Applying Passive Air Sampling and Isotopic Characterization to Assess Spatial Variability of Gaseous Elemental Mercury Across Ontario, Canada
Bibliographic record
Abstract
Abstract This study deployed 59 passive air samplers (PAS) across the province of Ontario, Canada (and eight additional deployments in bordering states of northern USA) to assess the influence of local/regional sources, temporal differences, and possible transformations in the atmospheric gaseous elemental mercury (GEM) pool. The spatial mapping achieved with the PASs allowed for the observation of differences in GEM and its isotopic composition over the region. Concentrations of GEM were higher with more negative δ202Hg values near urban/industrial areas and suspected emission sources in southern Ontario. In northern Ontario, far from industrial influences, lower concentrations of GEM with isotopically more positive δ202Hg values were found at the boreal forest sites compared to sites within the Hudson Bay Lowlands. The differences in northern Ontario may be from greater uptake of GEM by the boreal forest removing isotopically light Hg and lowering concentrations. Lower GEM and a shift toward more positive δ202Hg were also observed during the summer relative to other times of year supporting vegetation uptake as an important sink for GEM. PASs were also deployed along a transect of increasing distance from a Hg recycling facility. The Hg emissions from the facility had unique positive Δ199Hg and Δ200Hg signatures, but GEM concentrations and isotopic compositions returned to background within 400 m of the facility. The province‐wide variations in atmospheric GEM and isotopic compositions show that both sources (e.g., urban/industrial emissions) and sinks (e.g., vegetation uptake) contribute to the spatial and temporal patterns of the residual atmosphere GEM pool.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".