Global Assessment of Emerging Contaminant Removal in Wastewater Treatment Plants: In Silico Hazard Screening and Risk Evaluation
Bibliographic record
Abstract
Pharmaceuticals and personal care products (PPCPs) are emerging contaminants (ECs), whose presence in the environment is of increasing concern due to their widespread use and possible detrimental effects on wildlife and humans. These chemicals may present multiple hazardous properties such as environmental persistence, toxicity, high mobility, and the potential for bioaccumulation. In this study, extended bibliographic research was conducted to characterize the removal efficiency (RE) of PPCPs in wastewater treatment plants (WWTPs) considering different technologies. Measured values of RE were collected from the literature or calculated for 251 compounds. The molecular structure of the 245 PPCPs were used as the input to generate predictions of multiple properties using several QSAR tools, such as the OECD Toolbox, OPERA, EPI Suite™, and QSAR-ME Profiler. These predictions were compared to regulatory thresholds to identify hazardous chemicals and to screen persistent, mobile and toxic (PMT) or persistent, bioaccumulative and toxic (PBT) substances. Finally, chemicals were prioritized by combining values of RE and QSAR predictions for multiple properties. A total of 16 out of the 245 molecules were prioritized as the most hazardous compounds to the aquatic environment and, among these, six were associated with potential risk due to their exposure concentrations reported in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".