Biopile bioremediation of petroleum hydrocarbon contaminated soils from a sub-Arctic site
Bibliographic record
Abstract
Petroleum contamination of several hundred sites in the northern arctic and sub-arctic regions of Canada has occurred as a result of petroleum oil exploration and use of petroleum fuels for heating, transportation and electricity generation. Petroleum contamination can persist in the ground for long periods of time and be a source of long-term environmental contamination. Bioremediation is a non-disruptive and often cost-effective technology for remediation of petroleum-contaminated sites that involves the microbial degradation of hydrocarbon compounds. Biopiles allow for rapid ex-situ treatment of petroleum-hydrocarbon contaminated soils. Two pilot scale biopiles (300 kg soil each) were construct using soils contaminated with approximately 1 500 mg/kg total petroleum hydrocarbons (TPH) from Norman Wells, North West Territories. Both systems were supplied with oxygen to stimulate aerobic conditions, and monitored in an enclosed room maintained at a temperature of 15oC, the ambient summer temperature in Norman Wells. One biopile was amended with ammonium nitrate at a ratio of 100:5:1 (C:N:P) to determine the effects of nutrients on TPH biodegradation. The research showed that biodegradation occurred within both biopile systems. Analysis of the hydrocarbon fractions, TPH chromatograms, and oxygen consumption and carbon dioxide production supported biodegradation versus volatilization. However, an absolute confirmation of whether these loses were due to biodegradation (or to what extent) are not possible to be reported here. Analysis of the inorganic nitrogen and aggregation of the soils helped provide insight into the process of biodegradation in both biopile systems. Overall 42% of the total petroleum hydrocarbons were removed from the nutrient amended biopile and 38 % in the control biopile. For the F2 (>C10-16) fraction, both systems had less than 200 mg/kg soil and for the F3 (>C16-34) fraction around 700 mg/kg soil.
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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 | 0.001 | 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".