A field intercomparison of three passive air samplers for gaseousmercury in ambient air
Bibliographic record
Abstract
Abstract. Passive air samplers (PASs), providing time-averaged concentration of gaseous mercury over the time scale of weeks to months, are promising to fill a gap in the monitoring of atmospheric mercury worldwide. Their usefulness will depend on their ease-of-use and robustness under field conditions, their availability and affordability, and most notably, their ability to provide results of acceptable precision and accuracy. Here we describe a comparative evaluation of three PASs with respect to their ability to record precisely and accurately atmospheric background concentrations at sites in both southern Italy and southern Ontario. The study includes the CNR-PAS with gold nanoparticles as a sorbent, developed by the Italian National Research Council, the IVL-PAS using an activated carbon-coated disk, developed by the Swedish Environmental Research Institute, and the MerPAS® using a sulfur-impregnated activated carbon sorbent, developed at the University of Toronto and commercialized by Tekran. Detection limits are deduced from the variability in the amount of mercury quantified in more than 20 field blank samples for each PAS. Analytical and sampling precision is quantified through 22 triplicated deployments for each PAS ranging in length from two to twelve weeks. Accuracy and bias are assessed through comparison with gaseous elemental mercury concentrations recorded by Tekran 2537 automated mercury analyzers operating alongside the PASs at both locations. The performance of the PASs was significantly better in Italy, with all of them providing concentrations that are not statistically significantly different from the average of the active sampling results. In Canada, where weather conditions were much harsher and more variable during the February through April deployment period, differences were observed amongst PASs. At both sites, the MerPAS® is currently the most sensitive, precise and accurate among the three PASs. A key reason for this is the larger size and the radial configuration of the MerPAS®, which results in blank levels that are lower relative to the sequestered amounts of mercury than in the other two PASs, which rely on axial diffusion geometries. Because the blank-correction becomes relatively smaller with longer deployment, sampler performance tends to be better during deployments of 8 and 12 weeks.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".