Over the past 15 years Douglas County, NV has removed production wells in northern Carson Valley from use due to relatively high arsenic concentrations (Carl Ruschmeyer, January 2013, Douglas County Public Works Director, verbal communication). To maintain the supply of water to the public, the town of Minden has been providing water to Douglas County and Carson City. Due to the projected increases in municipal demand, water resource managers are concerned that increasing pumping rates from wells in Minden may change groundwater chemistry and degrade the resource by potentially drawing in arsenic enriched water. Long-term exposure to arsenic can cause illnesses ranging from skin discoloration to various cancers including those of the bladder, skin, and kidney (U.S. Environmental Protection Agency, 2001). Naturally occurring arsenic is one of the most common contaminants in groundwater in the western United States. Arsenic found in basin-fill aquifers is oftentimes associated with alluvial/lacustrine sedimentary deposits derived from the weathering of volcanic rocks and geothermal waters (Welch and others, 1988). The primary aquifers beneath Carson Valley are comprised of quaternary aged basin-fill deposits of weathered granitic and volcanic material (Welch, 1994). Factors contributing to increasing arsenic concentrations in groundwater include, but are not limited to, proximity to arsenic bearing rocks, relatively long groundwater flow paths, the application of phosphate containing fertilizers, and leaching from soils in irrigated areas (Busbee and others, 2009; Anning and others, 2012). The vulnerability of groundwater resources to contamination is influenced by the physical properties of the aquifer, pumping rates, locations of wells and screened intervals relative to the groundwater flow system, and geochemical environment (Focazio and others, 2006). Arsenic mobility and transport through the subsurface is largely controlled by the interaction of groundwater with aquifer sediments. Arsenite (As(III)), the reduced form of inorganic arsenic, usually exhibits greater mobility in groundwater than the oxidized form, arsenate (As(V)) largely due to the greater attraction of As(V) to aquifer sediments relative to that of As(III) at pH values exceeding 8.5 (Smedley and Kinniburgh, 2002). Arsenic speciation (form) is influenced by the relative redox condition of the aquifer environment. For example, in the vicinity of the Douglas County Airport, where arsenic speciation has been characterized, arsenic in groundwater collected at depths greater than 250 feet below land surface was found to be primarily As(III); however, in the upper 150 feet of the aquifer As(V) predominated (Paul and others, 2010). This data set provides a spatial and temporal assessment of available chemical and physical data from local, county, state, and federal databases for the Carson Valley, near Minden, Nevada. Critical data gaps will be identified and, if necessary, additional sample collection and monitoring under conditions of routine groundwater pumping from both municipal and agricultural supply wells will be suggested. Data included as part of this data set, are data provided by the USGS and Carson Valley water purveyors with the support of the Carson Water Subconservancy District and Nevada Division of Environmental Protection to evaluate arsenic mobility and transport in Carson Valley. The data available and described in this release are groundwater water level observations and water chemistry for selected wells in the Carson Valley, Nevada. Cited reference information are available in the supplemental information field in the metadata file associated with this data release.

Groundwater arsenic concentrations in the San Joaquin Valley have varied over the decades from 1980 to 2019. This report was compiled to determine whether arsenic concentrations are increasing or decreasing and the mechanism controlling the trends. The San Joaquin Valley contains 4,979 wells with arsenic analyses and possible co-detections of any of the following constituents: dissolved oxygen, field-measured pH, iron, manganese, sulfate, nitrate, or water level. Water quality data comes from two sources: 3,302 wells from with California State Water Resources Control Board - Division of Drinking Water and 1,448 wells from the U.S. Geological Survey National Water Information System (California State Water Resources Control Board – Division of Drinking Water, 2019; U.S. Geological Survey, 2020). There are an additional 229 wells with data from both sources. Other data compiled in addition to the constituents analysed are well type, water use, status, and depth. Well location in relation to the regions defined in the study unit, the El Nido and Pixley subsidence areas, and lateral position from the valley center were also collected (Hansen et al., 2018; Faunt and Sneed, 2015; Faunt, 2009; Voss et al., 2019). The co-detections of constituent trends with arsenic trends was used to determine possible mechanisms controlling arsenic variability in addition to the location and depth of the wells.

