Bioremediation of Diesel Fuel Contaminated Soil: Comparison of Individual Compounds to Complex Mixtures
Bibliographic record
Abstract
Bioremediation is a cost effective means of remediating soils contaminated with petroleum hydrocarbons. Various factors affect the efficiency of the process, including environmental conditions, microbial population present and composition of the hydrocarbon spill. To evaluate the impact of mixture composition on individual compound and overall degradation, biodegradation experiments were conducted in sealed, 1-liter bioreactor/respirometer vessels containing soil spiked with hydrocarbon compounds in isolation and in mixtures. The influence of bacteria and fungi on the degradation process was also monitored. The degradation behavior of the various compounds was monitored using the fraction of contaminant remaining and first-order degradation coefficients based on hydrocarbon loss. The results showed that the degradation trend of a compound changed when present in a simple mixture or when degraded in isolation. The presence of the compounds as either an aliphatic or aromatic mixture did not change the degradation trend. The presence of a mixture also affected the amount of degradation with some compounds degrading to a greater extent when in isolation. Overall, the majority of degradation occurred in the first 10 d, suggesting that the first-order model may not be an appropriate model for degradation periods longer than 10 d when nutrient limited. It was also found that fungal metabolism is important for the degradation of hydrocarbons, particularly for branched species such as pristane as the decay rate increased one order of magnitude when bacteria were inhibited.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".