Occurrence and partitioning behavior of E. coli and wastewater micropollutants following rainfall events
Bibliographic record
Abstract
The fate of indicator bacteria and wastewater micropollutants (WWMPs) discharged from water resource recovery facilities (WRRFs) and combined sewer overflows (CSOs) needs to be understood for the protection of drinking water sources. This study investigated the partitioning behavior in wet and dry weather of Escherichia coli and 6 WWMPs including acetaminophen (ACET), caffeine (CAF), theophylline (THEO), carbamazepine (CBZ), 2-hydroxycarbamazepine (CBZ-2OH), and the artificial sweetener aspartame (ASP) between the water, suspended particulate matter (SPM), and sediments. ACET, CAF, THEO and CBZ were detected in all matrices. In contrast, CBZ-2OH and ASP were strongly associated with suspended particles, being exclusively detected in SPM and sediments. When comparing dry to wet weather concentrations, three main patterns were observed depending on the compounds and CSO locations: (i) compounds with low removal efficiency by wastewater treatment (CBZ, CBZ-2OH and ASP) are diluted with the addition of stormwater in the aqueous and SPM phases; (ii) contaminants with high removal efficiency ( E. coli , ACET, CAF, THEO) saw an increase in their particle associated fraction (39 to 59% for E. coli , and from 12%–43% to 42%–91% for WWMPs); and (iii) no significant difference was observed for surficial sediment concentrations after rainfall events, although spatial variability was observed. Results highlight the importance of monitoring the SPM phase to overcome dilution effects in the dissolved phase. Analyzing SPM with regards to micropollutants and indicator bacteria may be more relevant for risk assessment and the assessment of the relative contribution of CSO bypass flows and treated wastewater effluents loads.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".