Emissions database development and dispersion model predictions of airborne particulate elements in the Canadian Athabasca oil sands region
Bibliographic record
Abstract
This study developed an emission inventory for 29 elements in PM2.5 and PM2.5-10 covering an area of approximately 300 by 420 km2 in the Athabasca Oil Sands Region in northern Alberta, Canada. Emission sources were aggregated into nine categories, of which the Oil Sands (OS) Sources emitted the most, followed by the Non-OS Dust sources for both fine and coarse elements over the study area. The top six fine particulate elements include Si, Ca, Al, Fe, S, and K (933, 442, 323, 269, 116, and 103 tonnes/year, respectively), the sum of which accounted for 20.5% of the total PM2.5 emissions. The top five coarse elements include Si, Ca, Al, Fe, and K (3713, 1815, 1198, 1073, and 404 tonnes/year), and their sum accounted for 29% of the total PM2.5-10 emissions. Using this emission inventory as input, the CALPUFF dispersion model simulated reasonable element concentrations in both PM2.5 and PM2.5-10 when compared to measurements collected at three sites during 2016–2017. Modeled PM10 concentrations of all elements were very close to the measurements at an industrial site with the highest ambient concentration, overestimated by 65% at another industrial site with moderate ambient concentration, and underestimated by 27% at a remote site with very low ambient concentration. Model-measurement differences of annual average concentrations were within 20% for Si, Ca, Al, Fe, Ti, Mn, and Cu in PM2.5, and were 20–50% for K, S, and Zn in PM2.5 at two sites located within the OS surface mineable area. Model-measurement differences were larger, but still within a factor of two for elements in PM2.5-10 at these two sites and for elements in both PM2.5 and PM2.5-10 at a background site.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".