Effect of Terminal Electron Acceptors on Greenhouse Gas Emissions in Oil Sands Tailings
Bibliographic record
Abstract
Surface mining of Alberta's oil sands produces large volumes of water and solid wastes stored in oil sands tailings ponds. Tailings ponds contain numerous contaminants that have negative effects on the environment (air, water, and land). Each tailings pond has its unique characteristics depending on different chemical additive used during bitumen extraction. Diverse microbial populations developed within each pond which have a vital role in the bioremediation of toxic compounds therein. As tailings ponds age and at low redox conditions (~ -250 mV), some prokaryotes, particularly methanogenic archaea, oxidize petroleum hydrocarbons (such as naphtha) using acetic acid or CO2 as a terminal electron acceptor (TEA) resulted in methane (greenhouse gas) efflux from OSTPs. Some studies estimated the current methane emissions to be up to 43 million liters/day. GHG evolution is stimulated by the naphtha diluent that ends up in tailings ponds during the bitumen extraction process but the processes controlling methane production are not well understood. In this study, the effect of TEA (SO42- and NO3-), nutrients (PO43- and NH4+), and carbon source (naphtha) on GHG production was investigated in microcosm experiments using mature fine tailings of Suncor Energy Pond 7. In NO3- and SO4-amended microcosms, methanogenesis was reduced by 98.5 and 97.8%, respectively, as initial NO3- or SO42- concentration increased. In MFT-amended with phosphate under methanogenic conditions, CH4 production increased with increasing PO43- concentration by a factor 2.4 times that of the unamended biotic control. Biogenic gas production (CH4, H2S, and N2O) increased with increasing naphtha concentration. The yield of CH4 produced from naphtha consumed under methanogenic conditions was 0.058 g/g which is lower than the theoretical yield since some of the carbon from naphtha was likely used to make microbial biomass and/or the formation of biogenic CO2 or other intermediates like acetate. There was a difficulty in measuring the biogenic CO2 produced due to the equilibrium between HCO3− and CO32− in MFT and biogenic CO2. In conclusion, it is possible to minimize GHG emissions by manipulation of TEA. However, increasing naphtha and/or phosphate concentration will result in increasing CH4 production.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".