Exploring the role of the sampler housing in limiting uptake of semivolatile organic compounds in passive air samplers
Bibliographic record
Abstract
Passive air samplers (PASs) are simple, versatile devices that are increasingly used to determine the concentrations of semivolatile organic compounds (SVOCs) in the atmosphere. Using PAS and interpreting PAS-derived data with confidence requires a detailed understanding of the factors that control the uptake kinetics. A number of experiments were aimed at clarifying the role that the housing has in limiting the uptake of SVOCs in a PAS. Specifically, we quantified the gradient in the amount of polychlorinated biphenyls (PCBs) accumulated in XAD-filled mesh cylinders with increasing distance from the PAS housing's opening. That gradient was non-existent in an artificially ventilated housing (i.e. different segments of a cylinder contained the same amount of PCBs), minor during outdoor deployments (i.e. the bottom third of the cylinder sampled approximately 20% more PCBs than the top third), and strong during indoor deployments (i.e. the bottom third of the cylinder sampled twice the amount sampled by the top third). This is consistent with the thickness of the air boundary layer surrounding the XAD-resin increasing with increasing distance from the housing's opening and decreasing with increasing air turbulence. An experiment with housings absorbing different amounts of sunlight revealed that heat-induced convection has a minor effect on the gradient within the mesh cylinder and on the total amount of accumulated PCB. Similarly, this gradient and the total amount sorbed was also not influenced by the number of XAD-filled mesh cylinders placed within a housing as long as they were deployed outdoors. However, if four mesh cylinders were placed in one housing in a calm indoor setting, the top third of the mesh cylinders was notably starved of PCBs, suggestive of an air concentration gradient within the sampler housing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".