Characterization of PM<sub><b>2.5</b></sub> and PM<sub><b>10</b></sub> Fugitive Dust Source Profiles in the Athabasca Oil Sands Region
Bibliographic record
Abstract
ABSTRACTGeological samples were collected from 27 representative locations in the Athabasca Oil Sands Region (AOSR) in Alberta, Canada. These samples were resuspended onto filter substrates for PM2.5 and PM10 size fractions. Samples were analyzed for 229 chemical species, consisting of elements, ions, carbon, and organic compounds. These chemical species are normalized to gravimetric mass to derive individual source profiles. Individual profiles were grouped into six categories typical of those used in emission inventories: paved road dust, unpaved road dust close to and distant from oil sand operations, overburden soil, tailings sands, and forest soils. Consistent with their geological origin, the major components are minerals, organic and elemental carbon, and ions. The sum of five major elements (i.e., Al, Si, K, Ca, and Fe) and their oxidized forms account for 25‒40% and 45‒82% of PM mass, respectively. Si is the most abundant element, averaging 17-18% in the Facility (oil sand operations) and 23-27% in the Forest profiles. Organic carbon is the second most abundant species, averaging 9-11% in the Facility and 5-6% in the Forest profiles. Elemental carbon abundance is 2‒3 times higher in Facility than Forest profiles. Sulfate abundance is ~7 times higher in the Facility than in the Forest profiles. The ratios of cation/anion and base cation (sum of Na+, Mg2+, K+, and Ca2+)/nitrogen- and sulfur-containing ions (sum of NH4+, NO2-, NO3-, and SO42-) exceed unity, indicating that the soils are basic. Lead (Pb) isotope ratios of facility soils are similar to the AOSR stack and diesel emissions, while those of forest soils have much lower 206Pb/207Pb and 208Pb/207Pb ratios. High molecular weight n-alkanes (C25-C40), hopanes, and steranes are over an order of magnitude more abundant in Facility than Forest profiles. These differences may be useful for separating anthropogenic from natural sources of fugitive dust at receptors.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".