Forever chemicals (PFAS) in landfill leachate: Insights into fate, transport, and treatment strategies
Bibliographic record
Abstract
Per- and poly-fluoroalkyl substances (PFAS) represent a class of persistent synthetic chemicals whose disposal in landfills creates a significant secondary source of environmental contamination via leachate. A critical challenge in leachate management is the transformation of PFAS precursors into more mobile and regulated terminal compounds, such as perfluorooctanoic acid (PFOA) and perfluoro octane sulfonate (PFOS), which complicates risk assessment and treatment. This review addresses the urgent need for a clear understanding of PFAS fate, transport, and remediation in landfill leachate by synthesizing the most recent scientific literature from 2018 to 2025. The novelty of this work lies in its critical evaluation of the concept shift from single-stage removal methods to integrated treatment trains, which couple separation technologies (e.g., foam fractionation, membrane filtration) with destructive technologies (e.g., electrochemical oxidation, supercritical water oxidation). We analyze global variations in leachate concentrations, which can exceed 200,000 ng/L, and critically assess the performance, techno-economic feasibility, and limitations of current remediation strategies. Key research gaps are identified, including the lack of pilot-scale validation for hybrid systems, the need for strong life-cycle assessments, and the ongoing challenge of removing persistent short-chain PFAS. This review provides a comprehensive framework for researchers, engineers, and policymakers to develop and implement sustainable and effective management strategies to mitigate the environmental and public health risks posed by PFAS contaminated landfill leachate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".