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Research Briefs: North Carolina State University

Superfund Research Program

Center for Environmental and Health Effects of PFAS

Center Director: Carolyn J. Mattingly
Grant Number: P42ES031009
Funding Period: 2020-2025
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Research Briefs

  • 362 - Machine Learning Predicts Efficiency of Micropollutant Removal -- Knappe
    Release Date: 02/19/2025

    Scientists at the NIEHS-funded North Carolina State University Superfund Research Program (SRP) Center created machine learning models that can help predict how well granular activated carbon (GAC) can clean up contaminated water. With his student Yoko Koyama, Detlef Knappe, Ph.D., developed models that consider properties of the micropollutants — such as PFAS and volatile organic compounds — specific characteristics of the water being treated, and features of different GAC types.

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Last Reviewed: April 09, 2025