Next-Generation Pyrosequencing Analysis of Microbial Biofilm Communities on Granular Activated Carbon in Treatment of Oil Sands Process-Affected Water
Bibliographic record
Abstract
The development of biodegradation treatment processes for oil sands process-affected water (OSPW) has been progressing in recent years with the promising potential of biofilm reactors. Previously, the granular activated carbon (GAC) biofilm process was successfully employed for treatment of a large variety of recalcitrant organic compounds in domestic and industrial wastewaters. In this study, GAC biofilm microbial development and degradation efficiency were investigated for OSPW treatment by monitoring the biofilm growth on the GAC surface in raw and ozonated OSPW in batch bioreactors. The GAC biofilm community was characterized using a next-generation 16S rRNA gene pyrosequencing technique that revealed that the phylum Proteobacteria was dominant in both OSPW and biofilms, with further in-depth analysis showing higher abundances of Alpha- and Gammaproteobacteria sequences. Interestingly, many known polyaromatic hydrocarbon degraders, namely, Burkholderiales, Pseudomonadales, Bdellovibrionales, and Sphingomonadales, were observed in the GAC biofilm. Ozonation decreased the microbial diversity in planktonic OSPW but increased the microbial diversity in the GAC biofilms. Quantitative real-time PCR revealed similar bacterial gene copy numbers (>10(9) gene copies/g of GAC) for both raw and ozonated OSPW GAC biofilms. The observed rates of removal of naphthenic acids (NAs) over the 2-day experiments for the GAC biofilm treatments of raw and ozonated OSPW were 31% and 66%, respectively. Overall, a relatively low ozone dose (30 mg of O3/liter utilized) combined with GAC biofilm treatment significantly increased NA removal rates. The treatment of OSPW in bioreactors using GAC biofilms is a promising technology for the reduction of recalcitrant OSPW organic compounds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".