The map graphic image at https://www.sciencebase.gov/catalog/file/get/63140561d34e36012efa2b7f?name=arsenic_map.png illustrates arsenic values, in micrograms per liter, for groundwater samples from about 31,000 wells and springs in 49 states compiled by the United States Geological Survey (USGS). The map graphic illustrates an updated version of figure 1 from Ryker (2001). Cited Reference: Ryker, S.J., Nov. 2001, Mapping arsenic in groundwater-- A real need, but a hard problem: Geotimes Newsmagazine of the Earth Sciences, v. 46 no. 11, p. 34-36 at http://www.agiweb.org/geotimes/nov01/feature_Asmap.html. An excel tabular data file, a txt file, along with a GIS shape file of arsenic concentrations (20,043 samples collected by the USGS) for a subset of the sites shown on the map. Samples were collected between 1973 and 2001 and are provided for download.

This data release contains data used to develop models and maps that estimate probabilities of exceeding various thresholds of arsenic concentrations in private domestic wells throughout the conterminous United States. Three boosted regression tree (BRT) models were developed separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, and 10 micrograms per liter (µg/L). A random forest (RF) model was developed to estimate the most probable arsenic concentration category (≤5, >5 to ≤10, or >10 µg/L). The models use arsenic concentration data from private domestic wells located throughout the conterminous United States and independent variables that are available as geospatial data. The models were used to produce maps that are included in this data release. The model input data (predictor variables) that were used to make the maps are within a zipped folder (Map_Input_Data.zip) that contains 85 tif-raster files, one for each model predictor variable. The map probability estimates that are outputs from the model are in a zipped folder (Map_Output_Data.zip) that contains 13 tif-raster files, one model estimate map for each of the BRT models and four for the RF model, as well as 2 confidence interval maps for each BRT model.

This data release contains data used to develop models and maps that estimate probabilities of exceeding various thresholds of arsenic concentrations in private domestic wells throughout the conterminous United States. Three boosted regression tree (BRT) models were developed separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, and 10 micrograms per liter (µg/L). A random forest (RF) model was developed to estimate the most probable arsenic concentration category (≤5, >5 to ≤10, or >10 µg/L). The models use arsenic concentration data from private domestic wells located throughout the conterminous United States and independent variables that are available as geospatial data. The models were used to produce maps that are included in this data release. The model input data (predictor variables) that were used to make the maps are within a zipped folder (Map_Input_Data.zip) that contains 85 tif-raster files, one for each model predictor variable. The map probability estimates that are outputs from the model are in a zipped folder (Map_Output_Data.zip) that contains 13 tif-raster files, one model estimate map for each of the BRT models and four for the RF model, as well as 2 confidence interval maps for each BRT model.

Arsenic concentrations from 20,450 domestic wells in the U.S. were used to develop a logistic regression model of the probability of having arsenic > 10 µg/L (“high arsenic”), which is presented at the county, state, and national scales. Variables representing geologic sources, geochemical, hydrologic, and physical features were among the significant predictors of high arsenic. For U.S. Census blocks, the mean probability of arsenic > 10 µg/L was multiplied by the population using domestic wells to estimate the potential high-arsenic domestic-well population. Approximately 44.1 M people in the U.S. use water from domestic wells. The population in the conterminous U.S. using water from domestic wells with predicted arsenic concentration > 10 µg/L is 2.1 M people (95% CI is 1.5 to 2.9 M). Although areas of the U.S. were underrepresented with arsenic data, predictive variables available in national datasets were used to estimate high arsenic in unsampled areas. Linking predictive modeling to private well use information nationally, despite the uncertainty, is beneficial for broad screening of the population at risk from elevated arsenic in drinking water from private wells. This dataset represents modeled probabilities of arsenic > 10 micrograms per liter in domestic wells in the U.S.

Arsenic concentrations from 20,450 domestic wells in the U.S. were used to develop a logistic regression model of the probability of having arsenic > 10 µg/L (“high arsenic”), which is presented at the county, state, and national scales. Variables representing geologic sources, geochemical, hydrologic, and physical features were among the significant predictors of high arsenic. For U.S. Census blocks, the mean probability of arsenic > 10 µg/L was multiplied by the population using domestic wells to estimate the potential high-arsenic domestic-well population. Approximately 44.1 M people in the U.S. use water from domestic wells. The population in the conterminous U.S. using water from domestic wells with predicted arsenic concentration > 10 µg/L is 2.1 M people (95% CI is 1.5 to 2.9 M). Although areas of the U.S. were underrepresented with arsenic data, predictive variables available in national datasets were used to estimate high arsenic in unsampled areas. Linking predictive modeling to private well use information nationally, despite the uncertainty, is beneficial for broad screening of the population at risk from elevated arsenic in drinking water from private wells. This dataset represents modeled probabilities of arsenic > 10 micrograms per liter in domestic wells in the U.S.

