Smart water, smart models: algorithmic assessment of water quality under evolving chemical and industrial stressors

Environmental Science Water Research & Technology 2026 Open access

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Authors: Samuel Amankwaa, Jaan Prokofiev, Juri Belikov, Varsha Srivastava, Angela Ivask, Alexandra Nefedova, Bo Zhou, Mengxi Liu, Svetlana Polivtseva
DOI: 10.1039/d6ew00223d
Cited by: 0
References: 196

Abstract

Machine learning integrates routine monitoring, targeted PFAS and microplastic analyses, and hydrometeorological data to prioritize hotspots and guide predict-sample-verify cycles, enabling more focused interventions in stressed water systems.

Fields of Study & Subjects

Per- and polyfluoroalkyl substances research Effects and risks of endocrine disrupting chemicals Chemistry and Chemical Engineering

Publication details

Journal / source
Environmental Science Water Research & Technology
Volume
12
Issue
8
Pages
2395–2415
Publication date
1 January 2026
Language
EN