Remediation of Oil-Contaminated Soil Using Nerium oleander Extract: Mechanisms of Hydrocarbon Absorption and Removal
Bibliographic record
Abstract
Findings from this study indicated the impact of hydrocarbon pollution on soil around the Dora refinery.The polluted soil contained high levels of hydrocarbons, up to 1,000 parts per million (ppm) for some soils, dominated by hydrocarbons such as benzene, toluene, and xylene.An asymmetrical distribution of these compounds was observed, with heavier hydrocarbons (e.g., kerosene and diesel) found in the lower soil strata, and lighter hydrocarbons (e.g., methane and ethane) in the upper layers.Aromatic hydrocarbon levels in contaminated soils were found to be 80-90% higher compared to uncontaminated samples.GC-MS analysis confirmed the presence of alkanes and aromatic compounds, including anthracene and furene, in the contaminated soils, indicating potential adverse effects on both soil and plant health.Application of Nerium oleander extract to polluted soil resulted in a significant reduction of hydrocarbons, ranging from 40% to 60% over a few weeks.Compared to untreated soil, hydrocarbon levels were reduced by approximately 50%.Heavy metal analysis revealed significantly higher concentrations in polluted soils than in uncontaminated ones, with iron (Fe) at 1000-1200 ppm, lead (Pb) at 50-70 ppm, cadmium (Cd) at 5-8 ppm, and zinc (Zn) at 150-200 ppm, indicating industrial contamination in the area.Various concentrations of Nerium oleander extract were tested, with the 10% concentration showing the most effective results-achieving a 70% hydrocarbon reduction after 14 days and up to 85% efficiency after 21 days.When compared to treatments using distilled water and ethanol, Nerium oleander extract demonstrated superior performance in hydrocarbon removal from the soil.In conclusion, the 10% oleander extract is a viable environmental treatment for removal of hydrocarbon pollution from contaminated soil and outperforms other treatments in terms of efficacy and ability to improve oil and industrial pollutioncontaminated soil quality.
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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) | 0.000 | 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".