Occurrence, removal, and environmental impacts of emerging contaminants detected in water and wastewater in Southern Ontario—Part I: occurrence and removal
Bibliographic record
Abstract
A comprehensive study was conducted at two wastewater treatment plants (WWTPs) and one water treatment plant (WTP) in Windsor, Ontario, Canada. The occurrence of 220 emerging and legacy compounds, their removal efficiencies by the existing treatment processes, and their potential environmental impacts were studied. The results are reported in a two part paper. In this part (I), the occurrence and removal efficiencies are presented. Three of the 47 target pharmaceutically active compounds (PhACs) and endocrine disrupting compounds (EDCs) contributed 89–96% of the total concentration of PhACs/EDCs in the WWTP influents. They were acetaminophen, ibuprofen, and naproxen. The existing treatment processes successfully removed between 95 and 98% of ‘all’ PhACs/EDCs, primarily due to the high removal rates of these three analgesics. Concentrations of PhACs/EDCs detected at the WTP intake were two to three orders of magnitude lower than those in the effluent of the upstream WWTP. These concentrations remained relatively unchanged in the finished drinking water, indicating the WTP's low removal efficiency for trace amounts of them. Polybrominated diphenyl ethers (PBDEs) were detected at concentrations as high as 150 ng/L (for PBDE-209) in the WWTPs’ influent, and removed at 86–96% efficiency. PDBE effluent concentrations were mostly below 1 ng/L at both WWTPs, with a maximum of 9 ng/L for PBDE-209. Octylphenol, nonylphenol, and nonylphenol ethoxylates concentrations were monitored in one WWTP's effluent, and ranged between undetectable and 286 ng/L (LoDs varied between 1.3 and 15.2 ng/L).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".