INFLUENCE OF MICROBIAL SULFATE REDUCTION ON PORE-WATER CHEMISTRY IN OIL SANDS FROTH TREATMENT TAILINGS
Bibliographic record
Abstract
Oxidative weathering of froth treatment tailings (FTT) at oil sands mines in Northern Alberta has the potential to generate acidic and metalliferous porewater. Residual bitumen and light hydrocarbons in FTT support growth of a diverse range of microbes, including those capable of dissimilatory sulfate reduction (DSR). This biogeochemical process can be effective in removing sulfide-mineral oxidation products such as Fe, SO4, and trace elements including As, Co, Cu, Ni, Se, and Zn through sulfate reduction and sulfide mineral precipitation under anoxic conditions. While previous experiments that have stimulated sulfidogenesis through organic carbon amendments effectively decreased the mass flux of sulfide-mineral oxidation products in mining environments, the extent of this process and its ability to attenuate sulfide-mineral oxidation products in FTT supported by residual hydrocarbons is unknown. The objective of this thesis is to assess the impact of microbial sulfate reduction on FTT pore-water geochemistry and to determine the potential extent to which this process may be supported by residual hydrocarbons within the tailings deposit. Laboratory experiments were designed to examine the influence of sulfidogenesis on pore-water chemistry and to constrain rates of sulfate reduction, while assessing the capacity for metal(loid) removal due to this process. Through batch and column experiments sulfate reduction was found to contribute to metal(loid) and sulfate removal, as well as pH buffering and H2S generation. Removal of As, Co, Ni, and Se exceeded 90% in simulated porewater over a pH range of 5 – 8. Removal of Fe was minimal below pH 7, but exceeded 95% in solutions above pH 7 with long residence times. Zn removal was influenced by ∑S(-II)(aq) concentrations, with increased Zn removal seen at lower ∑S(-II)(aq) concentrations. Sulfur isotope samples taken during the batch experiment show an increase in 34S-SO4 with decreasing SO4(aq) concentrations over time, showing ongoing microbial sulfate reduction during the experiment. A mass-based approach to determine sulfate removal found sulfur removal rates remained constant around 1.5 μmol d-1 g-1 when solutions were not saturated with respect to gypsum. Residence time and pH were major influences on the effectiveness of sulfate reduction, with enhanced metal(loid) removal occurring with longer residence times at circumneutral pH. These results show the pool of organic carbon in FTT deposits is capable of supporting dissimilatory sulfate reduction and this process can decrease mass fluxes of sulfide-mineral oxidation products in FTT porewater.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".