Groundwater arsenic concentrations in the San Joaquin Valley have varied over the decades from 1980 to 2019. This report was compiled to determine whether arsenic concentrations are increasing or decreasing and the mechanism controlling the trends. The San Joaquin Valley contains 4,979 wells with arsenic analyses and possible co-detections of any of the following constituents: dissolved oxygen, field-measured pH, iron, manganese, sulfate, nitrate, or water level. Water quality data comes from two sources: 3,302 wells from with California State Water Resources Control Board - Division of Drinking Water and 1,448 wells from the U.S. Geological Survey National Water Information System (California State Water Resources Control Board – Division of Drinking Water, 2019; U.S. Geological Survey, 2020). There are an additional 229 wells with data from both sources. Other data compiled in addition to the constituents analysed are well type, water use, status, and depth. Well location in relation to the regions defined in the study unit, the El Nido and Pixley subsidence areas, and lateral position from the valley center were also collected (Hansen et al., 2018; Faunt and Sneed, 2015; Faunt, 2009; Voss et al., 2019). The co-detections of constituent trends with arsenic trends was used to determine possible mechanisms controlling arsenic variability in addition to the location and depth of the wells.

This product "Predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwest Principal Aquifers study area" is a 1:250,000-scale vector dataset and was developed as part of a regional Southwest Principal Aquifers (SWPA) study. The study examined the vulnerability of basin-fill aquifers in the southwestern United States to nitrate contamination and arsenic enrichment. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions. Separate classifiers were developed for nitrate and arsenic because each constituent was expected to be affected by a different set of factors, and each factor could have a different magnitude or directional influence (increase/decrease) on concentration. For each constituent, two different classifiers were developed; a prediction classifier and a confirmatory classifier. The prediction classifiers were developed specifically to predict nitrate and arsenic concentrations in basin-fill aquifers across the SWPA study area and were based on explanatory variables representing source and susceptibility conditions. These explanatory variables were available throughout the entire SWPA study area and, therefore, did not pose a limitation for using the classifiers to predict concentrations. The confirmatory classifiers were developed to supplement the prediction classifiers in the evaluation of the conceptual model. The name, "confirmatory," reflects the classifier's purpose for evaluation of a-priori hypotheses and contrasts other general types of statistical models, such as those used for prediction or exploratory purposes. The confirmatory classifiers included the explanatory variables used in the prediction classifiers, as well as additional variables representing geochemical conditions and basin groundwater budget components. The inclusion of the geochemical and basin groundwater budget variables in the confirmatory classifiers allowed for further evaluation of the conceptual models, which was not possible with the prediction classifiers alone. The geochemical data, however, were only available at specific well locations, and consistent water-budget data were not available for every basin in the study area. The limited availability of the data for these variables constrained the confirmatory classifiers to observations from 16 case-study basins and precluded use of the confirmatory classifier for predicting concentrations across the SWPA study area. To contrast the scope of the two classifiers, the confirmatory classifiers were developed by using all available explanatory variables but with observations restricted to the 16 case-study basins, whereas the prediction classifiers were unrestricted with respect to spatial extent because these were developed by using a subset of the explanatory variables that were available throughout the study area.

This product "Predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwest Principal Aquifers study area" is a 1:250,000-scale vector dataset and was developed as part of a regional Southwest Principal Aquifers (SWPA) study. The study examined the vulnerability of basin-fill aquifers in the southwestern United States to nitrate contamination and arsenic enrichment. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions. Separate classifiers were developed for nitrate and arsenic because each constituent was expected to be affected by a different set of factors, and each factor could have a different magnitude or directional influence (increase/decrease) on concentration. For each constituent, two different classifiers were developed; a prediction classifier and a confirmatory classifier. The prediction classifiers were developed specifically to predict nitrate and arsenic concentrations in basin-fill aquifers across the SWPA study area and were based on explanatory variables representing source and susceptibility conditions. These explanatory variables were available throughout the entire SWPA study area and, therefore, did not pose a limitation for using the classifiers to predict concentrations. The confirmatory classifiers were developed to supplement the prediction classifiers in the evaluation of the conceptual model. The name, "confirmatory," reflects the classifier's purpose for evaluation of a-priori hypotheses and contrasts other general types of statistical models, such as those used for prediction or exploratory purposes. The confirmatory classifiers included the explanatory variables used in the prediction classifiers, as well as additional variables representing geochemical conditions and basin groundwater budget components. The inclusion of the geochemical and basin groundwater budget variables in the confirmatory classifiers allowed for further evaluation of the conceptual models, which was not possible with the prediction classifiers alone. The geochemical data, however, were only available at specific well locations, and consistent water-budget data were not available for every basin in the study area. The limited availability of the data for these variables constrained the confirmatory classifiers to observations from 16 case-study basins and precluded use of the confirmatory classifier for predicting concentrations across the SWPA study area. To contrast the scope of the two classifiers, the confirmatory classifiers were developed by using all available explanatory variables but with observations restricted to the 16 case-study basins, whereas the prediction classifiers were unrestricted with respect to spatial extent because these were developed by using a subset of the explanatory variables that were available throughout the study area.

