The dataset contains chromatographic traces of samples containing thioarsenic species and solubility data for disordered orpiment (arsenic sulfide). This dataset is associated with the following publication: Wilkin, R.T., R.G. Ford, L.M. Costantino, R.R. Ross, D.G. Beak, and K.G. Scheckel. Thioarsenite Detection and Implications for Arsenic Transport in Groundwater. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(20): 11684-11693, (2019).

The dataset shows the weighted percentage of arsenic speciation for untreated and treated soil samples with amendments designed to immobilize arsenic in soils. This dataset is associated with the following publication: Mele, E., E. Donner, A. Juhasz, G. Brunetti, E. Smith, A. Betts , P. Castaldi, S. Deiana, K. Scheckel , and E. Lombi. In situ fixation of metal(loid)s in contaminated soils: a comparison of conventional, by product and engineered soil amendments. David L. Sedlak ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 49: 13501-13509, (2015).

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.

These data contain concentrations of tetrachloroethylene in groundwater samples collected from 2011 to 2015 by the EPA. These data are a subset of all the groundwater at the site. Samples were collected using direct-push technology. In order to protect personally identifiable information (PII), all data collected by the U.S. Environmental Protection Agency do not contain spatial information. Please contact the U.S. Environmental Protection Agency for permission and access to spatial information for these samples. These data support the following publication: Wilson, J.L., Limmer, M.A., Samaranayake, V.A., Burken, J.G., 2018, Phytoforensics: trees as bioindicators of potential indoor exposure via vapor intrusion: PLoS ONE, v. 13, no. 2. DOI: 10.1371/journal.pone.0193247.

Measurements of Arsenic collected at Putah Creek Below Monticello Dam. Currently collected twice a year, previously collected quarterly. Access further information for this data set by contacting Bureau of Reclamation, California-Great Basin Region, Environmental Affairs Division (CGB-157). See ResultAttributes for STAFF_GAUGE, SMPL_DEPTH, SMPL_CATEGORY_NAME, METHOD_CODE, RESULT_RL, RESULT_RL-UNIT_STD_NAME, RESULT_MDL, RESULT_MDL-UNIT_STD_NAME, USBR_QA_SUBTYPE_NAME, USBR_QULFR_DESCRIPTION. STAFF_GAUGE is the water height in decimal feet measured by gauge (e.g., 15.2). SMPL_DEPTH is the vertical depth at which sample is collected (e.g., 0 - 15 cm). For water samples: depth below water/air interface. For sediment and soil samples: depth below water/solid or air/solid interface. SMPL_CATEGORY_NAME is the category type of sample (e.g., Composite). METHOD_CODE is the name of method used to obtain result (e.g., EPA 200.8). RESULT_RL is the result reporting limit (accounting for dilution) (e.g., 0.02). RESULT_RL-UNIT_STD_NAME is the unit associated with RESULT_RL (e.g., mg/L). RESULT_MDL is the result method detection limit (e.g., 0.007). RESULT_MDL-UNIT_STD_NAME is the unit associated with RESULT_MDL (e.g., mg/L). USBR_QA_SUBTYPE_NAME is the quality control type of the sample (e.g., USBR_BLANK_SPIKE). USBR_QULFR_DESCRIPTION is the quality assurance description (if any) (e.g., Result may have a high bias.).

This data release contains two spatial datasets and a data table in support of an evaluation of arsenic and uranium occurrence in Connecticut groundwater through spatially weighted and bedrock geology assessments. Spatial datasets include 1) a shapefile of 130 equal-area grid cells with associated arsenic attribute data, and 2) a shapefile of 110 equal-area grid cells with associated uranium attribute data. The State of Connecticut was divided based on a set of randomized equal-area grid cells based on the method of Scott (1990); one grid was created for arsenic, with 130 grid cells, and one was created for uranium, with 110 grid cells. Arsenic and uranium attribute data associated with the equal-area grid cells include the number of wells in each grid cell, the number of wells with constituent concentrations above three selected thresholds, the fraction of wells with constituent concentrations above three selected thresholds, and the percentage of wells with constituent concentrations above three selected thresholds. The three selected thresholds for arsenic include 3, 5, and 10 micrograms per liter (ug/L), with 10 ug/L representing the maximum contaminant level (MCL) established by the U.S. Environmental Protection Agency (EPA) for human health for arsenic. The three selected thresholds for uranium include 1, 10, and 30 ug/L, with 30 ug/L representing the EPA MCL for human health for uranium. The bedrock geology data table is table 4 from Gross and others (2020) formatted so that it can easily be joined with Connecticut's bedrock geology dataset (Connecticut Department of Environmental Protection, 2000) using the geologic unit abbreviation (UNIT attribute) in order to recreate figure 3 from Gross and others (2020). The data table includes counts and percentages of arsenic and uranium concentrations that exceed maximum contaminant levels from private wells in Connecticut, by geologic unit and major bedrock category, 2013-18.

