Improving Cap Water Quality in An Oil Sands End Pit Lake with Microbial Applications
Bibliographic record
Abstract
The oil sands industry in Alberta, Canada has been thriving for over five decades, and this multi-million-year-old resource has fueled rapid economic growth in Alberta. However, the oil sands industry has left behind tons of oil sands tailings and oil sands process-affected water (OSPW), and the surface mining process has greatly disturbed the landscape. Oil sands end pit lakes (EPLs) are proposed to reclaim the impacted land and remediate the large quantities of accumulated oil sands tailings and OSPW. A typical EPL is commissioned within a depleted mine pit, with oil sands tailings stored below a water cap. Using materials from Base Mine Lake (BML, the first full scale EPL), this thesis investigates the applicability of microbes for improving cap water quality. The objectives of this thesis were: (1) biological treatment of the residual bitumen; (2) model NA’s degradation by the microalgae, the BML cap water microbes, and the co-culture of the two; and (3) cap water turbidity mitigation using microbial addition. Recovery of the bitumen from the oil sands is not 100% effective, and the unextracted bitumen will remain in the oil sands tailings. In an EPL, the residual bitumen in the tailings can potentially cause environmental concerns if proper action is not taken. As bitumen migrates from the tailings to float on top of the water cap, it might change the surrounding water chemistry by releasing hydrocarbons and biodegradation by-products. Firstly, a biological amendment was used to treat three different types of oil sands tailings to remove bitumen content. Secondly, biostimulation treatment with acetate of the indigenous tailings microbial community was used to treat the bitumen. The presence of bitumen was found to increase the water toxicity. Analysis of the indigenous tailings microbial community profile combined with monitoring of CO2 (complete mineralization), dissolved organic carbon (indirect parameter) and petroleum hydrocarbons revealed that four genera (Rhodoferax, Acidovorax, Pseudoxanthomonas and Pseudomonas) were potential bitumen-degraders.Chlorella kessleri and Botryococcus braunii were tested for their capability to tolerate and biodegrade three model NAs (cyclohexanecarboxylic acid (CHCA), cyclohexaneacetic acid (CHAA), and cyclohexanebutyric acid (CHBA)): C. kessleri showed better tolerance and more effective removal of the tested NAs than B. braunii. BML cap water was also used as inoculum alone and co-cultured with C. kessleri to biodegrade the CHBA and CHCA. All tested cultures used β-oxidation pathway to biodegrade model NAs. The co-culture of BML inoculum and C. kessleri had a higher biodegradation rate of CHBA than BML inoculum and C. kessleri alone, and removed CHCA 25 d faster than C. kessleri alone (70 d). C. kessleri greatly increased the bacterial diversity of the BML inoculum, and this more diverse community was thought to lead to the more rapid and complete degradation of model NAs. Sporosarcina pasteurii, C. kessleri and B. braunii were tested to remove the cap water turbidity. C. kessleri addition can remove the cap water turbidity effectively in the bench scale experiment, and nutrient addition can remove a comparable level of turbidity possibly by stimulating the growth of the indigenous algal community. B. braunii didn't achieve any turbidity removal alone. S. pasteurii, a urea-hydrolyzing bacterium, can carry out microbial induced calcite precipitation (MICP) process leading to biocementation. MICP requires several conditions to occur: alkaline pH, Ca2+, CO32- and nucleation site availability. With the addition of S. pasteurii providing the nucleation sites, MICP has the potential to occur in BML cap water. Results in this study showed that with S. pasteurii alone, MICP might not be fully carried out in the BML cap water. However, the addition of S. pasteurii with calcium or urea could achieve effective turbidity removal with the formation of particles of increased size.
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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".