A comprehensive review on current technologies for removal of endocrine disrupting chemicals from wastewaters
Bibliographic record
Abstract
In the recent years, endocrine disrupting compounds (EDCs) has received increasing attention due to their significant toxic effects on human beings and wildlife by affecting their endocrine systems. As an important group of emerging pollutant, EDCs have been detected in various aquatic environments, including surface waters, groundwater, wastewater, runoff, and landfill leachates. Their removal from water resources has also been an emerging concern considering growing population as well as reducing access to fresh water resources. EDC removal from wastewaters is highly dependent on physicochemical properties of the given EDCs present in each wastewater types as well as various aquatic environments. Due to chemical, physical and physicochemical diversities in these parameters, variety of technologies consisting of physical, biological, electrochemical, and chemical processes have been developed for their removal. This review highlights that the effectiveness of EDC removal is highly dependent of selecting the appropriate technology; which decision is made upon a full wastewater chemical characterization. This review aims to provide a comprehensive perspective about all the current technologies used for EDCs removal from various aquatic matrices along with rising challenges such as the antimicrobial resistance gene transfer during EDC treatment.
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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) | 0.008 | 0.003 |
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".