This dataset provides aqueous nitrate+nitrite, aqueous manganese, aqueous iron, and total sulfate measurements in groundwater samples from 254 newly constructed private residential wells between 2014 and 2016. The study focuses on three geologically distinct regions of Minnesota: central, northwest, and northeast. These study regions were chosen due to their prevalent elevated As concentrations in drinking water. Each of the 254 wells were sampled in three rounds by the Minnesota Department of Health (MDH). The timing of the three sampling rounds was (1) immediately or shortly after well construction (round 1); (2) 3-6 months after initial sample collection (round 2); and (3) 12 months after initial sample collection (round 3). During each round, samples were collected for both total and aqueous As, aqueous nitrate+nitrite, aqueous manganese, aqueous iron, and total sulfate. Physiochemical characteristics, including specific conductance, pH, dissolved oxygen, oxidation reduction potential, and temperature, were also measured to gage the well water stability prior to sample collection. Round 1 sampling was timed to co-occur and mimic well driller regulatory sampling. Drillers collected samples after well development from the drill rig groundwater pump or from the residential plumbing, and the MDH sampler replicated the sample location and timing used by the driller. Sampling from the drill rig’s groundwater pump occurred after the well was drilled and developed, when the water was visibly clear, with little visible sediment particles. Samples from plumbing were collected after the plumbing was flushed out and physiochemical characteristic readings stabilized. Round 2 and round 3 by MDH staff were collected only from plumbing. Samples collected from plumbing were taken from faucets, hydrants, or pressure tanks prior to filters or treatment systems.

This dataset provides aqueous nitrate+nitrite, aqueous manganese, aqueous iron, and total sulfate measurements in groundwater samples from 254 newly constructed private residential wells between 2014 and 2016. The study focuses on three geologically distinct regions of Minnesota: central, northwest, and northeast. These study regions were chosen due to their prevalent elevated As concentrations in drinking water. Each of the 254 wells were sampled in three rounds by the Minnesota Department of Health (MDH). The timing of the three sampling rounds was (1) immediately or shortly after well construction (round 1); (2) 3-6 months after initial sample collection (round 2); and (3) 12 months after initial sample collection (round 3). During each round, samples were collected for both total and aqueous As, aqueous nitrate+nitrite, aqueous manganese, aqueous iron, and total sulfate. Physiochemical characteristics, including specific conductance, pH, dissolved oxygen, oxidation reduction potential, and temperature, were also measured to gage the well water stability prior to sample collection. Round 1 sampling was timed to co-occur and mimic well driller regulatory sampling. Drillers collected samples after well development from the drill rig groundwater pump or from the residential plumbing, and the MDH sampler replicated the sample location and timing used by the driller. Sampling from the drill rig’s groundwater pump occurred after the well was drilled and developed, when the water was visibly clear, with little visible sediment particles. Samples from plumbing were collected after the plumbing was flushed out and physiochemical characteristic readings stabilized. Round 2 and round 3 by MDH staff were collected only from plumbing. Samples collected from plumbing were taken from faucets, hydrants, or pressure tanks prior to filters or treatment systems.

This dataset provides aqueous nitrate+nitrite, aqueous manganese, aqueous iron, and total sulfate measurements in groundwater samples from 254 newly constructed private residential wells between 2014 and 2016. The study focuses on three geologically distinct regions of Minnesota: central, northwest, and northeast. These study regions were chosen due to their prevalent elevated As concentrations in drinking water. Each of the 254 wells were sampled in three rounds by the Minnesota Department of Health (MDH). The timing of the three sampling rounds was (1) immediately or shortly after well construction (round 1); (2) 3-6 months after initial sample collection (round 2); and (3) 12 months after initial sample collection (round 3). During each round, samples were collected for both total and aqueous As, aqueous nitrate+nitrite, aqueous manganese, aqueous iron, and total sulfate. Physiochemical characteristics, including specific conductance, pH, dissolved oxygen, oxidation reduction potential, and temperature, were also measured to gage the well water stability prior to sample collection. Round 1 sampling was timed to co-occur and mimic well driller regulatory sampling. Drillers collected samples after well development from the drill rig groundwater pump or from the residential plumbing, and the MDH sampler replicated the sample location and timing used by the driller. Sampling from the drill rig’s groundwater pump occurred after the well was drilled and developed, when the water was visibly clear, with little visible sediment particles. Samples from plumbing were collected after the plumbing was flushed out and physiochemical characteristic readings stabilized. Round 2 and round 3 by MDH staff were collected only from plumbing. Samples collected from plumbing were taken from faucets, hydrants, or pressure tanks prior to filters or treatment systems.

