Field Calibration of Rapidly Equilibrating Thin-Film Passive Air Samplers and Their Potential Application for Low-Volume Air Sampling Studies
Bibliographic record
Abstract
This paper reports on a field calibration and ambient deployment study with rapidly equilibrating thin-film passive air samplers. POlymer-coated Glass (POG) samplers have a coating of ethylene vinyl acetate (EVA) less than 1 microm thick coated on to glass, which can be dissolved off after exposure and prepared for quantification of persistent organic pollutants (POPs) that have partitioned into the film during field exposure. In this study, POGs were exposed for up to 18 d, in a study to assess compound uptake rates and their time to approach equilibrium. Results confirmed theoretical predictions, with time to equilibrium varying between a few hours to ca. 20 d for PCB-18 and PCB-138, respectively. Performance reference compounds and contaminated POGs were used to investigate depuration kinetics, confirming that lighter congeners behave extremely dynamically with substantial losses from the films over periods of a few hours. Repeated deployments of the samplers for different3-d periods yielded detectable levels of a range of PCB congeners, which had partitioned from as little as approximately 2 to 10 m3 air. This highlights the potential utility of POGs for extremely sensitive and dynamic passive air sampling in the future to help improve understanding of sources, environmental fate, and behavior of POPs. Recommendations are made for future improvements/refinements in POG sampling and handling procedures.
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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 | 0.004 | 0.008 |
| 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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".