Exploring the Relationship between Effect-based Analysis, Emerging Pollutants, and Conventional Water Quality Parameters in a Threatened Coastal Aquifer
Bibliographic record
Abstract
The presence of emerging contaminants (ECs) in groundwater reserves is a growing concern globally. Despite this reality, limited data exists describing EC-associated biological activity in aquifers and the associations between EC burdens and conventional water quality parameters including chemistry and microbiology. The Cape Flats Aquifer (CFA) is subject to various sources of contamination (industrial, residential, and agricultural). To better understand the spatiotemporal patterns within this area, an extensive water quality index (WQI) was developed, incorporating 24 parameters measured over 98 boreholes within a five-year period (2018 – 2021, and 2023). Upon the identification of several contamination hotspots, a battery of effect-based methods (EBMs) were applied to screen groundwater for endocrine disruptive (i.e., estrogenicity- and androgenicity), and aryl hydrocarbon receptor activation over two seasons. An in vivo fish embryo toxicity (FET) assay was furthermore applied to screen for embryotoxicity and teratogenicity. Analytical chemistry was performed in parallel with the EBMs to quantify a selection of ECs including pharmaceuticals, pesticides, and personal care products. A subsequent pollution source delineation principal component analysis (PCA) using the WQI parameters showed that a mix of agricultural, geogenic, and residential sources contribute to the poor water quality. Further information could be deduced with the incorporation of the EBMs data, where certain sites had significant endocrine disruptive potential. We conclude that caution should be taken when abstracting water from areas that were identified as pollution hotspots and that efforts should be taken in curtailing some of the activities contributing to the deteriorating groundwater quality. Finally, we show that EBMs can be a valuable tool in combination with traditional water parameters to provide a more comprehensive analysis of at-risk groundwater sources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".