Organic contaminants of emerging concern in leachate of historic municipal landfills
Bibliographic record
Abstract
Many types of contaminants of emerging concern (CECs), including per- and poly-fluoroalkyl substances (PFAS), have been found in leachate of operating municipal landfills. However, there is only limited information on CECs presence in leachate of historic landfills (≥3 decades since closure, often lacking engineered liners or leachate collection systems) at concentrations that may pose a risk to nearby wells and surface water ecosystems. In this study, 48 samples of leachate-impacted groundwater were collected from 20 historic landfills in Ontario, Canada. The CECs measured included artificial sweeteners (ASs), PFAS, organophosphate esters (OPE), pharmaceuticals, bisphenols, sulfamic acid, perchlorate, and substituted phenols. The common presence of the AS saccharin, a known indicator of old landfill leachate, combined with mostly negligible levels of the AS acesulfame, an indicator of modern wastewater, revealed that most samples were strongly influenced by leachate and not cross-contaminated by wastewater (which can contain these same CECs). Several landfills, including ones closed in the 1960s, had total PFAS concentrations similar to those previously measured at modern landfills, with a maximum observed here of 12.7 μg/L. Notably elevated concentrations of several OPE, sulfamic acid, cotinine, and bisphenols A and S were found at many 30-60 year-old landfills. There was little indication of declining concentrations with landfill age, suggesting historic landfills can be long-term sources of CECs to groundwater and that certain CECs may be useful tracers for historic landfill leachate. These findings provide guidance on which CECs may require monitoring at historic landfill sites and wastewater treatment plants receiving their effluent.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".