Sorption of Polyfluoroalkyl Surfactants on Surface Soils: Effect of Molecular Structures, Soil Properties, and Solution Chemistry
Bibliographic record
Abstract
Zwitterionic, cationic, and anionic per- and polyfluoroalkyl substances (PFASs) are identified in aqueous film-forming foam (AFFF) concentrates and AFFF-impacted sites. However, the mobility potential of zwitterionic and cationic PFASs is poorly understood, preventing reliable site assessment. The study aimed to elucidate the mobility behaviors of PFASs of various charge states in saturated soil–water systems and assess critical influencing factors. Five anionic, three zwitterionic, and one cationic PFASs were investigated in five soils through batch sorption experiments. Pairwise comparison revealed that the quaternary ammonium group imparted a strong affinity of cationic perfluorooctaneamide ammonium compound (PFOAAmS) for soils. The influence of the quaternary ammonium group is mitigated in polyfluoroalkyl betaines, yet perfluorooctane sulfonamidoalkyl betaine (PFOSB) showed strong sorption in selected soils. Two soil bulk properties showed some correlations with the soil–water distribution coefficient ( K d ). A positive correlation with the fraction of soil organic carbon was found only for anionic PFASs, whereas cation exchange capacity had an approximate positive correlation with K d only for PFOAAmS. Water chemistry (Ca 2+ and pH) influences the sorption of nonanionic PFASs in very distinct fashions or even in opposite trends to what was known for anionic PFASs. Sorption was insensitive to pH changes except for PFOSB; PFOSB underwent profound sorption reduction because its speciation occurs around neutral pH, while the two other betaines and PFOAAmS have p K a values that are outside of the environmentally relevant range. The lack of correlations suggests that the transport potential of PFASs is probably highly site-specific. It remains a challenge in deciphering PFAS sorption mechanisms and predicting how AFFF plumes migrate.
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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 | 0.000 | 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".