Geology topics

Paul E. Stackelberg

Publications and source records attributed to Paul E. Stackelberg.

At least 19 recordsLinked to original sources

USGS2025

Groundwater salinity: Applying the specific conductance and water type proxy

Groundwater is increasingly needed for water supplies but may have limited utility in some locations because of its salinity. Salinity, often expressed as total dissolved solid (TDS), is frequently estimated using specific conductance (SC) measurements. However, the commonly used proxy (0.65 multiplied by SC to indicate TDS, common in many handheld meters) can result in inaccurate TDS estimates. First, the TDS–SC relationship is not linear over the entire concentration range of groundwater. Furthermore, the TDS (and salinity)–SC relationships vary substantially depending on the major-ion composition. Here we develop a proxy method utilizing SC and major-ion water type to estimate TDS and salinity specifically for groundwaters. Compared to most surface waters, groundwater tends to have a wider range of salinity (fresh to highly saline) and higher concentrations of bedrock-derived solutes such as carbonate ions, silica, and many other ions. The dataset used to develop the proxies includes water chemistry data from 149,059 discrete groundwater samples. The groundwater proxies, which employ nonlinear log–log relations, utilize five water types (HCO 3 , Cl, Ca-Mg-SO 4 , Na-K-SO 4 , and mixed waters), are accurate (median percent difference between TDS and salinity determined using the proxy compared to discrete measurements was <±0.8%) over a wide range of SC (up to 200 mS/cm), rapid, cost-effective, and can be measured on-site.

Groundwater↗

USGS2025

Gaps in water quality modeling of hydrologic systems

This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations of management alternatives to support ecosystem health and human beneficial use of water resources. It covers surface water and groundwater quality modeling, dealing with a broad suite of physical, biogeochemical, and anthropogenic drivers. Modeling capabilities for six constituents (or constituent categories) are explored: water temperature, salinity, nutrients, sediment, geogenic constituents, and contaminants of emerging concern. Each constituent was followed through the coupled atmospheric-hydrologic-human system, with prominent modeling gaps described for a diverse array of relevant inputs, processes, and human activities. Commonly identified modeling gaps primarily fall under three types: (1) model gaps, (2) data gaps, and (3) process understanding gaps. In addition to potential solutions for addressing specific individual modeling limitations, some broad approaches (e.g., enhanced data collection and compilation, machine learning, reduced-complexity modeling) are discussed as ways forward for tackling multiple gaps. This gap analysis establishes a framework of diverse approaches that may support improved process representation, scale, and accuracy of models for a wide range of water quality issues.

Water↗

USGS2024

A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA

Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R 2 ) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth to bedrock (DTB) in the Delaware River Basin. Geostatistical analysis shows that approximately one third of the DTB variance is at spatial scale smaller than 2 km. Hence, we interpret our point-scale R 2 of 0.3 (testing data) to be sufficient for regional-scale modelling. Bias-correction methods improve performance at two of the three MPE scales: point-scale change is negligible, while distributional and geostatistical performance improves. In contrast, bias correction applied to a global DTB model does not improve MPE performance. This work encourages scale-appropriate performance evaluations to enable effective model intercomparison.

Delaware, New Jersey, New York, Pennsylvania↗

USGS2024

Predictions of groundwater PFAS occurrence at drinking water supply depths in the United States

Per- and polyfluoroalkyl substances (PFAS), known colloquially as “forever chemicals”, have been associated with adverse human health effects and have contaminated drinking water supplies across the United States owing to their long-term and widespread use. People in the United States may unknowingly be drinking water that contains PFAS because of a lack of systematic analysis, particularly in domestic water supplies. We present an extreme gradient boosting model for predicting the occurrence of PFAS in groundwater at the depths of drinking water supply for the conterminous United States. Our model results indicate that 71 to 95 million people in the conterminous United States potentially rely on groundwater with detectable concentrations of PFAS for their drinking-water supplies prior to any treatment.

conterminous United States↗

USGS2023

Gross alpha-particle activity and high 226Ra concentrations do not correspond with high 210Po in the Atlantic and Gulf Coastal Plain aquifers of the United States

