Distinguishing trace elements in acid soluble ash (ASA) and acid insoluble ash (AIA) of Sphagnum mosses within the Athabasca Bituminous Sands Region
Bibliographic record
Abstract
The Athabasca Bituminous Sands (ABS) industry has dramatic benefits for the economy of Alberta, Canada. However, with increasing industrial operations, environmental concerns have grown regarding the contamination of air and water with trace elements (TEs). The ABS are composed of both minerals (ca. 85%) and bitumen (ca. 15%). While V, Ni, Mo, and Re are found primarily in bitumen, other potentially toxic TEs such as As, Cd, and Pb occur mostly in minerals. The mechanical processing of ABS by industry generates considerable volumes of dust particles from processes and sources such as open-pit mining, quarrying, road construction, petroleum coke transport and storage, and dry tailings. These dusts are dominated by coarse aerosols with short atmospheric residence times, consisting primarily of recalcitrant and sparingly reactive silicate minerals enriched in lithophile elements such as Al, Fe, and Mn. In contrast, high-temperature industrial processes such as the smelting and refining of metallic ores and coal combustion yield fine aerosols (< 2 µm) that can be transported for thousands of kilometers. These fine aerosols are respirable and mostly in the forms of oxides and hydroxides rich in TEs such as As, Cd, and Pb, posing a risk to all living organisms. Hence, it is important to differentiate between TEs in the two aerosol fractions. Here, Sphagnum mosses collected from ombrotrophic (rain-fed) bogs within the ABS region are used as biomonitors of atmospheric deposition, and compared with mosses from a reference site 264 km to the southwest. The aim is to estimate the percentage of TEs in the fine versus coarse aerosol fractions by determining the abundance of TEs in the acid soluble ash (ASA) and acid insoluble ash (AIA) in Sphagnum. Trace element concentrations (total, in ASA and in AIA) were obtained using ICP-MS. Concentrations of AIA and total concentrations of TEs increased towards industry, reflecting increasing dust deposition. Comparing the site nearest industry (JPH4) to the control site (UTK), the greatest differences in total concentrations were measured for lithophile elements such as Li, Be, and the lanthanides; V, Ni, and Mo were all 10x more abundant; the differences in chalcophile elements were much less apparent: Pb and Tl 6x, Ag 3x and Cu, Cd, and Zn < 2x. In AIA, Cs, Li, La, and Al were all more abundant at JPH4; Tl was slightly more abundant (3x); Ag, Cu, and Pb were all more abundant at UTK. In ASA, Th, Al, and the lanthanides were more abundant at JPH4; however, concentrations of Cd, Cu, Ag, Zn, Sb, and Tl were higher at UTK. In general, therefore, lithophile elements were more abundant in samples collected near industry, in total concentration as well as in the AIA and ASA fractions. However, chalcophile elements exhibited either insignificant differences, or were more abundant at the control site. Clearly, measuring only the total concentrations of TEs in moss from a dusty industrial region provides limited information about their associated health risks.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 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".