This data release contains two spatial datasets and a data table in support of an evaluation of arsenic and uranium occurrence in Connecticut groundwater through spatially weighted and bedrock geology assessments. Spatial datasets include 1) a shapefile of 130 equal-area grid cells with associated arsenic attribute data, and 2) a shapefile of 110 equal-area grid cells with associated uranium attribute data. The State of Connecticut was divided based on a set of randomized equal-area grid cells based on the method of Scott (1990); one grid was created for arsenic, with 130 grid cells, and one was created for uranium, with 110 grid cells. Arsenic and uranium attribute data associated with the equal-area grid cells include the number of wells in each grid cell, the number of wells with constituent concentrations above three selected thresholds, the fraction of wells with constituent concentrations above three selected thresholds, and the percentage of wells with constituent concentrations above three selected thresholds. The three selected thresholds for arsenic include 3, 5, and 10 micrograms per liter (ug/L), with 10 ug/L representing the maximum contaminant level (MCL) established by the U.S. Environmental Protection Agency (EPA) for human health for arsenic. The three selected thresholds for uranium include 1, 10, and 30 ug/L, with 30 ug/L representing the EPA MCL for human health for uranium. The bedrock geology data table is table 4 from Gross and others (2020) formatted so that it can easily be joined with Connecticut's bedrock geology dataset (Connecticut Department of Environmental Protection, 2000) using the geologic unit abbreviation (UNIT attribute) in order to recreate figure 3 from Gross and others (2020). The data table includes counts and percentages of arsenic and uranium concentrations that exceed maximum contaminant levels from private wells in Connecticut, by geologic unit and major bedrock category, 2013-18.

This data release contains groundwater-quality data for three parameters of interest (arsenic, manganese, and pH) and well information for sample sites for aquifers in the conterminous U.S. Water-quality data and well information were derived from a dataset compiled from three sources: the U.S. Geological Survey (USGS) National Water Information System (NWIS), the U.S. Environmental Protection Agency (USEPA) Safe Drinking Water Information System (SDWIS), and numerous agencies and organizations at the state, regional, and local level. The data compilation of the National Water Quality Program’s groundwater assessment team is an internal dataset informally referred to as the National Groundwater Aggregation (NGA). The current study of groundwater quality in the conterminous U.S. augments data compiled by others globally. Only geochemical parameters of interest (arsenic, manganese, pH) from wells in the national groundwater aggregation are presented—data from springs were not used. A table of site information includes attributes for each well, such as the state, water use code, depth, open interval (if available) and aquifer (if available). The provider of the water-quality data and well information in also in this table.

This data release contains groundwater-quality data for three parameters of interest (arsenic, manganese, and pH) and well information for sample sites for aquifers in the conterminous U.S. Water-quality data and well information were derived from a dataset compiled from three sources: the U.S. Geological Survey (USGS) National Water Information System (NWIS), the U.S. Environmental Protection Agency (USEPA) Safe Drinking Water Information System (SDWIS), and numerous agencies and organizations at the state, regional, and local level. The data compilation of the National Water Quality Program’s groundwater assessment team is an internal dataset informally referred to as the National Groundwater Aggregation (NGA). The current study of groundwater quality in the conterminous U.S. augments data compiled by others globally. Only geochemical parameters of interest (arsenic, manganese, pH) from wells in the national groundwater aggregation are presented—data from springs were not used. A table of site information includes attributes for each well, such as the state, water use code, depth, open interval (if available) and aquifer (if available). The provider of the water-quality data and well information in also in this table.