210 Po, which is of human-health concern based on lifetime ingestion cancer risk, is indirectly regulated in drinking water through the U.S. Environmental Protection Agency’s gross alpha-particle activity (GAPA) maximum contaminant level of 15 pCi/L (picocuries per liter). This regulation requires independent measurement of 226 Ra for samples exceeding the GAPA screening level of 5 pCi/L. There is no such requirement for 210 Po. Co-occurrence of 226 Ra and 210 Po, alpha-emitting 238 U-decay-series progeny, might be helpful in locating high- 210 Po waters but is unverified. Relations among 210 Po, 226 Ra, and GAPA evaluated for samples from 257 public-supply wells from Coastal Plain aquifers showed that concentrations of 226 Ra correlated with GAPA but neither correlated with 210 Po concentrations. The highest concentrations of 226 Ra and 210 Po were found under differing geochemical conditions. The highest 226 Ra occurred in low-pH oxidizing waters and in neutral-pH reducing waters, where geochemical conditions render Fe–Mn-hydroxide sorbents inefficient. 210 Po was highest (10.1 pCi/L) in reducing waters with high pH (>7.5, which results from progressive cation exchange), where 226 Ra was lowest─exchanged to clay minerals. Because 226 Ra and 210 Po did not co-occur, the GAPA screening might not be protective for 210 Po. Independent 210 Po analysis is prudent, especially where groundwater is reducing with high pH and low 226 Ra concentrations.

Environmental Science & Technology Water↗

USGS2023

Salinity and total dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type

The total concentration of dissolved constituents in water is routinely quantified by measurements of salinity or total dissolved solids (TDS). However, salinity and TDS are operationally defined by their analytical methods and are not equivalent for most waters. Furthermore, multiple methods are available to determine salinity and TDS, and these methods have inherent differences. TDS is defined as the mass of anhydrous residue remaining in a sample vessel after evaporation and subsequent oven drying at a defined temperature. Salinity is a measure of the mass of dissolved salts in a given mass of solution. In addition, there are approaches that quantify the total solute (TS) concentration, including gases. The purpose of this study is to develop a proxy method using specific conductance and major-ion water type to reliably predict salinity, TDS, and/or TS. Thus, we compared several methods to calculate salinity, TDS, and TS for 6391 surface water samples and conclude the following: TDS measurements are best suited for studies of anhydrous residue (e.g., evaporites); salinity determined by summing the speciated ion concentrations, termed S ∑spec , is the most comprehensive method to represent the concentration of dissolved constituents in natural waters; and TS determinations are useful if dissolved CO 2 and other gases are of interest. Thus, we utilized S ∑spec to compare differences between the various salinity, TDS, and TS methods, and to develop a new proxy method to predict salinity based on specific conductance (SC) and major ion water type, termed S SC_WT . For the surface waters used in this study, the median difference between TDS and S ∑Spec was between −19% and −24%, depending on the method. The median difference between S SC_WT and S ∑Spec was −2.4% for the samples in this study. The S SC_WT approach is cost effective, rapid, and capable of providing reliable real-time salinity determinations at surface water sites where SC data are available and water type is known.

Applied Geochemistry↗

USGS2022

Quality of groundwater used for public supply in the continental United States: A comprehensive assessment

The presence of contaminants in a source water can constrain its suitability for drinking. The quality of groundwater used for public supply was assessed in 25 principal aquifers (PAs) that account for 84% of groundwater pumped for public supply in the U.S. (89.6 million people on a proportional basis). Each PA was sampled across its lateral extent using an equal-area grid, typically with 60 wells per PA. Samples were analyzed for 502 constituents, of which 374 had either a regulatory or nonregulatory human health benchmark (HHB). Nationally, elevated concentrations (relative to HHBs) of geogenic constituents have a larger effect than anthropogenic constituents, as indicated by three metrics: detection frequency, 35% versus 8.1%; prevalence (based on area), 41% versus 6.4%; and population potentially affected, 31.2 million versus 7.1 million. Prevalence of any constituent at elevated concentrations was high─40 to 75%─in PAs comprising unconsolidated sediment (eight PAs) and sandstone or interbedded sandstones and carbonates (four PAs) in the West and Central Interior. Prevalence was lower─15 to 35%─in PAs comprising sediment and sedimentary rocks along the Gulf and Atlantic Coasts (four PAs), carbonates distributed across the continental U.S. (seven PAs), and hard rock (two PAs).

continental United States↗

USGS2022

Mapped predictions of manganese and arsenic in an alluvial aquifer using boosted regression trees

Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.

