Air concentrations and particle–gas partitioning of polyfluoroalkyl compounds at a wastewater treatment plant
Bibliographic record
Abstract
Environmental context Polyfluoroalkyl compounds, widely used chemicals in consumer and industrial products, are global pollutants in the environment. Transport mechanisms and environmental pathways of these compounds, however, are not yet fully understood. We show that a wastewater treatment plant can be an important source for polyfluoroalkyl compounds to the atmosphere where they have the potential to be transported long distances. Abstract An air sampling campaign was conducted at a wastewater treatment plant (WWTP) to investigate air concentrations and particle–gas partitioning of polyfluoroalkyl compounds (PFCs). Samples were collected at an aeration tank and a secondary clarifier using both active high volume samplers and passive samplers comprising sorbent-impregnated polyurethane foam (SIP) disks. Water to air transport of PFCs was believed to be enhanced at the aeration tank owing to aerosol-mediated transport caused by surface turbulence induced by aeration. Mean air concentrations of target PFCs at the aeration tank were enriched relative to the secondary clarifier by factors of ~19, ~4 and ~3 for ∑fluorotelomer alcohols (FTOHs) (11 000 v. 590 pg m–3), ∑perfluorooctane sulfonamides & perfluorooctane sulfonamidoethanols (FOSAs & FOSEs) (120 v. 30 pg m–3) and ∑perfluoroalkyl carboxylates & perfluoroalkyl sulfonates (PFCAs & PFSAs) (4000 v. 1300 pg m–3) respectively. The particle associated fraction in the atmosphere increased with increasing chain length for PFCAs (from 60 to 100%) and PFSAs were predominantly bound to particles (~98%). Lower fractions on particles were found for FTOHs (~3%), FOSAs (~30%) and FOSEs (~40%). The comparison of the active and passive air sampling showed good agreement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".