued emissions of shorter-chain replacement compounds, like GenX, on regional groundwater supplies.

Significant associations between PFOA and oil and gas operations were observed, and three private wells exhibited individual PFAS concentrations above those likely from atmospheric sources even when considering historic deposition from the nearby Chemours Washington Works facility. UOG and COG operations are identified as plausible nonhousehold-specific point sources in proximity to these three high-concentration samples. Our findings provide support for further studies emphasizing source attribution efforts and targeted sampling campaigns to provide additional clarity on the potential sources of PFAS to private drinking water sources, particularly in regions where reliance on private water wells co-occurs with spatially distributed industrial sources, like oil and gas development.

Acknowledgments

This research was supported by a National Priority Research Project under the U.S. Environmental Protection Agency EPA Grant Assistant Agreement No. CR839249. H.G.S. received additional support from a National Science Foundation Graduate Research Fellowship under Grant No. (NSF DGE-1122492). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the U.S. Environmental Protection Agency. The authors would like to acknowledge the expertise and analytical equipment provided by those at the Yale Analytical and Stable Isotope Center (YASIC), those at the Center for Perinatal, Pediatric and Environmental Epidemiology (Yale CPPEE), the Connecticut Agricultural Experiment Station (CAES), and the Cary Institute, without whom this work would not have been possible. We are also grateful to the efforts of three referees who made constructive recommendations for revisions that led to improvement in this manuscript.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.3c05192.

  • Target analytes (Table S1); internal standard concentrations (Table S2); calibration standard levels (Table S3); LCMS operating parameters (Table S4); standard recovery (Table S5); geospatial data sources (Table S6); selected model predictors (Table S7); targeted results (Table S8); nontargeted results (Table S9); surrogate matches (Table S10); and summary nontarget results (Table S11) (XLSX)

  • Land use classification for study counties (Figure S1); locations of all active UOG wells (Figure S2); locations of potential AFFF use and storage and waste management facilities (Figure S3); spatial distribution of the 334 industrial NPDES discharge permits (Figure S4); PFOA concentrations observed in soil and water samples collected near the Chemours Washington Works facility (Figure S5); sample collection and targeted analytical methods (Supplementary Methods 1); nontargeted workflow (Supplementary Methods 2); multivariate latent variable model formulation (Supplementary Methods 3); targeted PFAS concentrations measured above the LOQ (Figure S6); common mixtures of PFAS (Supplementary Discussion 1); comparison of total PFAS concentrations by topographic position index (TPI) (Figure S7); comparison of the number of nontargeted PFAS detected by sample type (Figure S8); spatial distribution of measured PFOA concentrations in groundwater and surface water (Figure S9); linear regression for PFOA concentrations in surface water vs Euclidean straight-line distance from the Chemours Washington Works facility (Figure S10); high-concentration groundwater samples (Supplementary Discussion 2); and locations of high-concentration groundwater samples HC1, HC2, and HC3 (Figure S11) (PDF)

The authors declare no competing financial interest.

Supplementary Material

es3c05192_si_001.xlsx (2.9MB, xlsx)
es3c05192_si_002.pdf (2.2MB, pdf)

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