Impact of dissolved organic matter chemical properties on perfluorooctane sulfonate solution binding affinities and adsorption on soils
Bibliographic record
Abstract
Abstract The fate and bioavailability of perfluorooctane sulfonate (PFOS) in soils is significantly influenced by its interactions with dissolved organic matter (DOM), which is elevated in soil solutions due to the land application of organic amendments. Evaluating the effects of DOM chemical properties on PFOS affinity and adsorption–desorption processes in soil is essential to better predict PFOS behavior in soils. We studied the interactions between PFOS and DOM from locally sourced and commercial organic amendments, including biosolids, animal manure, composts, and humic acid. Additionally, we measured PFOS adsorption on a kaolinitic Ultisol (Gwinnett) and adsorption–desorption on a smectitic Vertisol (Vaiden). PFOS affinity for DOM was strongly correlated with the humification index (HIX, r2 = 0.94), protein‐like fluorophores (C3, r2 = 0.76), and aromaticity (specific UV absorption at 254 nm [SUVA254], r2 = 0.71). The presence of 100 mg C L⁻¹ DOM from biosolids and animal waste enhanced PFOS adsorption by up to 90%, whereas DOM from plant and terrestrial sources reduced adsorption by as much as 40%. Strong correlations were observed between PFOS adsorption enhancement on Gwinnett and C3 (r2 = 0.72), SUVA254 (r2 = 0.68), and HIX (r2 = 0.62). In contrast, PFOS adsorption on Vaiden was substantially lower and less influenced by DOM, though DOM type still affected PFOS adsorption–desorption hysteresis on Vaiden. This study offers a framework for using easily measurable DOM chemical properties to predict DOM's impact on PFOS fate and behavior in soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".