Arkansas, Louisiana, Mississippi, Tennessee↗

USGS2022

Predicting regional fluoride concentrations at public and domestic supply depths in basin-fill aquifers of the western United States using a random forest model

A random forest regression (RFR) model was applied to over 12,000 wells with measured fluoride (F) concentrations in untreated groundwater to predict F concentrations at depths used for domestic and public supply in basin-fill aquifers of the western United States. The model relied on twenty-two regional-scale environmental and surficial predictor variables selected to represent factors known to control F concentrations in groundwater. The testing model fit R 2 and RMSE were 0.52 and 0.78 mg/L. Comparisons of measured to predicted proportions of four F-concentrations categories (<0.7 mg/L, 0.7–2 mg/L, >2 mg/L – 4 mg/L, and > 4 mg/L) indicate that the model performed well at making regional-scale predictions. Differences between measured and predicted proportions indicate underprediction of measured F at values by between 4 and 20 mg/L, representing less than 1% of the regional scale predicted values. These residuals most often map to geographic regions where local-scale processes including evaporative discharge in closed basins or intermittent streams concentrate fluoride in shallow groundwater. Despite this, the RFR model provides spatially continuous F predictions across the basin-fill aquifers where discrete samples are missing. Further, the predictions capture documented areas that exceed the F maximum contaminant level for drinking water of 4 mg/L and areas that are below the oral-health benchmark of 0.7 mg/L. These predictions can be used to estimate fluoride concentrations in unmonitored areas and to aid in identifying geographic areas that may require further investigation at localized scales.

Arizona, California, Colorado, New Mexico, Nevada,↗

USGS2021

Relation between road-salt application and increasing radium concentrations in a low-pH aquifer, southern New Jersey

The Kirkwood–Cohansey aquifer in southern New Jersey is an important source of drinking-water supplies, but the availability of the resource is limited in some areas by high concentrations of radium, a potential carcinogen at elevated concentrations. Radium ( 226 Ra plus 228 Ra) concentrations from a network of 25 drinking-water wells showed a statistically significant increase over a decadal time scale ( p < 0.05), with a median increase of 0.35 picocuries per liter. Increases in Ra are correlated with road-salt application rates, and we hypothesize that the correlation is causal. Geochemical processes associated with road-salt applications that can mobilize Ra into solution include competition by excess sodium for sorption sites and formation of chloride complexes (RaCl + and RaCl 2 ). The largest increases in Ra were in groundwater with low pH (≤5), which is an indirect surrogate for low cation-sorption capacity. Correlations with other potential anthropogenic causes for the increase in Ra were not observed, further suggesting a road-salt effect. Given the significant increase in Ra concentrations in this drinking-water source, the known carcinogenic risks from Ra, the direct link to road-salt application, and the likelihood for continued increases, additional monitoring is necessary in areas with similar hydrogeologic and geochemical settings.

