This dataset includes the elemental composition of 28 hydrant flush solids collected during routine hydrant flushing events in some of the utilities studied. The link to all remaining raw data (publicly accessible through the US EPA's Arsenic Demo website) is also provided. This dataset is associated with the following publication: Triantafyllidou, S., D. Lytle, A. Chen, L. Wang, T. Sorg, and C. Muhlen. Patterns of Arsenic Release in Distribution Systems. AWWA Water Science. John Wiley & Sons, Inc., Hoboken, NJ, USA, 1(4): e1149, (2019).

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.

This data includes information on Arsenic violations in the US, including time patterns and spatial patterns in Arsenic violations, and people served by systems in violation. Most of the data is from the Safe Drinking Water Information System. This dataset is associated with the following publication: Foster, S., M. Pennino, J. Compton, S. Leibowitz, and M. Kile. Arsenic Drinking Water Violations Decreased Across the United States Following Revision of the Maximum Contaminant Level.. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(19): 11478-11485, (2019).

This data includes information on Arsenic violations in the US, including time patterns and spatial patterns in Arsenic violations, and people served by systems in violation. Most of the data is from the Safe Drinking Water Information System. This dataset is associated with the following publication: Foster, S., M. Pennino, J. Compton, S. Leibowitz, and M. Kile. Arsenic Drinking Water Violations Decreased Across the United States Following Revision of the Maximum Contaminant Level.. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(19): 11478-11485, (2019).

Table showing linear combination fitting data for arsenic and lead speciation in three soils. This dataset is associated with the following publication: Kastury, F., E. Smith, R.R. Karna, K.G. Scheckel, and A.L. Juhasz. Methodological factors influencing inhalation bioaccessibility of metal(loid)s in PM2.5 using simulated lung fluid. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 241: 930-937, (2018).

Table showing linear combination fitting data for arsenic and lead speciation in three soils. This dataset is associated with the following publication: Kastury, F., E. Smith, R.R. Karna, K.G. Scheckel, and A.L. Juhasz. Methodological factors influencing inhalation bioaccessibility of metal(loid)s in PM2.5 using simulated lung fluid. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 241: 930-937, (2018).

Documented in this data release are data used to model and map the probability of arsenic being greater than 10 micrograms per liter in private domestic wells throughout the conterminous United States during drought conditions (Lombard and others, 2020). The model used to predict the probability of arsenic exceeding 10 micrograms per liter in private domestic wells was previously developed and documented by Ayotte and others (2017). Independent variables in the model include groundwater recharge and annual precipitation. In order to assess the impact of drought these variables were altered to simulate drought by reducing the 30-year average annual values by 25 and 50 percent. The impact of drought was also assessed by using groundwater recharge and precipitation values from the year 2012 when approximately 66 percent of the contiguous United States experienced drought. Data sources for groundwater recharge and precipitation for the year 2012 differ from those used in the original model and the drought simulations, therefore a 30-year average climate model was also produced using these new data sources (Thornton and others, 2018; Hay, 2019). Data are documented from the original model, the drought simulations with reduced values of groundwater recharge and precipitation, the year 2012 and the average annual precipitation and groundwater recharge from 1981 - 2010 from the new data sources. The model input data that were used to make the prediction maps are within a zipped folder (Prediction_Input_Data.zip) that contains 50 files, one for each model predictor variable. These include the predictor variables from the original model as well as the updated precipitation and groundwater recharge variables for the year 2012 and the average annual values based on the years1981 - 2010, and groundwater recharge and precipitation variables that were systematically decreased for drought simulations. The model prediction outputs are within a zipped folder (Prediction_Output_Data.zip) that contains 10 tif-format raster files, one for each of the eight drought simulations, one for the year 2012, and one for the updated average annual precipitation and groundwater recharge variables for 1981 - 2010. A third zipped folder (Change_Prob_Maps.zip) contains 10 tif-raster files that show the change in probability of arsenic exceeding 10 micrograms per liter in private domestic wells based on the drought simulations and the data used for the year 2012.

Documented in this data release are data used to model and map the probability of arsenic being greater than 10 micrograms per liter in private domestic wells throughout the conterminous United States during drought conditions (Lombard and others, 2020). The model used to predict the probability of arsenic exceeding 10 micrograms per liter in private domestic wells was previously developed and documented by Ayotte and others (2017). Independent variables in the model include groundwater recharge and annual precipitation. In order to assess the impact of drought these variables were altered to simulate drought by reducing the 30-year average annual values by 25 and 50 percent. The impact of drought was also assessed by using groundwater recharge and precipitation values from the year 2012 when approximately 66 percent of the contiguous United States experienced drought. Data sources for groundwater recharge and precipitation for the year 2012 differ from those used in the original model and the drought simulations, therefore a 30-year average climate model was also produced using these new data sources (Thornton and others, 2018; Hay, 2019). Data are documented from the original model, the drought simulations with reduced values of groundwater recharge and precipitation, the year 2012 and the average annual precipitation and groundwater recharge from 1981 - 2010 from the new data sources. The model input data that were used to make the prediction maps are within a zipped folder (Prediction_Input_Data.zip) that contains 50 files, one for each model predictor variable. These include the predictor variables from the original model as well as the updated precipitation and groundwater recharge variables for the year 2012 and the average annual values based on the years1981 - 2010, and groundwater recharge and precipitation variables that were systematically decreased for drought simulations. The model prediction outputs are within a zipped folder (Prediction_Output_Data.zip) that contains 10 tif-format raster files, one for each of the eight drought simulations, one for the year 2012, and one for the updated average annual precipitation and groundwater recharge variables for 1981 - 2010. A third zipped folder (Change_Prob_Maps.zip) contains 10 tif-raster files that show the change in probability of arsenic exceeding 10 micrograms per liter in private domestic wells based on the drought simulations and the data used for the year 2012.

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.