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Person

Sarah M Elliott

Hydrologist
Upper Midwest Water Science Center

Email: selliott@usgs.gov
Office Phone: 763-783-3130
ORCID: 0000-0002-1414-3024

Location
Mounds View Business Center
2280 Woodale Drive
Mounds View , MN 55112-4900
US
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Chemicals of Emerging Concern in Water and Bottom Sediment in Great Lakes Areas of Concern, 2013—Analytical Methods, Collection Methods, Environmental Data, and Quality Assurance
The U.S. Geological Survey (USGS), in cooperation with the U.S. Fish and Wildlife Service (USFWS) and the U.S. Environmental Protection Agency (EPA), identified the occurrence of contaminants of emerging concern (CECs) in water and bottom sediment collected in 2013 at 57 sites throughout the Great Lakes Basin. The 2013 effort is part of a long-term study that began in 2010. Included in this directory are references to or descriptions of analytical methods used, collection methods, environmental data, and associated quality-assurance data for samples collected in 2013. Samples were collected from April through October 2013 by USGS, USFWS, and/or EPA personnel. Study sites include tributaries to the Great Lakes...
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This data release contains input data used in model development and TIF raster files used to predict the probability of high arsenic (As) and high manganese (Mn) in groundwater within the glacial aquifer system in the northern United States. Input data include measured As and Mn concentrations at groundwater wells, and associated predictor variable data. The probability of high As and high Mn was predicted using boosted regression tree methods using the gbm package in R version 4.0.0. The response variables for individual models were the occurrence of: (1) As >10 µg/L, and (2) Mn >300 µg/L. Water-quality data were compiled from three sources, as described in Wilson and others (2019): a compilation of data from numerous...
This data release contains input data used in model development and TIF raster files used to predict the probability of low dissolved oxygen (DO) and high dissolved iron (Fe) in groundwater within the glacial aquifer system in the northern continental United States. Input data include measured DO and Fe concentrations at groundwater wells, and associated predictor variable data. The probability of low DO and high Fe was predicted using boosted regression tree methods using the gbm package in R (v. 4.0.0) in RStudio (v. 1.2.5042). The response variables for individual models were the occurrence of: (1) DO ≤0.5 mg/L, (2) DO ≤2 mg/L, and (3) Fe >100 µg/L. Water-quality data were compiled from three sources, as described...
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This data release contains: (1) ASCII grids of predicted probability of elevated arsenic in groundwater for the Northwest and Central Minnesota regions, (2) input arsenic and predictive variable data used in model development and calculation of predictions, and (3) ASCII files used to predict the probability of elevated arsenic across the two study regions. The probability of elevated arsenic was predicted using Boosted Regression Tree (BRT) modeling methods using the gbm package in R Studio version 3.4.2. The response variable was the presence or absence of arsenic >10 µg/L, the U.S. Environmental Protection Agency’s maximum contaminant level for arsenic, in 3,283 wells located throughout both study regions (1,363...
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A boosted regression tree (BRT) model was developed to predict pH conditions in three-dimensions throughout the glacial aquifer system (GLAC) of the contiguous United States using pH measurements in samples from 18,258 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and when coupled with long flow paths, results in the most alkaline conditions. Conversely, in areas where glacial sediments are thin and carbonate-poor, pH conditions remain acidic. At depths typical of drinking-water supplies, predicted pH > 7.5 – which is associated with arsenic mobilization – occurs more frequently...
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