Machine Learning Models for PFAS Tracking, Detection and Remediation: A Review

Abstract

Per- and polyfluoroalkyl substances (PFAS) are known for their persistence, toxicity, and potential to cause harm to human health and the environment. Traditional monitoring methods are often expensive and time-consuming. The paper provides a review of existing machine learning (ML) models for PFAS detection and treatment processes. The paper also highlights a ML workflow process for PFAS detection, remediation technologies, and the need for unified open-source database for PFAS assessment in water.