First Comprehensive Measurements of Emission Rates of Greenhouse Gases, Volatile Organic Compounds and Reduced Sulfur Compounds from a Tailings Pond in the Alberta Oil Sands
Bibliographic record
Abstract
Canada’s Oil Sands Region in northern Alberta contains the world’s largest deposits of commercially exploited bitumen. Extraction of synthetic crude oil from these deposits is a water intensive process, requiring large ponds for water recycling and/or final storage of tailings, already covering a total of over 100 km2 of liquid surface area in the Athabasca Oil sands. The primary extraction tailings ponds primarily contain sand, silt, clay and unrecovered bitumen, while a few secondary extraction ponds also receive solvents and inorganic and organic by-products of the extraction process. Fugitive emissions of pollutants from these ponds to the atmosphere may therefore be a concern, but until recently, data on emission rates for many pollutants, other than a few reported under regulatory compliance monitoring, were sparse. We present here the results from a comprehensive field campaign to quantify the emissions from a secondary extraction pond to the atmosphere of 68 volatile organic compounds (VOCs), 22 polycyclic aromatic compounds (PACs), 8 reduced sulfur compounds as well as methane, carbon dioxide and ammonia. Three micrometeorological flux methods (eddy covariance, vertical gradients and inverse dispersion modeling) were evaluated for methane fluxes to ensure their mutual comparability. Methane and carbon dioxide fluxes were similar to previous results based on flux chamber measurements. Emission rates for 12 PACs, alkanes and aromatic VOCs, several sulfur species, and ammonia were found to be significant. PACs were dominated by methyl naphthalenes and phenanthrenes, while diethylsulfide and and n-heptane were the dominant reduced sulfur and VOC species, respectively. The role of these previously unavailable emission rates in regional pollutant budgets will be discussed.
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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 | 0.000 | 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".