Long-term hydraulic and containment response of geosynthetic clay liners to PFAS-impacted leachates
Bibliographic record
Abstract
This paper explores the efficacy of individual geosynthetic clay liner (GCL) components and an activated carbon-amended GCL (ACA-GCL) in attenuating per- and polyfluoroalkyl substances (PFAS) from leachates obtained from two Australian landfills, denoted herein as leachates A and B. Batch sorption tests were performed on GCLs and their components to evaluate their sorptive affinity for 20 environmentally significant PFAS. Long-term hydraulic conductivity tests were also performed on activated carbon-amended GCLs subjected to leachates for 660 to 1111 days. Batch test results indicated that PFAS sorption increases with longer carbon chain lengths, likely attributed to enhanced hydrophobic interactions. Conversely, all sorbents exhibited minimal to negligible sorption of short-chain PFAS. Hydraulic conductivity tests yielded values of k = 7.6 × 10 −11 m s⁻¹ and k = 6.2 × 10 −11 m s⁻¹ for activated carbon-amended GCL specimens permeated by leachates A and B, respectively, indicating a moderate increase compared to conventional GCLs. Furthermore, sampling of hydraulic conductivity effluents at various intervals throughout the testing period demonstrated that the activated carbon-amended GCL specimens significantly retarded the breakthrough of 28 target PFAS compared to conventional GCLs. PFAS breakthrough time varied depending on the terminal head group and the carbon chain length, with longer perfluorocarbon chains exhibiting extended breakthrough times. Overall, the results suggest that while activated carbon-amended GCLs can effectively retard the migration of long-chain PFAS until their sorptive capacity is reached, they are less effective in addressing the migration of short-chain compounds, which presents a significant challenge. To prevent PFAS migration over time, robust landfill liner designs may be required, such as those incorporating materials capable of attenuating short-chain PFAS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".