This dataset reports total arsenic (AsTot) results analyzed using an in-field biosensor system, Field-Ready Electrochemical Detector for Arsenic (FRED-Arsenic), developed by FREDsense Technologies Corp., Calgary, Alberta, Canada. Samples were collected from two public-supply wells (NH-SGW 93 and NH-SGW 65) and one private well (NH-KFW 87). NH-SGW 93 and NH-KFW 87 both withdraw water from a crystalline-rock aquifer. NH-SGW 65 withdraws water from a glacial sand and gravel aquifer. Twelve samples for NH-KFW 87 were collected and analyzed on May 14, 2019 with sample times ranging from 0730 to 1800, and 12 samples were collected and analyzed on August 20, 2019 with sample times ranging from 0830 to 1320. Eleven samples for NH-SGW 93 were collected and analyzed on May 15, 2019 with sample times ranging from 0838 to 1437, with the last sample collected when the pump was turned off; 12 sampled were collected and analyzed on August 21, 2019 with sample times ranging from 0807 to 1159. Twelve samples for NH-SGW 65 were collected and analyzed on May 16, 2019 with sample times ranging from 0931 to 1400; 12 samples were collected and analyzed on August 22, 2019 with sample times ranging from 0813 to 1057.

This dataset reports total arsenic (AsTot) results analyzed using an in-field biosensor system, Field-Ready Electrochemical Detector for Arsenic (FRED-Arsenic), developed by FREDsense Technologies Corp., Calgary, Alberta, Canada. Samples were collected from two public-supply wells (NH-SGW 93 and NH-SGW 65) and one private well (NH-KFW 87). NH-SGW 93 and NH-KFW 87 both withdraw water from a crystalline-rock aquifer. NH-SGW 65 withdraws water from a glacial sand and gravel aquifer. Twelve samples for NH-KFW 87 were collected and analyzed on May 14, 2019 with sample times ranging from 0730 to 1800, and 12 samples were collected and analyzed on August 20, 2019 with sample times ranging from 0830 to 1320. Eleven samples for NH-SGW 93 were collected and analyzed on May 15, 2019 with sample times ranging from 0838 to 1437, with the last sample collected when the pump was turned off; 12 sampled were collected and analyzed on August 21, 2019 with sample times ranging from 0807 to 1159. Twelve samples for NH-SGW 65 were collected and analyzed on May 16, 2019 with sample times ranging from 0931 to 1400; 12 samples were collected and analyzed on August 22, 2019 with sample times ranging from 0813 to 1057.

Groundwater samples from public and private drinking water wells throughout the state of New Hampshire were analyzed for total Arsenic (As). Samples were collected after pH, specific conductivity, dissolved oxygen, and water temperature had met stabilization criteria as outlined in the USGS National Field Manual (United States Geological Survey 2005).The As analyses were carried out in the geochemistry laboratory in the Department of Earth Sciences at the University of New Hampshire (UNH). Not including replicate analysis, a total of 527 samples were analyzed via a hydride generator-inductively coupled plasma mass spectrometer (HG-ICP-MS) using a Cetac HGX-200 plumbed into a Nu Instruments Attom high-resolution inductively coupled plasma mass spectrometer following procedures adapted from Klaue and Blum (1999). Diluted aliquots of sample were run in triplicate, and the reported uncertainty is the standard deviation on the mean of these analyses. Generally, the data have a detection limit of ~ 0.2 μg/L as determined from repeated assessment of analytical blanks and using the conventional approach of defining the detection limit as the mean blank + ten times the standard deviation around the mean blank. These data are sensitive as they include sampling locations from privately owned wells, hence, latitude and longitude information is not included with the data set.

This data release contains a table of measured arsenic concentrations and associated model input variables used to test existing multivariate logistic regression models that predict the probabilities of arsenic concentrations exceeding threshold values of 1, 5, and 10 micrograms per liter in bedrock aquifers of New Hampshire. Location data are censored to the county level.

This data release contains a table of measured arsenic concentrations and associated model input variables used to test existing multivariate logistic regression models that predict the probabilities of arsenic concentrations exceeding threshold values of 1, 5, and 10 micrograms per liter in bedrock aquifers of New Hampshire. Location data are censored to the county level.