Assessment of the Influence of Soil Characteristics and Hydrocarbon Fuel Cocontamination on the Solvent Extraction of Perfluoroalkyl and Polyfluoroalkyl Substances
Bibliographic record
Abstract
Sites impacted by the use of aqueous film-forming foams (AFFFs) present elevated concentrations of perfluoroalkyl and polyfluoroalkyl substances (PFAS). The characterization of the PFAS contamination at such sites may be greatly complicated by the presence of hydrocarbon cocontaminants and by the large variety of PFAS potentially present in AFFFs. In order to further a more comprehensive characterization of AFFF-contaminated soils, the solvent extraction of PFAS from soil was studied under different conditions. Specifically, the impact of soil properties (textural class, organic matter content) and the presence of hydrocarbon contamination (supplemented in the form of either diesel or crude oil) on PFAS recovery performance was evaluated for two extraction methods [methanol/sodium hydroxide (MeOH/NaOH) and methanol/ammonium hydroxide (MeOH/NH 4 OH)]. While both methods performed satisfactorily for perfluoroalkyl acids and fluorotelomer sulfonates, the extraction of newly identified surfactants with functionalities such as betaine and quaternary ammonium was improved with the MeOH/NaOH based method. The main factors that were found to influence the extraction efficiency were the soil properties; a high organic matter or clay content was observed to negatively affect the recovery of the newly identified compounds. While the MeOH/NaOH solvent yielded more efficient recovery rates overall, it also entailed the disadvantage of presenting higher detection limits and substantial matrix effects at the instrumental analysis stage, requiring matrix-matched calibration curves. The results discussed herein bear important implications for a more comprehensive and reliable environmental monitoring of PFAS components at AFFF-impacted sites.
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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 | 0.001 |
| 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) | 0.000 | 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".