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Machine-learning model predictions and rasters of dissolved oxygen probability, iron concentration, and redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers

Dates

Publication Date
Start Date
1960
End Date
2019

Citation

Knierim, K.J., Kingsbury, J.A., and Haugh, C.J., 2020, Machine-learning model predictions and rasters of dissolved oxygen probability, iron concentration, and redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers: U.S. Geological Survey data release, https://doi.org/10.5066/P9N108JM.

Summary

Groundwater is a vital resource in the Mississippi embayment physiographic region (Mississippi embayment) of the central United States and can be limited in some areas by high concentrations of trace elements. The concentration of trace elements in groundwater is largely driven by oxidation-reduction (redox) processes. Redox processes are a group of biotically driven reactions in which energy is derived from the exchange of electrons. In groundwater, this commonly occurs through decomposition of organic matter (carbon) by microbes, which consumes dissolved oxygen (DO). Under low DO conditions, iron (Fe), manganese, and arsenic can dissolve from coatings on aquifer sediments and be released into groundwater. Therefore, predictions of [...]

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Attached Files

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BRT_DOandFe.R 6.06 KB text/x-rsrc
ExpVars.csv 15.08 KB text/csv
README.txt 8.82 KB text/plain
modelgeoref.txt 962 Bytes text/plain

Purpose

The machine-learning model predictions and groundwater quality rasters support the Scientific Investigations Map by Knierim and others (2021).

Map

Spatial Services

ScienceBase WMS

Communities

  • USGS Data Release Products
  • USGS Lower Mississippi-Gulf Water Science Center

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Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9N108JM

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