Long-chain perfluoro carboxylic acids in landfill leachate: Extraction, detection and biodegradation
Bibliographic record
Abstract
Landfills serve as major repositories for products containing per- and polyfluoroalkyl substances (PFASs). These compounds have been documented in the resulting leachate, posing a significant threat to both surface water and groundwater quality. Long-chain perfluoro carboxylic acids (LC-PFCAs), which act as precursors to shorter-chain PFCAs, are particularly persistent in the environment. Despite this, data on LC-PFCA's occurrence in landfill leachate is limited, highlighting the need for thorough monitoring and control efforts. This study focuses on the extraction of LC-PFCA using solid-phase extraction (SPE), detection by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and their biodegradation using an autochthonous bacterial community of landfill leachate based on the hypothesis that native bacteria can consume these pollutants. The findings of the study indicate that SPE efficiency and PFCA recovery rates were higher in diluted leachate samples. LC-MS/MS exhibited superior sensitivity and accuracy compared to direct MS injection, with lower detection limits. Throughout the sampling period, perfluoro nonanoic acid (PFNA) and perfluoro decanoic acid (PFDA), were detected in concentrations ranging from 30 to 640 ng/L and 40–510 ng/L, respectively. Further, the native microbial community degraded spiked PFNA and PFDA (1, 10, and 100 mg/L), with efficiencies of 35.26 ± 5.47% and 53.38 ± 6.54%, respectively, and Aeromonas, Proteus, Moheibacter, Pseudochrobactrum, Providencia and Pseudomonas were identified as the most promising genus to degrade LC-PFCAs. Overall, these findings of the study highlight the significance of robust analytical methods for LC-PFCA detection and reveals the promising prospects for PFNA and PFDA biodegradation by pre-existing bacterial communities. • PFNA and PFDA were detected in landfill at concentrations of 30–640 ng/L and 40–510 ng/L, respectively. • SPE recovery of 82%–110% was achieved for PFNA and PFDA from landfill leachate. • Native landfill community degraded 35.26 ± 5.47% and 53.38 ± 6.54% of PFNA and PFDA, respectively. • Aeromonas, Proteus, and Moheibacter, among others were identified as the most promising genus to degrade LC-PFCAs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".