Characterization and Quantification of Atmospheric Mercury Sources Using Passive Air Samplers
Bibliographic record
Abstract
Abstract The Minamata Convention on Mercury (Hg) requires improved atmospheric Hg monitoring and characterization of Hg sources. Here we demonstrate how a network of passive air samplers (PASs) can be used cost effectively to determine the spatial distribution of gaseous Hg and estimate atmospheric Hg emissions at contaminated sites. Gaseous Hg concentrations were mapped around a former Hg mine in the Monte Amiata district in Italy using simultaneous deployments of PASs across local and regional spatial scale grids. The concentration maps help visualize with great detail and precision the dispersal of gaseous Hg from a contaminated site, revealing even subtle effects of wind, season, and minor sources. Emissions estimated from the empirical data (80 ± 40 and 150 ± 75 kg/year for October and July, respectively) were robust to changes in the most uncertain parameters (excess Hg in the air above the mine and advection rate) and compared well to previous estimates for this and other closed Hg mines. This PAS‐based approach has a number of advantages: (i) concurrent deployments of multiple samplers constrain concentration changes to spatial variability only, (ii) time‐averaged data over longer periods negate biases related to short‐term, infrequent measurements, (iii) more spatially representative estimates of Hg distributions and emissions can be made at a fraction of the cost, and (iv) use is easy, especially in difficult terrain. Time‐averaged data across a broad area are also most pertinent for assessing chronic human exposure, especially in terms of the inhalation of Hg by workers and residents living close to contaminated sites.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".