An interdisciplinary team of researchers has developed a machine learning framework that uses limited water quality samples to predict which inorganic pollutants are likely to be present in a groundwater supply.
An interdisciplinary team of researchers has developed a machine learning framework that uses limited water quality samples to predict which inorganic pollutants are likely to be present in a groundwater supply. The new tool allows regulators and public health authorities to prioritize specific aquifers for water quality testing.
This proof-of-concept work focused on Arizona and North Carolina but could be applied to fill critical gaps in groundwater quality in any region.
Groundwater is a source of drinking water for millions and often contains pollutants that pose health risks. However, many regions lack complete groundwater quality datasets.
“Monitoring water quality is time-consuming and expensive, and the more pollutants you test for, the more time-consuming and expensive it is,” says Yaroslava Yingling, co-corresponding author of a paper describing the work and Kobe Steel Distinguished Professor of Materials Science and Engineering at North Carolina State University.
Read more at North Carolina State University
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