Field Calibration of XAD-Based Passive Air Sampler on the Tibetan Plateau: Wind Influence and Configuration Improvement
Bibliographic record
Abstract
The passive air sampler based on XAD-2 resin (XAD-PAS) has proven useful for collecting atmospheric persistent organic pollutants (POPs) in remote regions. Whereas laboratory studies have shown that, due to the open bottom of its housing, the passive sampling rate (PSR) of the XAD-PAS is susceptible to wind and other processes causing air turbulence, the sampler has not been calibrated in the field at sites experiencing high winds. In this study, the PSRs of the XAD-PAS were calibrated at three sites on the Tibetan Plateau, covering a wide range in temperature ( T ), pressure ( P ) and wind speed ( v ). At sites with low wind speeds (i.e., in a forest and an urban site), the PSRs are proportional to the ratio T 1.75 / P; at windy sites with an average wind speed above 3 m/s, the influence of v on PSRs cannot be ignored. Moreover, the open bottom of the XAD-PAS housing causes the PSRs to be influenced by wind angle and air turbulence caused by sloped terrain. Field calibration, wind speed measurements, and computational fluid dynamics (CFD) simulations indicate that a modified design incorporating an air spoiler consisting of 4 metal sheets dampens the turbulence caused by wind angle and sloped terrain and caps the PSR at ∼5 m 3 /day, irrespective of ambient wind. Therefore, the original XAD-PAS with an open bottom is suitable for deployment in urban areas and other less windy places, the modified design is preferable in mountain regions and other places where air circulation is complicated and strong.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".