The Surveying of Soil and Groundwater Pollution in a Petroleum Refinery and the Potential of Bioremediation for Oil Decontamination
Bibliographic record
Abstract
Soil contamination with crude oil is an important worldwide issue and the remediation of oil contaminated soils, sediments and groundwater is a major environmental challenge. In the target area of this survey, which is a petroleum refinery near Tehran, soil and groundwater pollution, and its source, contaminated area, and distribution of pollution were studied by means of different measurements. Oil content and volatile organic compounds were measured to determine soil and groundwater contamination. The investigations showed that the contamination of soil which is mainly silt and clay has reached to the groundwater which is around 20 m underground and formed an oily layer mainly containing gasoline, kerosene, and gas oil with different thicknesses in the whole area. The free oil existing over the groundwater table could be removed by physical ways such as pump and treat method but decontamination of soil is more complex. Due to long-lasting contamination of the field, the existence of accumulated indigenous microorganisms and the probable ability of them to effectively biodegrade pollutants by man-assisted interventions are expected. In this survey in order to clarify the contamination problem, some experiments have been done on the region soil and groundwater. Besides, the feasibility assessment of bioremediation in the investigated area is performed.
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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 | 0.001 | 0.001 |
| 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 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".