Per- and Polyfluoroalkyl Substances in Landfill Leachate: Patterns, Time Trends, and Sources
Bibliographic record
Abstract
Concentrations and isomer profiles for 24 per- and polyfluoroalkyl substances (PFASs) were monitored over 5 months (February-June, 2010) in municipal landfill leachate. These data were used to assess the role of perfluoroalkyl acid (PFAA) precursor degradation on changes in PFAA concentrations over time. The influence of total organic carbon, total suspended solids, pH, electrical conductivity (EC), leachate flow rates, and meteorological data (precipitation, air temperature) on leachate PFAS concentrations was also investigated. Perfluoropentanoate and perfluorohexanoate were typically the dominant PFASs in leachate, except for March-April, when concentrations of perfluorooctane sulfonate, perfluorooctanoate, and numerous PFAA-precursors (i.e., (N-alkyl) perfluorooctane sulfonamides and fluorotelomer carboxylic acids) increased by a factor of 2-10 (~4 μg/L to ~36 μg/L ΣPFASs). During this time, isomer profiles of PFOA became increasingly dominated by the linear isomer, likely from transformation of linear, telomer-manufactured precursors. While ΣPFAA-precursors accounted for up to 71% of ΣPFASs (molar basis) in leachate from this site, leachate from a second landfill displayed only low concentrations of precursors (<1% of ΣPFASs). Overall, degradation of PFAA-precursors and changes in leachate pH, EC, and 24-h precipitation were important factors controlling PFAS occurrence in leachate. Finally, 8.5-25 kg/yr (mean 16 kg/yr) of ΣPFASs was estimated to leave the landfill via leachate for subsequent treatment at a wastewater treatment plant.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".