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Isolation and identification of oil-degrading bacteria from oil-contaminated muddy soil samples at automotive service stations in Vietnam

2025· article· ru· W7155059233 on OpenAlexaff
Thi Kim Thanh Nguyen, Đỗ Thị Tuyến, Thi Thanh Thuy Tran, Thi Mo Luong, Quang Tuyen, The Can Nguyen, Khac Trinh Nguyen, Dinh Dai Phan, Cao Cuong Ngo

Bibliographic record

VenueSiberian Journal of Life Sciences and Agriculture · 2025
Typearticle
Languageru
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsGLS Industries (Canada)
Fundersnot available
KeywordsBioremediationAchromobacterContaminationIsolation (microbiology)Soil contaminationStenotrophomonasPollutionSoil test

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.223
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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