New Jersey↗

USGS2021

Depth of groundwater used for drinking-water supplies in the United States

Groundwater supplies 35 percent of drinking water in the United States. Mapping the quantity and quality of groundwater at the depths used for potable supplies requires an understanding of locational variation in the characteristics of drinking-water wells (depth and open interval). Typical depths of domestic- and public-drinking-water supply wells vary by and within aquifer across the United States. The depths to the top and bottom of the zones from which drinking water is withdrawn are important predictor variables in regional- and national-scale statistical water models, but spatially extensive maps of the depths to drinking-water-supply sources are not consistently available in modeled regions. Therefore, it was necessary to generate a set of grids representing surfaces of the approximate common depth and length of open intervals in the wells from which water is withdrawn for domestic- and public-drinking-water supply (withdrawal zones) within the conterminous United States. Well data (about 7.6 million records) were compiled from several sources, including the U.S. Geological Survey’s National Water Information System (600,922 records), the U.S. Environmental Protection Agency’s Safe Drinking Water Information System dataset (66,540 records, primarily public-supply wells), a groundwater ambient monitoring dataset (31,448 records, primarily domestic-supply wells), individual State data (6,096,503 records), a national brackish aquifer study (96,885 records), and a glacial aquifer study (729,564 records). Fifty-seven principal aquifers and 65 secondary hydrogeologic regions have been designated in the conterminous United States. The principal aquifers and secondary hydrogeologic regions vary in depth, thickness, lithology, and transmissivity characteristics. Some principal aquifers underlie secondary hydrogeologic regions, and may in turn be overlain by glacial sediment or basin and valley fill aquifers, which may also be used as drinking-water sources. The principal aquifer and secondary hydrogeologic region polygons were merged with overlying sediment polygons, where present, including glacial sediment, coarse glacial sediment, and stream valley alluvium (alluvium) polygons, to generate unique hydrogeologic settings across the conterminous United States. A total of 288 distinct hydrogeologic settings resulted from the merging of principal aquifer, secondary hydrogeologic region, glacial sediment, coarse glacial sediment, and alluvium polygons. Each well was assigned to a hydrogeologic setting on the basis of location. Hydrogeologic setting well groupings were used to guide calculations of the median value for well depth and depth to and length of open intervals across the hydrogeologic setting. Where well data were sparse or missing, wells from hydrogeologic settings with similar well construction properties, geology, physiography, and topography were grouped and used to calculate the moving median depth (if less than five wells in a 100-kilometer [62.1-mile] radius) and to estimate open interval length (if not available within hydrogeologic setting). Grids were generated to represent what might be considered as the “typical” or “median” domestic- and public-supply well in an area. The well properties are defined with moving median grids of top depth, bottom depth, and length of open interval at a 1-square-kilometer (0.38-square-mile) grid cell scale. Median depths and open intervals of domestic- and public-supply wells varied by lithology of the hydrogeologic setting and overlying sediment. Overall, the median depths were 142 feet (43.3 meters) for all domestic-supply wells and 202 feet (61.6 meters) for all public-supply wells. The median open intervals were 21 feet (6.4 meters) for domestic-supply wells and 49 feet (14.9 meters) for public-supply wells. The shallowest median bottom open interval depths for domestic-supply wells were in the secondary hydrogeologic regions with coarse glacial sediment, which suggests that the wells are most commonly completed in the permeable coarse glacial sediment and not in the underlying secondary hydrogeologic region. Public-supply wells were completed at relatively shallow median depths when drilled in permeable sediment that overlie secondary hydrogeologic regions. When public-supply wells were completed in principal aquifers, the median depths were typically greater than wells completed in secondary hydrogeologic regions. Well data used in this study were limited to those available from national or State digital databases. Several quality-assurance checks were performed during data compilation, but a comprehensive quality assurance inspection for each of the data sources was outside the scope of this study. Grids defining typical open intervals in domestic- and public-supply wells are presented. Although there are many places where multiple aquifers are stacked, these results correspond primarily to the aquifer with the highest documented number of wells for each use.

Scientific Investigations Report↗

USGS2021

Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States

Groundwater supplies 50% of drinking water worldwide and 30% in the United States. Geogenic and anthropogenic contaminants can, however, compromise water quality, thus limiting groundwater availability. Reduction/oxidation (redox) processes and redox conditions affect groundwater quality by influencing the mobility and transport of common geogenic and anthropogenic contaminants. In the glacial aquifer system, northern United States (GLAC, 1.87 million km 2 ), groundwater with high arsenic or manganese concentration is associated with reducing conditions and high nitrate with oxidizing conditions. This study uses machine learning to identify the relative influence of drivers of redox conditions (e.g., residence time vs. reactivity) across the glacial landscape. We developed three‐dimensional boosted regression tree models to predict redox conditions using the likelihood of low dissolved oxygen or high iron as indicators of anoxic conditions. Results indicate that variation in redox condition is controlled primarily by residence time (e.g., groundwater age and relative depth) and to a lesser extent by geochemical reactivity (e.g., subsurface contact time, soil carbon). Older water and deeper wells, along with more water storage or slower water movement was associated with higher probability of anoxic conditions. Mapped model results illustrate regions where anoxic redox conditions may mobilize geogenic contaminants or oxic conditions may limit denitrification potential. Results may also provide simplified redox input for process or predictive models of, for example, arsenic, manganese, or nitrate. Machine learning modeling methods can lead to improved understanding of contaminant occurrence and what drives redox conditions, and the methods may be transferable to other settings.

