Isolation and identification of oil-degrading bacteria from oil-contaminated muddy soil samples at automotive service stations in Vietnam
Bibliographic record
Abstract
Background. Oil pollution from vehicle maintenance and oil storage tanks at automotive service stations poses significant environmental challenges, affecting both soil and water ecosystems. Bioremediation is an effective and eco-friendly approach that utilizes microorganisms to degrade hydrocarbons in contaminated environments. Numerous indigenous bacterial species capable of hydrocarbon degradation have been studied for their potential application in pollution treatment. Purpose. This study aimed to isolate and identify oil-degrading bacterial strains from oil-contaminated muddy soil samples collected from automotive service stations in Hanoi and Dong Nai, Vietnam. The objective was to evaluate their degradation efficiency and explore their potential application in bioremediation strategies. Materials and methods. Four oil-contaminated muddy soil samples were collected from car wash areas and oil storage tanks in Hanoi and Dong Nai, Vietnam, in August 2024. In this study, we used methods such as: enrichment in GOST mineral medium supplemented with crude oil mixed in DO, isolation method, assessment of oil degradation ability by gravimetric methods, OD600nm measurement by UV-vis spectrophotometer, study of morphological characteristics and molecular identification of bacterial strains. Results. From four oil-contaminated mud samples, after three enrichment cycles in a mineral medium supplemented with 5% (w/v) crude oil and diesel, sample M4 exhibited the highest oil degradation efficiency, achieving 80.12% removal after three enrichment cycles. Six representative bacterial strains were isolated on MPA agar from sample M4 and identified based on morphological and biochemical characteristics. Using molecular biological techniques, these hydrocarbon-degrading strains were identified as Achromobacter xylosoxidans ZB1.3 (PQ351236), Ignatzschineria rhizosphaerae ZB2.4 (PQ351237), Stenotrophomonas acidaminiphila ZB2.1 (PQ351238), Brevundimonas diminuta KN2.3 (PQ351239), Aeromonas hydrophila KN3.2 (PQ351240), and Rhodococcus ruber JN5.2 (PQ351241). The isolates, particularly strain JN5.2, demonstrated the ability to grow in a mineral medium supplemented with 1% oil after six days of incubation. Conclusion. These results reveal the diversity of oil-degrading microorganisms and underscore the potential of indigenous microbial communities for self-remediation in oil-polluted environments, offering a sustainable and effective solution for environmental restoration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".