Effects of Organic Matter, Iron (Hydr)oxides, and Iron Reductive Dissolution on Per- and Polyfluoroalkyl Substances Sorption to Aqueous Film-Forming Foam-Impacted Solids
Bibliographic record
Abstract
Aqueous film-forming foams (AFFFs) are a major source of per- and polyfluoroalkyl substances (PFAS) contamination in groundwater and soil. Although PFAS sorption is known to depend on the properties of solid matrices, the roles of organic matter and iron (hydr)oxides remain poorly understood, especially for polyfluorinated compounds. To investigate the interplay between particulate organic matter and iron (hydr)oxides in PFAS-contaminated subsurface environments, we studied the partitioning of neutral, cationic, and anionic PFAS in AFFF to well-characterized solids using the mixed-mode solid-phase extraction (SPE) technique combined with the total oxidizable precursor (TOP) assay. Our results indicated that organic matter consistently enhanced PFAS sorption. In contrast, the effect of goethite, a representative iron-containing mineral, on PFAS sorption varied with the PFAS charge. Using three different AFFFs, we found that anionic PFAS exhibited strong sorption regardless of the organic matter content, while cationic and zwitterionic PFAS sorbed poorly. These trends were more pronounced for iron-coated sand, which showed a higher affinity for anionic PFAS. Treatment with ascorbate, a mild reductant, released PFAS associated with iron (hydr)oxides. These findings highlight the importance of iron (hydr)oxides in the retention of anionic PFAS and could be leveraged to manage PFAS mobility through subsurface iron amendments or in situ iron (hydr)oxide formation/dissolution.
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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) | 0.001 | 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".