Advancement in treatment of wastewater: Fate of emerging contaminants
Bibliographic record
Abstract
The scarcity of industrial and domestic use water has become an important issue as industrial operations and localized pollution have burgeoned over the last decade. Wastewater (WW) treatment for recycling and reuse is gaining importance as an alternate source of water supply to circumvent water shortages. Wastewater treatment requires thorough planning, design, construction, and management of treatment facilities in order to discharge the treated water to the aquatic environment or for recycling and reuse. One detrimental effect of growing populations and urbanization has been the release of many persistent emerging contaminants (ECs) to the environment, mainly detected in WW. The entry of these ECs to the aquatic environment through wastewater treatment plants (WWTPs) may cause different ecological risks such as reproductive toxicity, endocrine disruption, and microbial resistance. The quantification of ECs (in ng or pg level) in complex matrices, such as WW samples recognized through non‐target screening approaches, has played a key role in the planning and design of water treatment facilities. The purpose of this review is to provide information about advancements in wastewater treatment technologies such as constructed wetland (CW) and advanced oxidation processes (AOPs) and the fate of emerging contaminants during these treatments. Further, this review also reports the ecological effects of these contaminants and their by‐products formed during various advanced WW treatment processes. The review also discusses advancements in different analytical techniques for the analysis of ECs in WW.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".