Developing Specialized Bacterial Consortia for Enhanced Biodegradation of Chlorpyrifos in Dairy Farms in Nakuru County
Bibliographic record
Abstract
Chlorpyrifos (CP), a widely used organophosphate in Kenya, poses significant human health risks due to its high solubility. This study aimed to isolate CP-degrading bacteria from dairy farm soils in Nakuru County and develop bacterial consortia for efficient degradation. Soil samples were collected from six sub-counties: Molo, Njoro, Rongai, Subukia, Gilgil, and Naivasha. The enrichment culture technique using minimal salt medium (MSM) was employed to isolate CP-degrading bacterial strains. Seven bacterial strains were identified: Alcaligenes faecalis, Bacillus weihenstephanensis, Bacillus toyonensis, Alcaligenes sp., Pseudomonas sp., Pseudomonas japonicum, and Brevundimonas diminuta. Bacillus weihenstephanensis exhibited the highest individual degradation efficiency at 79.8%. Consortia treatments demonstrated enhanced degradation, with consortia M1, M2, and M3 achieving removal efficiencies of 91.3%, 92.48%, and 93.76%, respectively, and M5 achieving 95.32%. The study also monitored 3,5,6-trichloro-2-pyridinol (TCP), a CP metabolite, showing that consortia treatments significantly reduced TCP levels to near initial concentrations. Kinetic analysis revealed consortia had significantly shorter half-lives for CP degradation compared to individual isolates, indicating improved bioremediation potential. The findings underscore the potential of using tailored bacterial consortia for effective bioremediation of CP-contaminated environments. Future research should focus on field trials to validate these laboratory findings under real-world conditions and explore the genetic and enzymatic mechanisms underlying CP degradation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".