Fugitive emissions of polycyclic aromatic compounds from an oil sands tailings pond based on fugacity and inverse dispersion flux calculations
Bibliographic record
Abstract
Alberta’s oil sands tailings ponds are suspected to be a source of fugitive emissions of polycyclic aromatic compounds (PACs) to the atmosphere. Here we report, for the first time, fluxes of 6 parent and 21 alkylated PACs based on the measured co-located air and water concentrations using a two-film fugacity-based model (FUG), an inverse dispersion model (DISP) and a simple box model (BOX). Air samples were collected at the Suncor Tailings Pond 2/3 using a high volume air sampler from the “pond” and towards the pond (“non-pond”) directions separately. Mean ∑27PACs in air from the “pond” direction was greater than the “non-pond” direction by a factor of 17. Water-air fugacity ratio of 20 PACs quantifiable in water indicated net volatilization from water. Dispersion and box model results also indicated upward fluxes of 22 PACs. Correlation between the estimated flux results of BOX and DISP model was statistically significant (r = 0.99 and p < 0.05), and correlation between FUG and DISP results ranged from 0.54 to 0.85. In this first-ever assessment of PAC fluxes from tailings pond, the three models confirmed volatilization fluxes of PACs indicating Suncor Tailings Pond 2/3 is a source of PAC emissions to the atmosphere. This study addressed a key data gap identified in the Joint Oil Sands Monitoring Emissions Inventory Compilation Report (Government of Alberta and Canada, 2016) which is the lack of consistent real-world tailings pond fugitive emission monitoring of organic chemicals. Our findings highlight the need for measurements from other tailings ponds to determine their overall contribution in releasing PACs to the atmosphere. This paper presents a practical method for estimating PAC emissions from other tailings ponds, which can provide a better understanding of these fugitive emissions, and thereby help to improve the overall characterization of emissions in the oil sands region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".