This is the manuscript and supplementary material for "Speciation of Inorganic Arsenic with Mixed-Mode HPLC- Electrospray Ionization-Mass Spectrometry and Arsenite Oxidation". This dataset is associated with the following publication: Li, T. Speciation of inorganic arsenic with mixed mode HPLC-ESI-MS and Arsenite Oxidation. TALANTA. Elsevier Science Ltd, New York, NY, USA, 259: 124487, (2023).

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.

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.

The U.S. Geological Survey (USGS), in cooperation with the U.S. Centers for Disease Control and Prevention and the Maine Center for Disease Control and Prevention, assessed the physical and chemical characteristics and the occurrence, distribution, and oxidation state of inorganic arsenic in drinking water from selected domestic well-water supplies in Maine in 2001–2 and 2006–7. The data collected provide support for evaluating arsenic-removal efficiencies of household water-purification systems and provide information to State and local officials that can be used in determining a water-treatment approach for the removal of arsenic from drinking water.

The U.S. Geological Survey (USGS), in cooperation with the U.S. Centers for Disease Control and Prevention and the Maine Center for Disease Control and Prevention, assessed the physical and chemical characteristics and the occurrence, distribution, and oxidation state of inorganic arsenic in drinking water from selected domestic well-water supplies in Maine in 2001–2 and 2006–7. The data collected provide support for evaluating arsenic-removal efficiencies of household water-purification systems and provide information to State and local officials that can be used in determining a water-treatment approach for the removal of arsenic from drinking water.

A method for the identification and determination of arsenosugars in the extract of kelp

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.