Atmospheric polycyclic aromatic hydrocarbons in the Canadian Athabasca oil sands region: emission database update and assessment of contributions of oil sands sources to ambient concentrations
Bibliographic record
Abstract
An existing emission database for 16 polycyclic aromatic hydrocarbons (PAHs) covering the Athabasca oil sands region (AOSR) of Alberta, Canada was updated using the latest knowledge reported in literature and then validated using ambient concentration measurements through dispersion modeling. The revised domain-total emission of the total of 15 PAH species (∑15PAHs, excluding naphthalene) was 25.3 tonnes/yr, of which phenanthrene (9.4 tonnes/yr), fluorene (4.2), acenaphthene (2.9), pyrene (2.4), and fluoranthene (1.9) were the dominant species. Emission source sectors quantified in this study include point sources (7.9 tonnes/yr), mine fleet (6.5), residential and commercial (3.7), local traffic and airport (3.4), tailings ponds (1.8), fugitive dust (1.6), and agriculture controlled burning (0.4). The revised domain-total naphthalene emission was 56.3 tonnes/yr. With this emission database as input, the dispersion model-predicted concentrations were comparable with passive-sampling measurements; specifically, modeled annual concentration of ∑15PAHs was only ∼4 % lower than measurements at seven local sites and ∼17 % lower at nine remote sites. The emission sectors were regrouped into Oil Sands (OS) sources and non-Oil Sands (non-OS) sources to conduct dispersion model sensitivity tests. OS sources contributed ∼70 % to the domain-averaged ambient concentration of ∑15PAHs. Areas north of Fort McMurray, especially those close to or downwind of tailings ponds and active mining pits, had OS source contributions exceeding 50 %, while areas south of Fort McMurray were mainly impacted by non-OS sources.
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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 | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".