Microplastic pollution in aquatic environments in Africa: status and research opportunities
Bibliographic record
Abstract
The global demand for plastics has increased majorly because of their versatility, light weight, strength and cost–effectiveness. Africa is ranked top in mismanagement of plastic waste, resulting in the plastic problem in the environment. Nevertheless, plastics produce microplastics through degradation and fragmentation of plastic debris largely from anthropogenic sources. Microplastics have become ubiquitous in the natural environment, and the terrestrial environment is the major source. The propensity of microplastics to adsorb and concentrate persistent organic pollutants (POPs) provides potential health effects in the different trophic levels of organisms in both aquatic and territorial environments. Thus, the fate of microplastics is increasingly becoming a global concern. Despite the numerous global studies on the impact of microplastics in the environment, there are insufficient data available on the occurrence and distribution of microplastics and associated health effects in aquatic ecosystems in Africa. The reviewed research articles from 2000 to 2021 provide a summary of the current knowledge on the occurrence and distribution of microplastics, analytical approaches used to detect and quantify microplastics, associated health effects and mitigation measures through government policy to ban plastic use in Africa. The findings presented provide a platform for future research to focus on the associated effects of adsorbed and concentrated POPs on microplastics in aquatic environments in Africa. With the evidence presented, policymakers will make more informed decisions on the future of plastics in Africa. The authors recommend improving information and expanding knowledge through research on the fate and potential ecological impact of microplastics in aquatic environments in Africa.
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 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 | 0.002 | 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 | 0.000 | 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 teacher head, 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".