Emerging contaminant exposure to aquatic systems in the Southern African Development Community
Bibliographic record
Abstract
The growing production and use of chemicals and the resultant increase in environmental exposure is of particular concern in developing countries where there is rapid industrialization and population growth but limited information on the occurrence of emerging contaminants. Advances in analytical techniques now allow for the monitoring of emerging contaminants at very low concentrations with the potential to cause harmful ecotoxicological effects. Therefore, we provide the first critical assessment of the current state of knowledge about chemical exposure in waters of the Southern African Developmental Community (SADC). We achieved this through a comprehensive literature review and the creation of a database of chemical monitoring data. Of the 59 articles reviewed, most (n = 36; 61.0%) were from South Africa, and the rest were from Botswana (n = 6; 10.2%), Zimbabwe (n = 6; 10.2%), Malawi (n = 3; 5.1%), Mozambique (n = 3; 5.1%), Zambia (n = 2; 3.4%), Angola (n = 1; 1.7%), Madagascar (n = 1; 1.7%), and Tanzania (n = 1; 1.7%). No publications were found from the remaining seven SADC countries. Emerging contaminants have only been studied in South Africa and Botswana. The antiretroviral drug ritonavir (64.52 µg/L) was detected at the highest average concentration, and ibuprofen (17 times) was detected most frequently. Despite being the primary water source in the region, groundwater was understudied (only 13 studies). High emerging contaminant concentrations in surface waters indicate the presence of secondary sources of pollution such as sewage leakage. We identify research gaps and propose actions to assess and reduce chemical pollution to enable the SADC to address the Sustainable Development Goals, particularly Goal 3.9, to reduce the deaths and illnesses from hazardous chemicals and contamination. Environ Toxicol Chem 2022;41:382-395. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".