Influence of Sampler Configuration on the Uptake Kinetics of a Passive Air Sampler
Bibliographic record
Abstract
Passive air samplers (PAS) are simple and cost-effective tools to monitor semivolatile organic compounds in air. Chemical uptake occurs by molecular diffusion from ambient air to a passive sampling medium (PSM). Previous calibration studies indicate that even for the same type of PAS, passive air sampling rates (R, m(3)(air)/d) can be highly variable due to the influence of a number of factors. Earlier studies mainly focused on factors (e.g., wind speed and temperature) influencing R via the kinetic resistance posed by the air boundary layer surrounding the PSM because that layer was deemed to be the main factor determining the uptake kinetics. Whereas recent calibration studies suggest that the PAS configuration can influence R, so far few studies have specifically focused on this factor. In this study, with the objective to understand the effect of PAS configurations on R, we applied a gravimetrical approach to study the kinetics of water vapor uptake from indoor air by silica gel placed inside cylindrical PAS of various configurations. We also conducted an indoor calibration for polychlorinated biphenyls on the same type of PAS using XAD-resin as the PSM. R was found to be proportional to the interfacial transfer area of the PSM but not the amount of the PSM because chemicals mainly accumulated in the outer layer of the PSM during the deployment time of the PAS. The sampler housing and the PSM can introduce kinetic resistance to chemical uptake as indicated by changes in R caused by positioning the PSM at different distances from the opening of the sampler housing and by using PSM of different diameters. Information gained from this study is useful for optimizing the PAS design with the objective to reduce the material and shipping costs without sacrificing sampling efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".