Tracing Atmospheric Sources of Mercury through Passive Air Sampling and Isotope Characterization
Bibliographic record
Abstract
Gaseous elemental mercury (GEM) is the dominant form of mercury (Hg) in the atmosphere and is the main species responsible for Hg being globally distributed far from point sources. Tracing Hg sources in the atmosphere remains challenging, but a potential new tool to aid in this is the measurement of stable Hg isotope ratios in atmospheric samples. Mercury isotopes undergo both mass-dependent fractionation (MDF) and mass-independent fractionation (MIF) making them useful for identifying and quantifying sources and transformations of Hg. Current collection methods for isotope characterization of atmospheric GEM rely on power and instrumentation requiring technical expertise, making sampling on large spatial scales and in remote locations difficult. Here, we assess the ability of a passive air sampler (PAS), which samples GEM, to collect and preserve Hg for isotope analysis. Based on both field and laboratory experiments, this thesis demonstrates that no MIF occurs during sampling of GEM by the PAS and that there is consistent MDF offset of ≈-1.2 ‰ in δ202Hg. The PAS was then used in two reconnaissance studies on the spatial distribution of GEM concentrations and isotopes across large regions (Ontario, Canada and Madre de Dios, Peru). In both Ontario and Peru, the Hg isotopic composition of GEM collected with the PAS allowed regional and local sources to be identified and traced. In Ontario, southern regions with greater local urban/industrial emission sources and/or trans-regional air masses containing industrial sources had GEM that was isotopically distinct from northern regions, which have fewer emission point sources. In Madre de Dios, artisanal and small-scale gold mining (ASGM) activity is a main Hg source to the region and is isotopically distinct, allowing the contribution of ASGM derived GEM to be estimated. In addition to source tracing, Hg isotopes in GEM were also affected by vegetation uptake of GEM in both Ontario and Peru, which is a major removal process of GEM from the atmosphere and a major source of Hg to terrestrial ecosystems and soils. In Peru, isotope compositions in foliage and soils in Amazonian forests near mining indicated uptake of ASGM-GEM. These results demonstrate that Hg isotopes can be used to improve our understanding of sources to the atmosphere and soils (local/regional/global) and also assess major removal processes from the atmosphere.
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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 | 0.000 |
| 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 | 0.001 | 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".