Evaluating officially reported polycyclic aromatic hydrocarbon emissions in the Athabasca oil sands region with a multimedia fate model
Bibliographic record
Abstract
Emissions of organic substances with potential toxicity to humans and the environment are a major concern surrounding the rapid industrial development in the Athabasca oil sands region (AOSR). Although concentrations of polycyclic aromatic hydrocarbons (PAHs) in some environmental samples have been reported, a comprehensive picture of organic contaminant sources, pathways, and sinks within the AOSR has yet to be elucidated. We sought to use a dynamic multimedia environmental fate model to reconcile the emissions and residue levels reported for three representative PAHs in the AOSR. Data describing emissions to air compiled from two official sources result in simulated concentrations in air, soil, water, and foliage that tend to fall close to or below the minimum measured concentrations of phenanthrene, pyrene, and benzo(a)pyrene in the environment. Accounting for evaporative emissions (e.g., from tailings pond disposal) provides a more realistic representation of PAH distribution in the AOSR. Such indirect emissions to air were found to be a greater contributor of PAHs to the AOSR atmosphere relative to reported direct emissions to air. The indirect pathway transporting uncontrolled releases of PAHs to aquatic systems via the atmosphere may be as significant a contributor of PAHs to aquatic systems as other supply pathways. Emission density estimates for the three PAHs that account for tailings pond disposal are much closer to estimated global averages than estimates based on the available emissions datasets, which fall close to the global minima. Our results highlight the need for improved accounting of PAH emissions from oil sands operations, especially in light of continued expansion of these operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".