Field Testing a Flow-Through Sampler for Semivolatile Organic Compounds in Air
Bibliographic record
Abstract
Even without access to the electrical grid, a flow-through sampler (FTS) can collect gaseous and particle-bound semivolatile organic compounds (SOCs) from large volumes of air by turning into the wind and having the wind blow through a porous sampling medium. To test its performance under field conditions, a FTS and a traditional pumped high volume air sampler, both using polyurethane foam (PUF) as sampling medium, were codeployed at the campus of the University of Toronto Scarborough from August 2006 to June 2007. Quantitative relationships between the wind speed outside the sampler and after passage through the PUF were established and allow the accurate estimation of sampling volumes under conditions of low and high wind speed. Polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) were quantified in the samples taken by both air samplers. Separate analysis of seven PUF disks arranged sequentially within the FTS, confirm that even relatively volatile SOCs do not experience serious break-through. Theoretical plate number analysis of the break-through curves yields an understanding of the effect of temperature and wind speed on FTS sampling efficiency, and reveals different behavior of gaseous and particle-bound-compounds on the PUF. Air concentrations of PCBs and PAHs obtained with the FTS compare favorably with those obtained by averaging the concentrations of several 24 h active high volume samples taken during the same time period.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".