Pharmaceutical and personal care products (PPCPs) as endocrine disrupting contaminants (EDCs) in South African surface waters
Bibliographic record
Abstract
Globally, water resources are under constant threat of being polluted by a diverse range of man-made chemicals, and South Africa is no exception. These contaminants can have detrimental effects on both human and wildlife health. It is increasingly evident that several chemicals may modulate endocrine system pathways in vertebrate species, and these are collectively referred to as endocrine disrupting contaminants (EDCs). Although the endocrine-disrupting effect of water pollutants has been mainly linked to agricultural pesticides and industrial effluents, other pollutants such as pharmaceuticals and personal care products (PPCPs) are largely unnoticed, but also pose a potentially significant threat. Here we present for the first time in a South African context, a summarised list of PPCPs and other EDCs detected to date within South African water systems, as well as their possible endocrine-disrupting effect in-vitro and in-vivo. This review addresses other factors which should be investigated in future studies, including endocrine disruption, PPCP metabolites, environmental toxicology, and antibiotic resistance. The challenges of removing EDCs and other pollutants at South African wastewater treatment works (WWTWs) are also highlighted. The need for focused research involving both in-vitro and in-vivo studies to detect PPCPs in water systems, and to delineate adverse outcome pathways (AOPs) of priority PPCPs to aid in environmental impact assessment (EIA), are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".