Effect of Wind on the Chemical Uptake Kinetics of a Passive Air Sampler
Bibliographic record
Abstract
Passive air samplers (PASs) operate in different types of environment under various wind conditions, which may affect sampling rates and thus introduce uncertainty to PAS-derived air concentrations. To quantify the effect of wind speed and angle on the uptake in cylindrical PASs using XAD-resin as the sampling medium, we measured the uptake kinetics of polychlorinated biphenyls (PCBs) in XAD and of water in silica-gel, both under quasi wind-still condition and with lab-generated wind blowing toward the PASs at various speeds and angles. Passive sampling rates (PSRs) of PCBs under laboratory generated windy conditions were approximately 3-4 times higher than under wind-still indoor conditions. The rate of water uptake by silica-gel increased with wind speed, following a logarithmic function so that PSRs are more strongly influenced at lower wind speed. PSRs of both PCBs and water varied little with wind angle, which is consistent with computational fluid dynamic simulations showing that different angles of wind incidence cause only minor variations of air velocities within the cylindrical sampler housing. Because modifications of the design of the cylindrical PAS were not successful in eliminating the wind speed dependence of PSRs at low wind levels, indoor and outdoor deployments require different sets of PSRs. The effect of wind speed and angle on the PSRs of the cylindrical PAS are much smaller than what has been reported for the double-bowl polyurethane foam PAS. PSRs of the cylindrical XAD-PAS therefore tend to vary much less between sampling sites exposed to different wind conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".