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Project #5276

PFAS Fingerprinting for Source Identification Using Machine Learning

$312,453
In Progress
Principal Investigator
Anna
Schroeder
Research Manager
Ms. Mary Smith
Contractor
South Platte Renew
Per- and Polyfluoroalkyl Substances (PFAS)
Monitoring
Innovation

Abstract

PFAS is widely used in both industrial and household products, making it present in many water reclamation facility influents. While treatment of PFAS is costly, source identification and control can reduce PFAS loads to treatment facilities. The goal of this research is to identify methods that could be used in the future for PFAS source identification and, eventually, source apportionment using statistical models and machine learning to support and optimize monitoring requirements.