Field validation of a novel passive air sampler and monitoring of semivolatile organic pollutants in the remote marine and continental boundary layer
Bibliographic record
Abstract
Passive air sampling allows spatially high resolved study designs and access to truly remote sites, but determination of the sampling efficiency, especially that of the particulate phase remains challenging. In this work, we present a self-directional passive air sampler which relies on the Venturi principle to enhance the sample air flow and to efficiently collect the air gas and particulate phases. Sample air flow rates ranged 1–13 m 3 d −1 depending on wind speed. The sampler was validated by side-by-side sampling with active air samplers at a central European continental site and a Caribbean coastal site. The sampler is found suitable for monitoring organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) at trace concentrations in the remote environment (mostly 0.1–1 pg m −3 concentration range). For some OCPs these levels are among the lowest concentrations in air ever reported. The vertical distribution of polycyclic aromatic compounds, OCPs and PCBs at the rural continental site is found determined by advection in the planetary boundary layer during most seasons. • Passive air sampler with enhanced sample air flow. • Vertical distribution of POPs at continental site determined by long-range transport. • Extremely low cyclodiene pesticides levels measured in marine background air.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".