Improved Characterization of Gas–Particle Partitioning for Per- and Polyfluoroalkyl Substances in the Atmosphere Using Annular Diffusion Denuder Samplers
Bibliographic record
Abstract
Gas-phase perfluoroalkyl carboxylic acids (PFCAs) sorb strongly on filter material (i.e., GFF, QFF) used in conventional high volume air samplers, which results in an overestimation of the particle-phase concentration. In this study, we investigated an improved technique for measuring the gas-particle partitioning of per- and polyfluoroalkyl substances (PFASs) using an annular diffusion denuder sampler. Samples were analyzed for 7 PFAS classes [i.e., PFCAs, perfluoroalkane sulfonic acids (PFSAs), fluorotelomer alcohols (FTOHs), fluorotelomer methacrylates (FTMACs), fluorotelomer acrylates (FTACs), perfluorooctane sulfonamides (FOSAs), and perfluorooctane sulfonamidoethanols (FOSEs)]. The measured particulate associated fraction (Φ') using the diffusion denuder sampler generally followed the trend FTACs (0%) < FTOHs (~8%) < FOSAs (~21%) < PFSAs (~29%) < FOSEs (~66%), whereas the Φ' of the C(8)-C(18) PFCAs increased with carbon chain length, and ranged from 6% to 100%. The ionizability of some PFASs, when associated with particles, is an important consideration when calculating the gas-particle partitioning coefficient as both ionic and neutral forms can be present in the particles. Here we differentiate between a gas-particle partitioning coefficient for neutral species, K(p), and one that accounts for both ionic and neutral species of a compound, K(p)'. The measured K(p)' for PFSAs and PFCAs was 4-5 log units higher compared to the interpolated K(p) for the neutral form only. The measured K(p)' can be corrected (to apply to the neutral form only) with knowledge of the pK(a) of the chemical and the pH of the condensed medium ("wet" particle or aqueous aerosol). The denuder-based sampling of PFASs has yielded a robust data set that demonstrates the importance of atmospheric pH and chemical pK(a) values in determining gas-particle partitioning of PFASs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".