Application Of Machine Learning To Understand Pfas Occurrence, Distribution, Transport And Removal In Water

Adewale Ajao

doi:10.20944/preprints202403.0627.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 11 March 2024 / Approved: 11 March 2024 / Online: 11 March 2024 (12:51:46 CET)
Version 2 : Received: 20 April 2024 / Approved: 22 April 2024 / Online: 23 April 2024 (08:23:35 CEST)

Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review.

PFAS, machine learning, Ground water, Environment, sustainability.

Environmental and Earth Sciences, Environmental Science

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Application Of Machine Learning To Understand Pfas Occurrence, Distribution, Transport And Removal In Water

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