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Record W4407772726 · doi:10.1002/rem.70014

In Situ Treatment‐Train Remediation of Per‐ and Polyfluoroalkyl Substance–Impacted Groundwater

2025· article· en· W4407772726 on OpenAlexaffabout
Matt Pourabadehei, David Tarnocai, Robert Timlin, Renan Orquiza, Chris McRae, Andrew Tam

Bibliographic record

VenueRemediation Journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsCanadian Armed ForcesPublic Works and Government Services Canada
Fundersnot available
KeywordsEnvironmental remediationGroundwaterGroundwater contaminationEnvironmental scienceIn situWaste managementMining engineeringGeologyEnvironmental engineeringEnvironmental chemistryGeotechnical engineeringEngineeringContaminationChemistryAquiferEcology

Abstract

fetched live from OpenAlex

ABSTRACT It has been more than a decade since per‐ and polyfluoroalkyl substances (PFAS) were listed as persistent organic pollutants (POPs) in the Stockholm Convention and, to date, PFAS are still considered emerging contaminants. Our understanding of the fate, transport, bioaccumulation, and toxicity of this group of chemicals is evolving and, subsequently, the PFAS regulations around the world are being updated on a frequent basis. To meet the current and progressively more stringent PFAS regulatory guidelines, enhanced and viable field‐deployable remediation technologies play a crucial role for mitigating risks associated with PFAS exposure, particularly for treating aqueous media. An innovative In Situ Treatment‐Train, in the form of a Permeable Reactive Barrier (“ISTT‐PRB”), was designed and a pilot‐scale ISTT‐PRB was constructed between August 2021 and March 2022. This pilot‐scale treatment system was designed to provide a proof‐of‐concept test of using a PRB to intercept and remediate PFAS‐impacted groundwater within a portion of a PFAS‐contaminated location at Canadian Forces Base Trenton in Ontario, Canada. Utilization of permeable retaining walls between reactive cells (where treatment‐train media are located) allows separate replacement of the tested treatment media (consisting of modified bentonite clay and granular activated carbon) in any single cell, or chamber, if and when the media become exhausted and reach PFAS breakthrough. Hence, PFAS can be removed from the site, even though the ISTT‐PRB is an in situ remediation system. The pilot‐scale ISTT‐PRB has successfully treated PFAS‐impacted groundwater (migrating from the source zone) during almost 2 years of operation, with up to a 99.9% PFAS removal efficiency in lag (downstream) reactive cells. Lower PFAS removal efficiency was observed after heavy precipitation events or when the ground and PRB were fully saturated (i.e., when the groundwater table was above the top of the treatment media). Saturated ground conditions may have resulted in untreated groundwater bypassing the lead (upstream) reactive cells, which adversely affected the quality of treated groundwater in the lag reactive cells, therefore adversely affecting the PFAS removal efficiencies. Despite the saturated ground conditions during certain periods of the year, the removal efficiencies in lag reactive cells remained above ~94% in the second year of operation. Construction of a full‐scale system in areas of higher hydraulic gradient and deeper water table may improve the performance of an ISTT‐PRB. After almost 2 years of operation, all three ISTT‐PRB chambers (with 2.5%. 5.0%, and 7.5% mixing ratios of treatment media with PFAS‐free fine sand) equally showed high PFAS removal efficiency. It is believed that the ISTT‐PRB pilot system can effectively intercept and treat PFAS‐impacted groundwater for years without original treatment media replacement. When the treatment media are approaching fully adsorbed conditions, the performance of each chamber with different sorbent media mixing ratios can be reassessed, and subsequently, an optimized mixing ratio can be determined.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.282
Teacher spread0.268 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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