The Effect of Temperature and Aeration Rate on Bioremediation of Diesel-contaminated Soil in Solid-phase Bench-scale Bioreactors
Bibliographic record
Abstract
Bioremediation of hydrocarbon (HC) contaminated soils is most effective in aerobic conditions. Despite the fact that mass transfer of oxygen is an important process parameter, the effect of this parameter on solid-phase bioremediation has received limited attention. In this study, the combined effect of temperature and aeration on the bioremediation of low organic content coarse-grained soils, freshly contaminated with diesel, was investigated in solid-phase bench-scale bioreactors. Total HC and carbon range soil concentrations, volatilization, and microbial activity were monitored throughout the six-month experiments at two temperatures (7 and 22°C) and at two aeration rates (13 and 45 mL·s−1). Total HC removal reached between 48 and 83%. Generally, removal increased proportionally with temperature and aeration rates, and decreased proportionally with HC compounds molecular weight. Both biodegradation and volatilization played important roles in removal in all treatments. The high aeration rate enhanced microbial activity in soil. Enhancement was believed to be due to increased mass transfer of oxygen from the soil gas to the soil solution, where microbial activity occurs. However, high aeration also enhanced volatilization, especially at 22°C where 51% of HCs were lost to volatilization. High aeration rate enhanced biodegradation of compounds > nC15 without promoting their excessive volatilization.
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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 | 0.001 |
| 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) | 0.001 | 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".