The effects of meteorological parameters and diffusive barrier reuse on the sampling rate of a passive air sampler for gaseous mercury
Bibliographic record
Abstract
Abstract. Passive air sampling of gaseous mercury (Hg) requires a high level of accuracy to discriminate small differences in atmospheric concentrations. Meteorological parameters have the potential to decrease this accuracy by impacting the sampling rate (SR), i.e., the volume of air that is effectively stripped of gaseous mercury per unit of time. We measured the SR of a recently calibrated passive air sampler for gaseous Hg in the laboratory under varying wind speeds (wind still to 6 m s−1), temperatures (−15 to +35 °C), and relative humidities (44 to 80 %). While relative humidity has no impact on SR, SR increases slightly with both wind speed (0.003 m3 day−1 increase in SR or 2.5 % of the previously calibrated SR for every m s−1 increase for wind speeds > 1 m s−1, typical of outdoor deployments) and temperature (0.001 m3 day−1 increase in SR or 0.7 % for every 1 °C increase). The temperature dependence can be fully explained by the effect of temperature on the molecular diffusivity of gaseous mercury in air. Although these effects are relatively small, accuracy can be improved by adjusting SRs using measured or estimated temperature and wind speed data at or near sampling sites. We also assessed the possibility of reusing Radiello® diffusive barriers previously used in the passive air samplers. The mean rate of gaseous Hg uptake was not significantly different between new and previously used diffusive barriers in both lab and outdoor deployments, irrespective of the applied cleaning procedure. No memory effect from Radiellos® previously deployed in a high Hg atmosphere was observed. However, a loss in replicate precision for the dirtiest Radiellos® in the indoor experiment suggests that cleaning is advisable prior to reuse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".