Assessment of fishes, sediment and water from some inland rivers across the six geopolitical zones in Nigeria for microplastics
Bibliographic record
Abstract
In Nigeria, limited research has been conducted on Microplastics (MPs) in inland rivers, necessitating a comprehensive assessment to understand the extent of contamination. This study aimed to assess the abundance, distribution, and composition of MPs in fishes, sediment, and water from inland rivers across Nigeria's six geopolitical zones. Samples were collected from selected rivers in each geopolitical zone (Rivers Yauri, Benue, Argungu, Jamare, Ogun, Ethiope and Orashi). MPs were isolated using a combination of filtration, density separation, and visual identification. MPs abundance, distribution, shapes, colors, and chemical composition were determined using microscopy and Fourier-transform infrared spectroscopy. The study presents the first report of MPs in six in land rivers in Nigeria and found that MPs were present in all the fishes, sediments and river waters studied across all the rivers. The abundance and composition of MPs varied among the different sample types, with fibers being the most abundant shape in both water and fish samples. PET, PP, and PE were the most prevalent types of plastics found in fish samples, while PE/PA/Nylon, PVA, and PVC were predominant in water samples. PA/Nylon, PUR, PVC, and PET were the most common in sediment samples. Source analysis by Principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the presence of MPs was mainly influenced by local anthropogenic activities. However, estimated daily intakes are generally low, indicating that daily consumption of the samples is not likely to be harmful. The widespread presence of MPs in inland rivers across Nigeria highlights the urgent need for effective waste management strategies and environmental conservation efforts to mitigate plastic pollution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".