Impact of diesel and biodiesel contamination on soil microbial community activity and structure
Bibliographic record
Abstract
Soil contamination as a result of oil spills is a serious issue due to the global demand for diesel fuel. As an alternative to diesel, biodiesel has been introduced based on its high degradability rates and potential for reducing of greenhouse gases emissions. This study assessed the impacts diesel and biodiesel contamination on soil microbial community activity and structure. Our results suggest higher microbial activity in biodiesel contaminated soils and analysis of PLFA profiles confirmed shifts in microbial community structure in response to contamination. High-throughput 16S rRNA amplicon sequencing also revealed a lower bacterial richness and diversity in contaminated soils when compared to control samples, supporting evidence of the detrimental effects of hydrocarbons on soil microbiota. Control samples comprised mostly of Actinobacteria, whereas Proteobacteria were predominantly observed in diesel and biodiesel contaminated soils. At genus level, diesel and biodiesel amendments highly selected for Rhodococcus and Pseudomonas spp., respectively. Moreover, predicted functional profiles based on hydrocarbon-degrading enzymes revealed significant differences between contaminated soils mostly due to the chemical composition of diesel and biodiesel fuel. Here, we also identified that Burkholderiaceae, Novosphingobium, Anaeromyxobacter, Pseudomonas and Rhodococcus were the main bacterial taxa contributing to these enzymes. Together, this study supports the evidence of diesel/biodiesel adverse effects in soil microbial community structure and highlights microbial taxa that could be further investigated for their biodegradation potential.
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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 | 0.001 | 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".