Thermally Enhanced Bioremediation of NAPL Polluted Soil-Water Resources
Bibliographic record
Abstract
The use of conventional techniques for physico-chemical remediation of hydrocarbon such non-aqueous phase liquids (NAPL)-polluted sites may disturb the natural biotic settings of the (sub)-surface. However, natural attenuation has been reported very slow and sometime results as incomplete removal under prevailing site conditions. In particular, microbial growth is quite slow in cold regions, which reduces the applicability of bioremediation in treating NAPL-polluted soil-water. Thus, this study aims to evaluate the thermally enhanced bioremediation techniques to treat NAPL-polluted soil-water using practical experiments. A one-dimensional large column setup was designed and fabricated for this purpose. The column was integrated with automatic temperature controlling baths to maintain different soil-water temperatures (4 °C, 20 °C, 28 °C, and 36 °C), which was circulated through the porous media filled in the column setup. Results show a high dissolution rate of toluene, the selected light NAPL, at an elevated temperature of 28–36 °C. The biodegradation rates of the NAPL were found to be 0.002 mg L/h, 0.008 mg L/h, 0.012 mg L/h, and 0.015 mg L/h at soil-water temperature levels of 4 °C, 20 °C, 28 °C, and 36 °C, respectively. It was found that at high soil-water temperature (28 °C and 36 °C), a significant increment in microbial actions accelerates the biodegradation rate of NAPL in the subsurface system. The outcomes of this study may help in treating NAPL-polluted sites using solar or geo-thermal based heating systems for thermally enhanced bioremediation.
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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 | 0.000 | 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".