Northern Continental United States↗

USGS2021

Machine-learning predictions of high arsenic and high manganese at drinking water depths of the glacial aquifer system, northern continental United States

Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT models’ predictor variables (PVs) included recently developed three-dimensional estimates of a suite of groundwater age metrics, redox condition, and pH. We also demonstrated a successful approach to significantly improve ML prediction sensitivity for imbalanced data sets (small percentage of high values). We present predictions of the probability of high As and high Mn concentrations in groundwater, and uncertainty, at two nonuniform depth surfaces that represent moving median depths of GLAC domestic and public supply wells within the three-dimensional model domain. Predicted high likelihood of anoxic condition (high iron or low dissolved oxygen), predicted pH, relative well depth, several modeled groundwater age metrics, and hydrologic position were all PVs retained in both models; however, PV importance and influence differed between the models. High-As and high-Mn groundwater was predicted with high likelihood over large portions of the central part of the GLAC.

Environmental Science & Technology↗

USGS2021

Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression model

Ensemble-tree machine learning (ML) regression models can be prone to systematic bias: small values are overestimated and large values are underestimated. Additional bias can be introduced if the dependent variable is a transform of the original data. Six methods were evaluated for their ability to correct systematic and introduced bias. Method performance was evaluated using four case studies of groundwater quality: the units of the dependent variable were pH in two and log-concentration in the others. When performance metrics (bias and RMSE for both points and the CDF) were computed using the same units as those in the ML model, empirical distribution matching (EDM) provided the best results. When the metrics were computed using retransformed concentration, EDM and a method incorporating Duan's smearing estimate were both effective. A method based on the Z-score transform approximates EDM if the correlation coefficient between rank-ordered ML estimates and rank-ordered observations approaches one.

Environmental Modeling and Software↗

USGS2021

Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States

Groundwater is an important source of drinking water supplies in the conterminous United State (CONUS), and presence of high nitrate concentrations may limit usability of groundwater in some areas because of the potential negative health effects. Prediction of locations of high nitrate groundwater is needed to focus mitigation and relief efforts. A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate. Nitrate was predicted at a 1 km resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrations from 12,082 wells and included predictor variables representing well characteristics, hydrologic conditions, soil type, geology, land use, climate, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. The model provided accurate estimates at national and regional scales: the training (R 2 of 0.83) and hold-out (R 2 of 0.49) data fits compared favorably to previous studies. Predicted nitrate concentrations were less than 1 mg/L across most of the CONUS. Nationally, well depth, soil and climate characteristics, and the absence of developed land use were among the most influential explanatory factors. Only 1% of the area in either water supply zone had predicted nitrate concentrations greater than 10 mg/L; however, about 1.4 M people depend on groundwater for their drinking supplies in those areas. Predicted high concentrations of nitrate were most prevalent in the central CONUS. In areas of predicted high nitrate concentration, applied manure, farm fertilizer , and agricultural land use were influential predictor variables. This work represents the first application of XGB to a three-dimensional national-scale groundwater quality model and provides a significant milestone in the efforts to document nitrate in groundwater across the CONUS.

Science of the Total Environment↗

USGS2020

Machine learning predictions of pH in the Glacial Aquifer System, Northern USA

A boosted regression tree model was developed to predict pH conditions in three dimensions throughout the glacial aquifer system of the contiguous United States using pH measurements in samples from 18,386 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and, when coupled with long flowpaths, results in the most alkaline conditions. Conversely, in areas where glacial sediments are thin and carbonate‐poor, pH conditions remain acidic. At depths typical of drinking‐water supplies, predicted pH >7.5—which is associated with arsenic mobilization—occurs more frequently than predicted pH <6—which is associated with water corrosivity and the mobilization of other trace elements. A novel aspect of this model was the inclusion of numerically based estimates of groundwater flow characteristics (age and flowpath length) as predictor variables. The sensitivity of pH predictions to these variables was consistent with hydrologic understanding of groundwater flow systems and the geochemical evolution of groundwater quality. The model was not developed to provide precise estimates of pH at any given location. Rather, it can be used to more generally identify areas where contaminants may be mobilized into groundwater and where corrosivity issues may be of concern to prioritize areas for future groundwater monitoring.

