Modeling the Uptake of Semivolatile Organic Compounds by Passive Air Samplers: Importance of Mass Transfer Processes within the Porous Sampling Media
Bibliographic record
Abstract
Air sampling based on diffusion of target molecules from the atmospheric gas phase to passive sampling media (PSMs) is currently modeled using the two-film approach. Originally developed to describe chemical exchange between air and water, it assumes a uniform chemical distribution in the bulk phases on either side of the interfacial films. Although such an assumption may be satisfied when modeling uptake in PSMs in which chemicals have high mobility, its validity is questionable for PSMs such as polyurethane foam disks and XAD-resin packed mesh cylinders. Mass transfer of chemicals through the PSMs may be subject to a large resistance because of the low mass fraction of gas-phase chemicals in the pores, where diffusion occurs. Here we present a model that does not assume that chemicals distribute uniformly in the PSMs. It describes the sequential diffusion of vapors through a stagnant air-side boundary layer and the PSM pores, and the reversible sorption onto the PSM. Sensitivity analyses reveal the potential influence of the latter two processes on passive sampling rates (PSRs) unless the air-side boundary layer is assumed to be extremely thick (i.e., representative of negligible wind speeds). The model also reveals that the temperature dependence of PSRs, differences in PSRs between different compounds, and a two-stage uptake, all observed in field calibrations, can be attributed to those mass transfer processes within the PSM. The kinetics of chemical sorption to the PSM from the gas phase in the macro-pores is a knowledge gap that needs to be addressed before the model can be applied to specific compounds.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".