Rapidly Equilibrating Micrometer Film Sampler for Priority Pollutants in Air
Bibliographic record
Abstract
Modified polymer-coated glass samplers (POGs), termed EVA samplers, consist of micrometer-thin layers of ethylene vinyl acetate (EVA) coated onto a glass fiber filter or aluminum foil substrate. These samplers were designed to equilibrate rapidly with priority pollutants in air, making them ideal for short-term spatial studies in ambient or indoor air. The EVA sampler was calibrated by measuring the uptake of polychlorinated biphenyls (PCBs) over 8 weeks in an indoor environment, and four different film thicknesses were monitored that ranged from 0.1 to 30 μm. The results were used to calculate the average mass transfer coefficient (50.5 m/day) and generate contour maps that provide guidance in choosing an appropriate EVA sampler for a particular study based on film thickness, deployment time, and the log K(OA) of the anlayte. A range of air pollutant classes was also added to the EVA sampler prior to deployment to assess depuration rates. These included polychlorinated biphenyls (PCBs), current-use pesticides (CUPs), perfluorinated compounds (PFCs), and polybrominated diphenyl ethers (PBDEs). On the basis of the depuration profiles, the EVA sampler was a suitable equilibrium sampler for several CUPs and PCBs; however, for the high molecular weight PCBs and PBDEs, the EVA sampler operates as a linear uptake sampler. Samplers were also evaluated for their use as a rapid screening tool for assessing concentrations of siloxanes in indoor air. The EVA sampler was used to estimate air concentrations for D4 and D5 in laboratory air to be 118 and 89 ng/m(3), respectively. Analyses were performed directly using thermal desorption gas chromatography/mass spectrometry (TDS-GC-MS). EVA samplers show promise due to their relatively low cost and ease of deployment and applicability to a wide range of priority chemicals. The ability to alter the film thickness, and hence the sorption capacity and performance of the EVA sampler, allows for a versatile sampler that can be used under varying sampling conditions and deployment times.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".