Atmospheric emissions estimation of polycyclic aromatic compounds from an oil sands tailings pond using passive air samplers
Bibliographic record
Abstract
A mapping study targeting emissions of polycyclic aromatic compounds (PACs) from an oil sands tailings pond was undertaken in the Athabasca Oil sands Region (AOSR). Ten passive air samplers comprising polyurethane foam (PUF) disks were deployed around the perimeter of Suncor Tailings Pond 2/3 for a five-week period to generate time-integrated concentrations in air for PACs, which included ∑unsubstituted polycyclic aromatic hydrocarbons (PAHs), ∑alkylated PAHs (alk-PAHs), and ∑dibenzothiophenes (DBTs) (both unsubstituted and alkylated). Concentrations in air ranged from 13 to 70, 220–970, and 30–210 ng/m3, respectively, and were elevated in samplers downwind of the tailings pond. PAC emissions to air from the pond were estimated using only the air-side concentration information by applying a simplified Gaussian dispersion model and found to be 896 μg/m2/day. ∑alk-PAHs and ∑DBTs had the highest contribution to the total PAC fluxes (79% and 16%, respectively). This flux estimate for PACs is equivalent to 460 kg on an annual basis and 35 000 kg/year when scaled to represent all tailings ponds in the region. The results generally agree with fluxes estimated from coupled high volume air sampling data and tailings pond water concentrations from the same field study but which are complicated due to uncertainties associated with the use of pure water Henry's Law values for tailings pond water as well as the potential for surface oily films on the tailings ponds to impact water-air exchange of PACs. Overall, these findings support the use of relatively simple and electricity-free PUF disk samplers for mapping and estimating emissions from area sources such as tailings ponds, using only air-side concentration information.
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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 | 0.000 |
| 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".