This data release documents four tables that contain data for assessing the susceptibility of groundwater using environmental tracers collected from public-supply wells located in the Northern Atlantic Coastal Plain (NACP) Aquifer System and Piedmont and Blue Ridge Crystalline-Rock Aquifers of Eastern United States. Results for two modeling support studies located within the NACP are also included. Table 1 provides the primary results of this study and it contains condensed results from dissolved gas modeling and calculated environmental tracer concentrations, as well as results of the tritium age classification, susceptibility index, the mean groundwater age, fraction of Modern water (water that was recharged after 1952), and detailed lumped parameter model calibration results of each sample in this study. Mean groundwater ages were determined by calibration of environmental tracers (tritium, tritiogenic helium-3, sulfur hexafluoride, carbon-14 and radiogenic helium-4) to lumped parameter models for 231 public-supply wells. Calibrated lumped parameter models provide the optimal mean age and mixing parameter(s) used to compute the distribution of ages that explain the measured tracer concentrations in a sample. Tables two, three, and four provide results in support of table 1. Table two reports detailed results for the calibration of dissolved gas models to neon, argon, krypton, xenon, and nitrogen. Calibrated dissolved gas models provide the optimal water temperature, excess air, entrapped air, fractionation of gases, and excess nitrogen gas (mainly from denitrification) that explain the measured dissolved gases in a sample. Table three reports measured concentrations and the detailed calculations of environmental tracer concentrations derived from the dissolved gas modeling results reported in table 2. The dry-air mixing ratio is the atmospheric concentration (assuming the water has a single age) at the time of gas-water equilibration and is calculated for transient atmospheric gas tracers such as sulfur hexafluoride and chlorofluorocarbons. Tritiogenic helium-3 is the concentration of helium-3 that resulted from the decay of tritium and radiogenic helium-4 is the amount of helium generated from the decay of uranium and thorium in aquifer sediments. Table 4 reports results of calculated carbon-14 corrections caused by dissolution of carbonate minerals in the soil and saturated zone. Calculated carbon-14 corrections can be determined from analytical models of carbonate dissolution or from inverse geochemical modeling of the evolution of groundwater chemistry of a sample. The corrected carbon-14 concentration can be compared directly to carbon-14 atmospheric records, otherwise, dilution of the atmospheric record was inferred from Modern groundwater sample with 2 or more environmental tracers. In addition to these four tables, two ancillary tables are included to provide more detailed information about the fields and the abbreviations used in tables one through four. Please see processing steps in the general metadata file for more detailed information about the methods used to create the tables.

This data release documents four tables that contain data for assessing the susceptibility of groundwater using environmental tracers collected from public-supply wells located in the Northern Atlantic Coastal Plain (NACP) Aquifer System and Piedmont and Blue Ridge Crystalline-Rock Aquifers of Eastern United States. Results for two modeling support studies located within the NACP are also included. Table 1 provides the primary results of this study and it contains condensed results from dissolved gas modeling and calculated environmental tracer concentrations, as well as results of the tritium age classification, susceptibility index, the mean groundwater age, fraction of Modern water (water that was recharged after 1952), and detailed lumped parameter model calibration results of each sample in this study. Mean groundwater ages were determined by calibration of environmental tracers (tritium, tritiogenic helium-3, sulfur hexafluoride, carbon-14 and radiogenic helium-4) to lumped parameter models for 231 public-supply wells. Calibrated lumped parameter models provide the optimal mean age and mixing parameter(s) used to compute the distribution of ages that explain the measured tracer concentrations in a sample. Tables two, three, and four provide results in support of table 1. Table two reports detailed results for the calibration of dissolved gas models to neon, argon, krypton, xenon, and nitrogen. Calibrated dissolved gas models provide the optimal water temperature, excess air, entrapped air, fractionation of gases, and excess nitrogen gas (mainly from denitrification) that explain the measured dissolved gases in a sample. Table three reports measured concentrations and the detailed calculations of environmental tracer concentrations derived from the dissolved gas modeling results reported in table 2. The dry-air mixing ratio is the atmospheric concentration (assuming the water has a single age) at the time of gas-water equilibration and is calculated for transient atmospheric gas tracers such as sulfur hexafluoride and chlorofluorocarbons. Tritiogenic helium-3 is the concentration of helium-3 that resulted from the decay of tritium and radiogenic helium-4 is the amount of helium generated from the decay of uranium and thorium in aquifer sediments. Table 4 reports results of calculated carbon-14 corrections caused by dissolution of carbonate minerals in the soil and saturated zone. Calculated carbon-14 corrections can be determined from analytical models of carbonate dissolution or from inverse geochemical modeling of the evolution of groundwater chemistry of a sample. The corrected carbon-14 concentration can be compared directly to carbon-14 atmospheric records, otherwise, dilution of the atmospheric record was inferred from Modern groundwater sample with 2 or more environmental tracers. In addition to these four tables, two ancillary tables are included to provide more detailed information about the fields and the abbreviations used in tables one through four. Please see processing steps in the general metadata file for more detailed information about the methods used to create the tables.