A Review on the Fate of Legacy and Alternative Antimicrobials and Their Metabolites during Wastewater and Sludge Treatment
Bibliographic record
Abstract
Antimicrobial compounds are used in a broad range of personal care, consumer and healthcare products and are frequently encountered in modern life. The use of these compounds is being reexamined as their safety, effectiveness and necessity are increasingly being questioned by regulators and consumers alike. Wastewater often contains significant amounts of these chemicals, much of which ends up being released into the environment as existing wastewater and sludge treatment processes are simply not designed to treat many of these contaminants. Furthermore, many biotic and abiotic processes during wastewater treatment can generate significant quantities of potentially toxic and persistent antimicrobial metabolites and byproducts, many of which may be even more concerning than their parent antimicrobials. This review article explores the occurrence and fate of two of the most common legacy antimicrobials, triclosan and triclocarban, their metabolites/byproducts during wastewater and sludge treatment and their potential impacts on the environment. This article also explores the fate and transformation of emerging alternative antimicrobials and addresses some of the growing concerns regarding these compounds. This is becoming increasingly important as consumers and regulators alike shift away from legacy antimicrobials to alternative chemicals which may have similar environmental and human health concerns.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".