Bioventing Degradation Rates of Petroleum Hydrocarbons and Determination of Scale-up Factors
Bibliographic record
Abstract
Petroleum hydrocarbons are ubiquitous environmental contaminants that need to be removed from soil after a spill due to their toxic and carcinogenic characteristics. Bioventing has the potential to be an effective bioremediation technology for contaminated soil, yet many challenges exist when extending results from the laboratory to the field. Understanding scale dependent phenomena is difficult, because most laboratory studies were completed at the micro-scale and limited work has been done on the larger scale. Accordingly, the main aim of the current study was to determine petroleum hydrocarbons degradation rate coefficients at the medium scale (meso-scale), which is considered a bridge between the micro and field scales, and to determine the scale-up factor. Refinement of a previously developed correlation by incorporating scale-up effects was also an additional goal. Bioventing conditions were simulated in a bench scale reactor holding 4 kg of soil on a dry weight basis that was spiked with synthetic gasoline. Five different types of soils were tested, with each soil amended with water and nutrients. Preliminary testing with the meso-scale system showed the appropriateness of the experimental system for conducting reliable bioventing treatments. Evaluation of the experimental data showed two stage hydrocarbon degradation with a fast rate of degradation in the initial 8 d and then a slower rate for remaining 22 d of the treatments. Using statistical analysis, correlations were developed which suggest a significant positive effect of the initial population of petroleum degrading bacteria and a negative impact of increasing fractions of organic matter on the rate of hydrocarbon degradation in the soil. Results also exhibited a positive impact of increasing silt and a negative effect of increasing clay on the hydrocarbon degradation rate constant. In general, higher rates of degradation were observed at the meso-scale tests when compared with the results determined through a micro-scale correlation. Comparison of micro-scale and meso-scale correlations also suggested a scale-up factor (SF) of approximately 2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".