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Groundwater arsenic data and ASCII grids for predicting elevated arsenic in northwestern and central Minnesota using boosted regression tree methods

Dates

Publication Date
Time Period
1980
Time Period
2016

Citation

Elliott, S.M., and Christenson, C.A., 2019, Groundwater arsenic data and ASCII grids for predicting elevated arsenic in northwestern and central Minnesota using boosted regression tree methods: U.S. Geological Survey data release, https://doi.org/10.5066/F77H1HH8.

Summary

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 in the Northwest [...]

Contacts

Point of Contact :
Melinda L Erickson
Originator :
Sarah M Elliott, Catherine A Christenson
Metadata Contact :
Sarah M Elliott
Publisher :
U.S. Geological Survey
Distributor :
U.S. Geological Survey - ScienceBase
USGS Mission Area :
Water Resources
SDC Data Owner :
Upper Midwest Water Science Center

Attached Files

Click on title to download individual files attached to this item.

MN_As_BRT.zip
“Model archive zipped file”
38.73 MB
MNAs_BRT_metadata.xml
Original FGDC Metadata

View
46.83 KB

Purpose

Arsenic is a naturally-occurring contaminant in geologically diverse aquifers throughout the world, making chronic exposure to elevated arsenic via drinking water a human health concern. In Minnesota, USA, elevated arsenic concentrations are prevalent in drinking water aquifers in certain regions of the state. This data set was used to develop a model to predict the probability of elevated arsenic within two vulnerable regions in Minnesota. Results can help well drillers and homeowners identify important variables that may be controlled during well construction to minimize exposure to arsenic.

Additional Information

Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/F77H1HH8

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