A Flow-Through Sampler for Semivolatile Organic Compounds in Air
Bibliographic record
Abstract
A widely acknowledged limitation of current passive air sampling designs for semivolatile organic chemicals is their relatively low sampling rate, severely constraining the temporal resolution that can be achieved. Addressing the need for an improved sampling design which achieves significantly faster uptake while maintaining the capability of providing quantitative information, a new sampler has been developed that provides greatly increased sampling rates by forcing the wind to blow through the sampling medium. The sampler consists of a horizontally oriented, aerodynamically shaped, stainless steel flow tube mounted on a post with ball bearings, which turns into the wind with the help of vanes. A series of polyurethane foam (PUF) discs with relatively large porosity mounted inside the flow tube serve as the sampling medium. The sampled air volume is calculated from wind speed, which is measured outside the sampler and after passage through the sampling medium using precalibrated vortex rotor and turbine anemometers mounted on top of the sampler and at the exit of the flow tube, respectively. Small battery-operated data loggers are used for data storage. Under typical wind speed conditions, the sampler can collect 100 m(3)/ day, which is approaching the sampling rates of conventional high volume samplers. Controlled experiments in the laboratory and frontal chromatography theory yield the theoretical plate number and breakthrough volumes for polychlorinated biphenyls and polycyclic aromatic hydrocarbons in the PUF plugs and allow for the estimation of breakthrough levels for relatively volatile organic chemicals. After correction for breakthrough, the air concentration obtained with the new flow-through sampler are independent of sampling length and volume and compare favorably with those obtained from conventional pumped high volume samples.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".