Interannual and spatial variations in acid-soluble trace elements in snow: comparison with the mineralogy of dusts from open pit bitumen mining
Bibliographic record
Abstract
There is ongoing concern about trace element (TE) emissions to the global environment from the dusts generated by open pit mining of coal, iron ore, stone quarries, and aggregate extraction. However, the chemical composition and acid solubility of these dusts is highly variable. Here, TEs were determined in snow collected in 2016 and 2017 in the vicinity of open-pit bitumen mines in northern Alberta, Canada. Acid solubility was assessed quantitatively by comparing TE concentrations in leachates and acid digests. The mineralogical composition of the particles extracted from the snow was examined using SEM-EDS. The data is reproducible from one year to the next. TE concentrations were greater throughout the industrial zone compared to the reference location (UTK), with the midpoint between the two central upgraders being especially impacted. Regardless of their geochemical class (lithophile: Al, Be, Cs, La, Li, Sr, Th; chalcophile: As, Cd, Pb, Sb, Tl; or enriched in bitumen: Mo, Ni, V), all TEs showed strong, positive correlations with Y, a conservative element which serves as a surrogate for the abundance of mineral particles. The ratio V:Ni in the snow is less than the corresponding values for bitumen and petcoke, but similar to that of local road dust. The ratio La:Al in snow is elevated, relative to the earth's crust, suggesting an enrichment of heavy minerals monazite and zircon. The predominance of quartz and other stable silicates helps to explain the limited chemical solubility of the dusts, and predicts a low bioaccessibility of these TEs in the environment.
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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 | 0.001 | 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".