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.

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.

Les Arihood, Scientist Emeritus, U.S. Geological Survey (USGS), for his assistance in interpreting water-well drillers' records for the lithologic database and Sue Kahle, USGS hydrologist, for her contribution to the discussion of glacial geology in the northwestern United States. Finally, we thank Tom Nolan, USGS hydrologist, for his application of the dendrogram analysis in the comparison of sediment and aquifer characteristics of hydrogeologic terranes.

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Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large‐scale numerical models can be resource intensive. Using a novel automated approach, a set of 115 inexpensive general simulation models (GSMs) was used to create RTD metrics (fraction of young groundwater, defined as <65 years old; mean travel time of young fraction; median travel time of old fraction; and mean path length). GSMs captured the general trends in measured tritium concentrations in 431 wells. Boosted Regression Tree metamodels were trained to predict these RTD metrics using available wall‐to‐wall hydrogeographic digital sets as explanatory features. The metamodels produced a three‐dimensional distribution of predictions throughout the glacial system that generally matched with the numerical model RTD metrics. In addition to the expected importance of aquifer thickness and recharge rate in predicting RTD metrics, two new data sets, Multi‐Order Hydrologic Position (MOHP) and hydrogeologic terrane were important predictors. These variables by themselves produced metamodels with Nash‐Sutcliffe efficiency close to the full metamodel. Metamodel predictions showed that the volume of young groundwater stored in the glaciated United States is about 6,000 km3, or about 0.5% of globally stored young groundwater.