Mesocosm Study of Chemical Treatments on Methane Emissions in Oil Sands Tailings Ponds─Part I: Focusing on the Change of Microbial Communities and Tailings Dewaterability
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In this study, we proposed mitigation strategies to reduce methane emissions from oil sands tailings ponds and determined the extent to which certain chemicals (Na 2 MoO 4 ·2H 2 O, Fe 2 (SO 4 ) 3, Na 2 SO 4, and Na 3 C 6 H 5 O 7 ·2H 2 O) could affect the methanogenesis process. Lab-scale mesocosms were used to compare the amount of fugitive emissions between paraffinic and naphthenic producer tailings. The inter-relationships among different parameters, such as methane, water chemistry, residual bitumen content in tailings, and microbial community, were investigated before and after the methane inhibition process. It was found that under different chemical treatment regimens, methanogenic populations were either suppressed or stimulated, demonstrating that functionally similar disturbances in natural systems may result in distinct responses of the microbial populations involved. The 16S RNA gene sequencing data revealed that both solvents and chemical treatments significantly impacted microbial diversity and communities in tailings, leading to notable shifts in dominant microbial families and a decrease in diversity in the treated samples. These treatments affected methanogenic families, reducing the abundance of archaeal methanogens (e.g., Methanegulaceae ) while increasing the presence of microbial families involved in hydrocarbon degradation, such as Spirochaetaceae and Thermovirgaceae . This study lays the groundwork for potential economically viable approaches to reduce methane emissions from oil sands tailings ponds.
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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 | 0.000 | 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 | 0.001 |
| 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".