Bioremediation of drill cuttings and petroleum-contaminated soil using biosurfactant-enhanced soil washing, biostimulation, and bioaugmentation
Bibliographic record
Abstract
Innovative technologies to combat environmental pollution are a significant part of sustainability research due to their increasing economic and environmental impact. The present biological process-based research study described herein was conducted in three phases. It investigated the effects of rhamnolipid-enhanced soil washing (phase 1), bioremediation treatment using indigenous microorganisms (phase 2), and the effect of four specific environmental and nutritional conditions (phase 3) on the biodegradation of petroleum hydrocarbons (PHC) in drill cuttings and petroleum-contaminated soil obtained from sites in northeastern British Columbia. For phase 1, maximum PHC reduction recorded for total petroleum hydrocarbon (TPH) and the petroleum hydrocarbon fractions- F2, F3 and F4 fractions was 58.5%, 48.4%, 63.5% and 59.8% respectively for petroleum-contaminated soil, and 76.8%, 85.4%, 71.3% and 76.9% respectively for drill cuttings. In phase 2, maximum PHC reduction of TPH, F2 and F3 fractions was 94.9%, 98.8% and 94.0% respectively for petroleum-contaminated soil and 82.6%, 94.9% and 59.5% respectively for drill cuttings following 50 days of rhamnolipid-mediated biodegradation treatment. Results from experiments conducted in phase 3 confirmed the importance of oxygen availability in biodegradation and indicated the inhibitory effects of excessive addition of biosurfactants and nutrients to hydrocarbon biodegradation treatments. Promising TPH degradation results were observed in conditions that normally slow down biodegradation. TPH degradation of 59.0%, 59.8% and 56.7% were observed in experiments conducted at an average temperature of ‒7.46 °C, and in waterlogged and air-tight conditions respectively. These results provide important insight on rhamnolipid-mediated biodegradation and indicate the high potential of rhamnolipid washing and bioremediation treatments as a combined approach to reduce PHC to levels within regulatory standards.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".