Newly published research from Michigan State University scientists demonstrates how regionally specific machine learning-based modeling more effectively monitors levels of per- and polyfluoroalkyl, or PFAS, chemicals in Michigan drinking water compared to nationwide models.
Findings were published in the journal Water Research.
The project was led by A. Pouyan Nejadhashemi, MSU Foundation Professor in the departments of Biosystems and Agricultural Engineering, and Plant, Soil and Microbial Sciences. Other participating researchers in Nejadhashemi’s laboratory were Nicolas Fernandez, who recently joined the University of Florida as a postdoctoral associate, and Christian Loveall.
PFAS, a class of thousands of chemicals, has been a topic of increasing concern over the last several years. Toxicological studies have shown links to numerous human health problems, such as cancers and disorders of the endocrine system, which controls hormone regulation, neurological development and other essential processes.
Nejadhashemi, an expert in modeling and ecohydrology — the interaction between water and ecological systems — is advancing understanding of how PFAS moves through the environment and what can be done to lessen its harmful effects.
The team utilized a branch of artificial intelligence known as machine learning that is designed to use data and algorithms to mimic the way humans learn, improving accuracy over time.
“The Michigan Department of Environment, Great Lakes, and Energy estimates that over 1.5 million residents have consumed PFAS-contaminated water, and more than 11,300 sites are suspected to be contaminated with PFAS,” said Nejadhashemi, a faculty member in the MSU Center for PFAS Research and with MSU AgBioResearch. “However, the silver lining lies in a newly developed PFAS predictive model that offers promise in determining regional PFAS concentrations and presence.”
To read more, visit the AgBioResearch website.
