Characterization of CSO Microbial Contamination and their Risks to Drinking Water Sources
Bibliographic record
Abstract
Combined sewer overflows (CSOs) have been recognized as one of the major causes of surface water quality impairment, particularly drinking water supplies.Occurrence of CSOs as a discharge of an untreated mixture of wastewater effluents and stormwater into waterbodies upstream of drinking water sources may potentially deliver high amounts of fecal loads to downstream intakes, causing periods of elevated concentrations.The CSO-induced peak periods at the intakes must be characterized with regards to the level of microorganisms during these periods while treatment processes in drinking water treatment plants must effectively reduce concentrations by the required amounts.Microbial risk estimates based on concentration measurements may fail to include the peak events associated to discharge events due to the insufficiently frequent raw water quality sampling procedures.Microbiological-related impacts of CSOs at drinking water intakes are influenced by upstream loading conditions and transport process to the point of intakes.Given high inter and intra-event variability of CSO discharges, identifying their dynamic behavior and the corresponding loading characteristics remains a challenging task.Of the shortcomings of existing CSO load models, is that they do not reflect the variability of the event parameters to project a range of probable CSO loading conditions, instead of fixed loading estimates in course of events, or are too detailed to apply for the large numbers of CSOs upstream of intakes.As one of the requirements in source water protection for safeguarding source waters and public health, assessment of drinking water intakes with regards to microbial contamination should address the CSO microbiological contributions in terms of short-term (i.e.daily) and long-term (i.e.annual) risks.The main objectives of this project were to develop a CSO loading model that not only takes into account the variability of CSO discharges in terms of flowrate and concentration in a deterministicprobabilistic manner but is scale-adjustable to model CSO discharges of different scales.The model is to be used to quantify the CSO associated microbial risk of drinking water supplies with upstream CSOs.This approach can be incorporated into drinking water intakes' vulnerability and threat assessment through application of a hydrodynamic and water quality model combined with Quantitate microbial Risk Assessment (QMRA).A river located along the Quebec-Ontario provincial boundary line was considered as the case study.The river receives CSO discharges from the Quebec side, downstream of which two municipalities from two provinces use the river water for their drinking water treatment plants.This case study also provides a chance to investigate the
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".