Emerging pollutants of water supplies and the effect of climate change
Bibliographic record
Abstract
Emerging pollutants (EPs) are the contaminants of concern in water systems. These are mainly due to anthropogenic activities and are not always removed during water treatment, eventually affecting the quality of water supply systems (WSSs). These pollutants vary from organic pollutants such as pharmaceuticals, pesticides, and flame retardants to inorganic pollutants, like heavy metals or illicit drugs. Currently, there is a lack of adequate research on the chronic health effects of these pollutants. In addition, climate change plays a role in immobilizing these pollutants. Consequently, considering the increasing effects visible in many countries, the rising levels of contaminants have strained the effectiveness of water treatment facilities. As water supply is an essential service to communities, it is necessary to ensure the reliability against risks of EPs. Therefore, it is important to protect the health and safety of consumers. Climate change has brought threats to water infrastructures, like many others. This review examined the effect of climate change on EPs in water supplies by providing a detailed review of the contaminants, evaluating their potential toxicity, and determining the appropriate water treatment technologies. The role of the WSS in EP immobilization was examined in addition to the potential effect of climate change. This review of different critical and relevant literature aids with the identification of current research gaps. Based on the identified gaps, a research framework was developed for the future investigation of EPs.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".