Microplastics in African ecosystems: Current knowledge, abundance, associated contaminants, techniques, and research needs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite Africa ranking top in mismanaged plastic waste, there is insufficient data on the extent of microplastics and its interaction with other contaminants in its ecosystems. Microplastics pollution has been documented globally, however, specific data from the continent is crucial for accurate risk assessment and to drive policies. We critically reviewed 56 articles from 1987 to 2020 and provide an overview of the current knowledge of the abundance and distribution of microplastics and associated contaminants in African aquatic systems and organisms. Most of the studies were carried out in the marine environment and there is currently no available data on the abundance of microplastic pollution in the African terrestrial environment. We show that across all studies, 5-100% of all sampled aquatic organisms contained microplastics. Concerning high levels of microplastics were reported in fish from Egypt compared to other parts of Africa and the world. Across all persistent organic pollutants sampled in microplastics, hopanes and phthalates were present at high concentrations while sodium and zinc were high relative to other trace metals reported. The most frequently occurring plastics were polyethylene followed by polypropylene and polystyrene. We found that most of the studies relied on visual inspection (52%) > FTIR (38%) > Raman spectroscopy (5%) > Scanning electron microscopy (3%) > Differential scanning calorimetry (2%) for identifying microplastics. Major gaps in sampling and identification techniques which may have overestimated or underestimated the current levels were identified. We discuss other research priorities and recommend solutions to address these issues associated with microplastic pollution in Africa.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it