Chemical data from 43 334 wells were used to examine the role of land surface−soil−aquifer connections in producing elevated manganese concentrations (>300 μg/L) in United States (U.S.) groundwater. Elevated concentrations of manganese and dissolved organic carbon (DOC) in groundwater are associated with shallow, anoxic water tables and soils enriched in organic carbon, suggesting soil-derived DOC supports manganese reduction and mobilization in shallow groundwater. Manganese and DOC concentrations are higher near rivers than farther from rivers, suggesting river-derived DOC also supports manganese mobilization. Anthropogenic nitrogen may also affect manganese concentrations in groundwater. In parts of the northeastern U.S. containing poorly buffered soils, ∼40% of the samples with elevated manganese concentrations have pH values < 6 and elevated concentrations of nitrate relative to samples with pH ≥ 6, suggesting acidic recharge produced by the oxidation of ammonium in fertilizer helps mobilize manganese. An estimated 2.6 million people potentially consume groundwater with elevated manganese concentrations, the highest densities of which occur near rivers and in areas with organic carbon rich soil. Results from this study indicate land surface−soil−aquifer connections play an important role in producing elevated manganese concentrations in groundwater used for human consumption.

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[1] The proportion of an aquifer with constituent concentrations above a specified threshold (high concentrations) is taken as a nondimensional measure of regional scale water quality. If computed on the basis of area, it can be referred to as the aquifer scale proportion. A spatially unbiased estimate of aquifer scale proportion and a confidence interval for that estimate are obtained through the use of equal area grids and the binomial distribution. Traditionally, the confidence interval for a binomial proportion is computed using either the standard interval or the exact interval. Research from the statistics literature has shown that the standard interval should not be used and that the exact interval is overly conservative. On the basis of coverage probability and interval width, the Jeffreys interval is preferred. If more than one sample per cell is available, cell declustering is used to estimate the aquifer scale proportion, and Kish's design effect may be useful for estimating an effective number of samples. The binomial distribution is also used to quantify the adequacy of a grid with a given number of cells for identifying a small target, defined as a constituent that is present at high concentrations in a small proportion of the aquifer. Case studies illustrate a consistency between approaches that use one well per grid cell and many wells per cell. The methods presented in this paper provide a quantitative basis for designing a sampling program and for utilizing existing data.

In 2019, 254 samples were collected from five aquifer systems to evaluate perfluoroalkyl and polyfluoroalkyl substance (PFAS) occurrence in groundwater used as a source of drinking water in the eastern United States. The samples were analyzed for 24 PFAS, major ions, nutrients, trace elements, dissolved organic carbon (DOC), volatile organic compounds (VOCs), pharmaceuticals, and tritium. Fourteen of the 24 PFAS were detected in groundwater, with 60 and 20% of public-supply and domestic wells, respectively, containing at least one PFAS detection. Concentrations of tritium, chloride, sulfate, DOC, and manganese + iron; percent urban land use within 500 m of the wells; and VOC and pharmaceutical detection frequencies were significantly higher in samples containing PFAS detections than in samples with no detections. Boosted regression tree models that consider 57 chemical and land-use variables show that tritium concentration, distance to the nearest fire-training area, percentage of urban land use, and DOC and VOC concentrations are the top five predictors of PFAS detections, consistent with the hydrologic position, geochemistry, and land use being important controls on PFAS occurrence in groundwater. Model results indicate that it may be possible to predict PFAS detections in groundwater using existing data sources.