Bioremediation of Crude Oil Contaminated Saline Soil Using a Bacterial Consortium and Different Carriers
Bibliographic record
Abstract
Abstract Bioremediation of crude‐oil‐contaminated soils is critical to supporting soil ecosystem health and functioning. Here, to explore the potential for different remediation strategies to restore soil microbial functioning and enhance remediation, three strains of bacteria, Bacillus toyonensis, Bacillus sp. and Bacillus cereus, were inoculated to saline, crude oil‐contaminated soil collected from Azadegan Oil Field, Iran. Bacteria were added using different carriers, including corn biochar, humic acid, chitosan and kaolin, and free bacteria as a control. Changes in soil metal concentrations and residual crude oil were determined after 60 days of incubation. Soil respiration, microbial biomass, and enzyme (dehydrogenase and catalase) activities were measured. The carriers accompanying a bacterial consortium were more efficient than free bacteria in removing crude oil and heavy metals from soil, and they performed better in removing n‐alkanes with carbon chains of C12–24. The oil removal rate and metal immobilization efficiencies were greatest in soils with chitosan‐bacteria treatment, with removal of crude oil, Al, Pb, Sr, and Cr (93%, 63%, 37%, 54%, and 15%, respectively) greater than for soils with free cells without treatment (61%, 54%, 9%, 50%, and 3%, respectively). Soil electrical conductivity decreased after application of bacterial mixtures with and without carriers. The inoculation of bacteria and application of soil amendments increased soil microbial biomass carbon and the activity of catalase and dehydrogenase. Overall, our results indicated the greatest potential remediation was achieved by applying immobilized microorganisms in chitosan to accelerate the biodegradation of crude oil and the removal of heavy metals.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".