Selected emerging contaminants in water: Global occurrence, existing treatment technologies, regulations and associated risk
Bibliographic record
Abstract
Emerging contaminants (ECs) in aquatic environments have recently attracted the attention of researchers due to their ubiquitous occurrence and the potential risk they may pose to life. While advance analytical methods have improved global reporting in water matrices, additional information is needed to compile data on their occurrence, existing legislation, treatment technologies and associated human health risks. Therefore, the present study provides an overview of the occurrence of selected ECs, including personal care product, antibiotics, NSAIDs, EDCs and psychiatric drugs, the existing regulatory framework and their toxicological effects on human health. The water matrices under review are the treated wastewater, surface water, groundwater and, in a few cases, drinking water. The study also highlights different treatment technologies available, and evaluates their performance based on the removal efficiency for different classes of ECs. For removal of almost all ECs considered, ozonation integrated with gamma radiation was reported highly efficient. Risk analysis was also performed for selected ECs including diclofenac, ibuprofen, naproxen, carbamazepine, estrone, 17 β-estradiol, bisphenol A, sulfamethoxazole, erythromycin and triclosan. The human health risk analysis indicated the highest number of locations with potential risk due to the EDCs, with South America, Europe and Asia having multiple risks due to estrone and Bisphenol A. The results of this study will give a better insight into the current situation of ECs in the global water matrices, the performance assessment of treatment technologies and the risk analysis will describe the need for more robust regulatory structures around the world to prevent the occurrence of such contaminants in the aquatic environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".