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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.

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

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.

Nitrate contamination of groundwater in agricultural areas poses a major challenge to the sustainability of water resources. Aquifer vulnerability models are useful tools that can help resource managers identify areas of concern, but quantifying nitrogen (N) inputs in such models is challenging, especially at large spatial scales. We sought to improve regional nitrate (NO 3 − ) input functions by characterizing unsaturated zone NO 3 − transport to groundwater through use of surrogate, machine-learning metamodels of a process-based N flux model. The metamodels used boosted regression trees (BRTs) to relate mappable landscape variables to parameters and outputs of a previous “vertical flux method” (VFM) applied at sampled wells in the Fox, Wolf, and Peshtigo (FWP) river basins in northeastern Wisconsin. In this context, the metamodels upscaled the VFM results throughout the region, and the VFM parameters and outputs are the metamodel response variables. The study area encompassed the domain of a detailed numerical model that provided additional predictor variables, including groundwater recharge, to the metamodels. We used a statistical learning framework to test a range of model complexities to identify suitable hyperparameters of the six BRT metamodels corresponding to each response variable of interest: NO 3 − source concentration factor (which determines the local NO 3 − input concentration); unsaturated zone travel time; NO 3 − concentration at the water table in 1980, 2000, and 2020 (three separate metamodels); and NO 3 − “extinction depth”, the eventual steady state depth of the NO 3 − front. The final metamodels were trained to 129 wells within the active numerical flow model area, and considered 58 mappable predictor variables compiled in a geographic information system (GIS). These metamodels had training and cross-validation testing R 2 values of 0.52 – 0.86 and 0.22 – 0.38, respectively, and predictions were compiled as maps of the above response variables. Testing performance was reasonable, considering that we limited the metamodel predictor variables to mappable factors as opposed to using all available VFM input variables. Relationships between metamodel predictor variables and mapped outputs were generally consistent with expectations, e.g. with greater source concentrations and NO 3 − at the groundwater table in areas of intensive crop use and well drained soils. Shorter unsaturated zone travel times in poorly drained areas likely indicated preferential flow through clay soils, and a tendency for fine grained deposits to collocate with areas of shallower water table. Numerical estimates of groundwater recharge were important in the metamodels and may have been a proxy for N input and redox conditions in the northern FWP, which had shallow predicted NO 3 − extinction depth. The metamodel results provide proof-of-concept for regional characterization of unsaturated zone NO 3 − transport processes in a statistical framework based on readily mappable GIS input variables.

The global market for liquid mixed-metal stabilizers is migrating toward heavy-metal-free products. Historically, these systems have not been performance and cost competitive. High efficiency calcium-zinc stabilizers have the potential to replace heavy-metal-based products. The intrinsic value of non-phenolic lubricating calcium intermediates and calcium-zinc stabilizers is discussed.

The Washington DC Area geologic map database (DCDB) offers geologic map information to many types of professionals and to private citizens. Digital, geographically referenced, geologic data is more versatile than traditional hard copy maps, and facilitates the examination of relationships between numerous aspects of the geology and other types of data such as: landuse data, vegetation characteristics, surface water flow and chemistry, and various types of remotely sensed images. The DCDB was created by combining Arc/Info coverages, designing a Microsoft (MS) Access database, and populating this database. Proposed improvements to the DCDB include the addition of geochemical, structural, and hydrologic data.

This preliminary experimental lithogeochemical map shows the distribution of rock types in the Virginia and Maryland parts of the Chesapeake Bay watershed. The map was produced digitally by classifying geologic-map units according to composition, mineralogy, and texture; rather than by age and stratigraphic relationships as shown on traditional geologic maps. This map differs from most lithologic maps in that the lithogeochemical unit classification distinguishes those rock units having key water-reactive minerals that may induce acid neutralization, or reduction, of hosted water at the weathering interface. The validity of these rock units, however, is independent of water chemistry, because the rock units are derived from geologic maps and rock descriptions. Areas of high soil carbon content, and sulfide metal deposits are also shown. Water-reactive minerals and their weathering reactions yield five lithogeochemical unit classes: 1) carbonate rock and calcareous rocks and sediments, the most acid-neutralizing; 2)carbonaceous-sulfidic rocks and sediments, oxygen-depleting and reducing; 3) quartzofeldspathic rocks and siliciclastic sediments, relatively weakly reactive with water; 4) mafic silicate rocks/sediments, oxygen consuming and high solute-load delivering; and, 5) the rarer calcareous-sulfidic (carbonaceous) rocks, neutralizing and reducing. Earlier studies in some parts of the map area have related solute loads in ground and stream waters to some aspects of bedrock lithology. More recent preliminary tests of relationships between four of the classes of mapped lithogeochemical units and ground water chemistry, in the Mid-Atlantic area using this map, have focused on and verified the nitrate-reducing and acid-neutralizing properties of some bedrock and unconsolidated aquifer rock types. Sulfide mineral deposits and their mine-tailings effects on waters are beginning to be studied by others. Additional testing of relationships among the lithogeochemical units and aspects of ground and surface water chemistry could help to refine the lithogeochemical classification, and this map. The testing could also improve the usefulness of the map for assessing aquifer reactivity and the transport properties of reactive contaminants such as acid rain, and nitrate from agricultural sources, in the Chesapeake Bay watershed.