Tracing Atmospheric Mercury from Artisanal and Small-Scale Gold Mining
Bibliographic record
Abstract
Artisanal and small-scale gold mining (ASGM) is one of the largest primary sources of mercury (Hg) pollution in the atmosphere globally; however, there is a paucity of atmospheric Hg data in ASGM areas. We measured atmospheric gaseous elemental mercury (GEM) concentrations and stable Hg isotopes at fine spatial resolution in the Madre de Dios region of Peru, where ASGM is a major source of Hg. This study employed new passive air samplers that overcome logistical challenges in measuring atmospheric Hg in remote locations. Regional GEM concentrations were elevated (∼1.3 to 11 ng m –3 ) compared to the background (<1 ng m –3 ), with very high GEM levels (∼10 to >5000 ng m –3 ) associated with mining areas and gold shops. Because ASGM-derived GEM is isotopically distinct, its contribution to regional and local atmospheric Hg was estimated using an isotope mixing model and found to be generally over 70%. We also show that vegetation is taking up ASGM-derived GEM, affecting both the concentrations and isotope compositions of GEM as well as in foliage and litter samples. This supports vegetation uptake as a key removal process of GEM from the atmosphere and therefore a major source of Hg to terrestrial ecosystems and soils, which is heightened in ASGM regions.
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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 | 0.001 |
| 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) | 0.000 | 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".