Biomonitoring of surface water quality in Africa: contamination levels by chemical pollutants, including emerging pharmaceutical compounds and heavy metals, and the use of biomarkers – a literature review
Bibliographic record
Abstract
Chemical pollution of surface waters represents a major challenge in Africa, particularly in urban and industrialized areas. The use of biomarkers to monitor this pollution appears to be an effective approach for assessing water quality and its impacts on aquatic ecosystems. This literature review aims to enhance knowledge on the application of biomarkers in monitoring chemical pollutants, particularly emerging pharmaceutical compounds (EPCs) and heavy metals in Africa. An analysis of 50 scientific articles published between 2010 and 2025 was conducted. The results show that biomarkers can detect the effects of chemical pollutants on aquatic organisms. An increase in enzymatic activity was observed in fish exposed to contaminants, reflecting a biological response to chemical stress. Furthermore, oxidative stress biomarkers prove particularly useful for assessing the impact of pollution by measuring free radicals and antioxidants in fish tissues. The integration of biomarkers into chemical pollution monitoring in Africa is a powerful tool for assessing water quality and the impact of human activities on aquatic ecosystems. This data provides a solid foundation for guiding future research and strengthening water quality management policies at the continental level.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".