Comparison of Annular Diffusion Denuder and High Volume Air Samplers for Measuring Per- and Polyfluoroalkyl Substances in the Atmosphere
Bibliographic record
Abstract
Overestimation of the particle phase concentration collected on glass-fiber filters (GFFs) has been reported for perfluoroalkyl carboxylic acids (PFCAs) using conventional high volume air samplers. In this study, per- and polyfluoroalkyl substances (PFASs) were determined in the gas and particulate phases using colocated annular diffusion denuder and high volume air samplers at a semiurban site in Toronto, Canada, in winter 2010. Samples were analyzed for 7 PFAS classes (i.e., PFCAs, perfluoro-alkane sulfonic acids (PFSAs), fluorotelomer alcohols (FTOHs), fluorotelomer methacrylates (FTMACs), fluorotelomer acrylates (FTACs), perfluorooctane sulfonamides (FOSAs), and perfluorooctane sulfonamidoethanols (FOSEs)). The gas diffusion coefficients for individual PFASs were calculated and the denuder performance was evaluated. Modeled subcooled liquid vapor pressures (p(L)) correlated well with the vapor phase breakthrough for the denuder and high volume air systems. Total air concentrations for PFASs measured using annular diffusion denuders and high volume samplers were in agreement within a factor of 4; however, much greater differences were observed for measurements of gas-particle partitioning. Vapor phase PFSAs and PFCAs can adsorb to the GFF using high volume air samplers, resulting in much higher particle-associated fractions for these chemicals compared to the annular diffusion denuder sampler. This effect was not observed for the FTOHs, FTMACs, FTACs, FOSAs, and FOSEs. Thus, for investigations of gas-particle partitioning of PFSAs and PFCAs, the diffusion denuder sampler is the preferred method. The results of this study improve our understanding of the gas-particle partitioning of PFASs, which is important for modeling their long-range transport in air.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".