Groundwater↗

USGS2020

Occurrence and geochemistry of lead-210 and polonium-210 radionuclides in public-drinking-water supplies from principal aquifers of the United States

On the basis of lifetime cancer risks, lead-210 ( 210 Pb) and polonium-210 ( 210 Po) ≥ 1.0 and 0.7 pCi/L (picocuries per liter), respectively, in drinking-water supplies may pose human-health concerns. 210 Pb and 210 Po were detected at concentrations greater than these thresholds at 3.7 and 1.5%, respectively, of filtered untreated groundwater samples from 1263 public-supply wells in 19 principal aquifers across the United States. Nationally, 72% of samples with radon-222 ( 222 Rn) concentrations > 4000 pCi/L had 210 Pb ≥ 1.0 pCi/L. 210 Pb is mobilized by alpha recoil associated with the decay of 222 Rn and short-lived progeny. 210 Pb concentrations ≥ 1.0 pCi/L occurred most frequently where acidic groundwaters inhibited 210 Pb readsorption (felsic-crystalline rocks) and where reducing alkaline conditions favored dissolution of iron–manganese- (Fe–Mn-) oxyhydroxides (which adsorb 210 Pb) and formation of lead–carbonate complexes (enhancing lead (Pb) mobility). 210 Po concentrations ≥ 0.7 pCi/L occurred almost exclusively in confined Coastal Plain aquifers where old (low percent-modern carbon-14) groundwaters were reducing, with high pH (>7.5) and high sodium/chloride (Na/Cl) ratios resulting from cation exchange. In high-pH environments, aqueous polonium (Po) is poorly sorbed, occurring as dihydrogen polonate (H 2 PoO 3 (aq)) or, under strongly reducing conditions, as a hydrogen-polonide anion (HPo – ). Fe–Mn- and sulfate-reduction and cation-exchange processes may mobilize polonium from mineral surfaces. Po 2+ occurrence in low-to-neutral-pH waters is attenuated by adsorption.

Environmental Science Technology↗

USGS2020

Environmental tracer evidence for connection between shallow and bedrock aquifers and high intrinsic susceptibility to contamination of the conterminous U.S. glacial aquifer

Covering a large portion of the northern conterminous United States (1.87 x 106 km2), the glacial aquifer serves as the primary water supply for 39 million public and domestic water users. Mean groundwater age, groundwater age distribution, and susceptibility to land surface contamination, using a new metric (Susceptibility Index; SI) based on the full age distribution and less prone to bias than estimated mean age, is reported for 168 public and domestic wells across the aquifer. Comparison of groundwater age metrics between well networks of varying spatial scale suggest an extensive sample network of equally spaced, long screened interval wells can be used to characterize aquifer wide groundwater age. Estimated mean age ranges from 1 to 50,000 years and, according to the composite age distribution, approximately 63 percent of all sampled water recharged after 1950 (i.e., modern) and 18 percent of the sampled water was recharged greater than 10,000 years ago. The later finding strongly suggests a connection between the glacial aquifer and underlying bedrock aquifers. Statistical analysis of glacial aquifer hydrogeology and age metrics show groundwater ages are young (less than few 100 years) and more susceptible to land surface contamination (larger SI) in unconfined and shallow portions of the aquifer. Old groundwater (greater than 1000 years) is more often associated with thicker sequences of fine grain sediments and/or shallow bedrock. Calculated SI is shown to be more strongly related to the number of land surface contaminants detected than mean age or fraction modern. Statistical analysis of SI and hydrogeology indicates SI is largely dictated by well depth and confinement. This study demonstrates how sample network design can be used to characterize groundwater age of large aquifers with a limited number of samples and how interpretation of environmental tracers can be used to improve conceptual models of groundwater aquifers and identify groundwater susceptible to contamination.

Glacial aquifer↗