A Passive Air Sampler for Precise, Spatially Distributed Atmospheric Gaseous Mercury Monitoring and Source Characterization
Bibliographic record
Abstract
This thesis describes the design, testing and implementation of a novel, inexpensive, high precision passive air sampler (PAS) for gaseous mercury (Hg) monitoring. The PAS uses a sulfur-impregnated activated carbon as a sorbent, a commercially available, radial diffusive barrier (Radiello®), and a protective shield that doubles as a storage and transport container. The diffusive barrier and protective shield also help reduce variability in uptake kinetics that can be caused by wind and precipitation. An initial outdoor calibration in Toronto revealed highly linear uptake over a one-year period and unprecedented precision. Small, but predictable effects on uptake kinetics caused by temperature and wind speed were determined in laboratory experiments. The diffusive barrier was also deemed reusable after cleaning. A study evaluating the accuracy of the PAS against industry standard active instrument at 20 global sites determined (i) the recommended sampling rate (volume of air stripped of gaseous Hg per unit time; 0.135 ± 0.013 m3 day-1) that can be adjusted for the measured temperature and wind speed during each deployment, (ii) the excellent precision based uncertainty (4 ± 3 %), and (iii) the average overall uncertainty is at most (9 ± 6 %); a conservative measure due to inherent active instrument uncertainties. The analytical method (thermal decomposition, amalgamation, and atomic absorption spectrometry) was improved by the addition of sodium-carbonate to samples, which prolongs the life of the analytical instrument’s catalyst. Two source characterization studies (at a former Hg mine and across the Greater Toronto Area) demonstrate the PAS’s ability to successfully monitor concentration gradients and to estimate emissions. The results produced in this thesis are indicative of the PAS’s ability to measure at both background and high concentration sites with exceptional accuracy and precision, and its potential to improve atmospheric Hg science globally.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".