Biostimulation of Oil Sands Process-Affected Water with Phosphate Yields Removal of Sulfur-Containing Organics and Detoxification
Bibliographic record
Abstract
The ability to mitigate toxicity of oil sands process-affected water (OSPW) for return into the environment is an important issue for effective tailings management in Alberta, Canada. OSPW toxicity has been linked to classical naphthenic acids (NAs), but the toxic contribution of other acid-extractable organics (AEOs) remains unknown. Here, we examine the potential for in situ bioremediation of OSPW AEOs by indigenous algae. Phosphate biostimulation was performed in OSPW to promote the growth of indigenous photosynthetic microorganisms and subsequent toxicity and chemical changes were determined. After 12 weeks, the AEO fraction of phosphate-biostimulated OSPW was significantly less toxic to the fission yeast Schizosaccharomyces pombe than unstimulated OSPW. Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) analysis of the AEO fraction in phosphate-biostimulated OSPW showed decreased levels of SO3 class compounds, including a subset that may represent linear arylsulfonates. A screen with S. pombe transcription factor mutant strains for growth sensitivity to the AEO fraction or sodium dodecylbenzenesulfonate revealed a mode of toxic action consistent with oxidative stress and detrimental effects on cellular membranes. These findings demonstrate a potential algal-based in situ bioremediation strategy for OSPW AEOs and uncover a link between toxicity and AEOs other than classical NAs.
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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 | 0.000 |
| 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 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".