Degradation Rates for Petroleum Hydrocarbons Undergoing Bioventing at the Meso-Scale
Bibliographic record
Abstract
Bioventing can be effective for the remediation of soil contaminated with petroleum hydrocarbons. However, implementing laboratory results in field scenarios is difficult due to the lack of scale-up factors. Accordingly, laboratory bioventing experiments were undertaken at the meso-scale and then compared with previously completed micro-scale tests to evaluate the important scale-up factor. The developed meso-scale system holds 4 kg of soil, with bioventing conditions controlled from a nutrient, airflow, and water content perspective. Three soils were tested, and categorized as loamy sand, silt loam, and a mixture. Results over a 30-day period showed a two-stage degradation pattern that encompassed first-order degradation rates as compared with the single-stage first-order degradation rate determined in the micro-scale study. For the first stage (0–8 days), the degradation rate for loamy soil was 0.598 day−1, with the silty soil at 0.460 day−1, and mixed soil at 0.477 day−1. After 8 days, the degradation rate constant for the loamy soil dropped to 0.123 day−1, with the silty soil dropping to 0.075 day−1, and the degradation rate for the mixed soil dropping to 0.093 day−1. Comparison of the measured degradation rate values with the results from the micro-scale experiments gave scale-up factors varying from 1.9 to 2.7 for the types of soil considered in the current study. These differences in degradation rates between the two scales show the importance of scale-up factors when transferring feasibility study results to the field.
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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) | 0.000 | 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 | 0.001 |
| 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".