A machine learning framework to predict PPCP removal through various wastewater and water reuse treatment trains.

ABSTRACT: The persistence of pharmaceuticals and personal care products (PPCPs) through wastewater treatment and resulting contamination of aquatic environments and drinking water is a pervasive concern, necessitating means of identifying effective treatment strategies for PPCP removal. In this study, we employed machine learning (ML) models to classify 149 PPCPs based on their chemical properties and predict their removal via wastewater and water reuse treatment trains. We evaluated two distinct clustering approaches: C1 (clustering based on the most efficient individual treatment process) and C2 (clustering based on the removal pattern of PPCPs across treatments). For this, we grouped PPCPs based on their relative abundances by comparing peak areas measured via non-target profiling u

SUBMITTER: Choi JM 

PROVIDER: S-EPMC11694563 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature