Artificial sweeteners in wastewater treatment plants: A systematic review of global occurrence, distribution, removal, and degradation pathways
Bibliographic record
Abstract
The widespread use of artificial sweeteners in foods, drinks, and pharmaceuticals has led to rising concentrations in wastewater, with specific sweeteners raising concerns due to demonstrated toxicological risks to ecosystems and humans. To date, a comprehensive summary of the occurrence, distribution, and removal status of artificial sweeteners in wastewater treatment plants (WWTP) is lacking, making it difficult to evaluate the associated risks and environmental impacts. We conducted a systematic review of scientific literature and grey literature with rigorous screening covering 24 countries and 6 continents. Globally, sucralose, acesulfame, saccharin, and cyclamate are prevalent artificial sweeteners in WWTP, with concentrations of 0.6-303.0 µg/L in influent and 0.1-81.2 µg/L in effluent. Sucralose showed obvious increasing concentrations over time in wastewater in the United States and Canada, with an increase of 5.6-5.7 µg/L·y in influent and 4.7-5.5 µg/L·y in effluent. Summer wastewater usually contains 11.1-33.3 % higher concentrations of artificial sweeteners than other seasons. Saccharin and cyclamate are the most easily removable sweeteners (>90.0 % removal) in WWTP, followed by acesulfame (25.0-70.1 %) and sucralose (-10.0-10.0 %). Wastewater treatment processes with longer HRT and more diverse microbial communities showed better performance in sucralose removal, while processes with aerobic conditions showed better performance in acesulfame and saccharin removal than anaerobic processes. Increasing trends for persistent sucralose and acesulfame removal have been observed globally, suggesting potential microbial evolution/adaptation. This review contributes to a comprehensive understanding of the spatiotemporal distribution and ever-evolving biodegradation of artificial sweeteners in WWTP, providing future perspectives and potential policy requirements.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".