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Record W4415461890 · doi:10.1016/j.hazadv.2025.100920

Per- and poly-fluoroalkyl substances (PFAS) in water matrices impacted by industrial activities: Challenges and treatment strategies

2025· article· en· W4415461890 on OpenAlex
Jianfei Chen, Seyed Hesam‐Aldin Samaei, Lin Zhang, Ying Zhang, Onita D. Basu, Yang Yang, Jinkai Xue

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Hazardous Materials Advances · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsCarleton UniversityUniversity of Regina
FundersCity of ReginaFaculty of Graduate Studies and Research, University of AlbertaNatural Sciences and Engineering Research Council of CanadaMitacsUniversity of Regina
KeywordsWater pollutionWater treatmentWork (physics)Water supply

Abstract

fetched live from OpenAlex

Per- and polyfluoroalkyl substances (PFAS) are increasingly recognized for their adverse effects on the environment and human health. Here, we offer a comprehensive review of PFAS existing in water matrices affected by industrial activities, delving into their characterization, human exposure pathways, toxicological effects, the evolving landscape of regulatory measures, and potential treatment strategies. Current adsorption, oxidation, and membrane-based techniques exhibit critical limitations, including inadequate removal efficiency for emerging/short-chain PFAS, persistent toxic byproduct generation, and prohibitive operational costs at scale. Thus, a critical aspect of this review is the exploration of advanced treatment techniques for PFAS-laden waters affected by industrial activities and the use of commercial products. We systematically examine an array of treatment technologies, including carbon-based adsorbents, biopolymers, anion exchange resins, foam fractionation, membrane-based separation systems, and innovative on-site destructive methods, such as electrochemical oxidation, photolysis, and sonolysis. Additionally, this review covers the latest advancements in piezocatalysis and micro-/nanobubble technologies. Standalone treatment processes are usually insufficient for complex PFAS mixtures, as no single technology can sustainably and cost-effectively remove both short- and long-chain PFAS. We, therefore, highlight the increased efficiency, both technical and economic, of certain combined or integrated treatment processes in removing a broad group of PFAS. By consolidating current findings and identifying key research gaps, this review provides practical guidance for developing cost-effective, scalable, and sustainable PFAS treatment or remediation strategies. Future work should advance scalable PFAS treatment technologies, deepen mechanistic understandings of branched and ether-PFAS degradation, and address long-term monitoring and regulatory alignment for commercial implementation, alongside non-targeted analysis and ecotoxicity assessment of transformation products.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.277
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it