Vulnerability assessment of drinking water intakes to microbial contamination during combined sewer overflows under global change: A bottom-up approach
Bibliographic record
Abstract
Combined sewer overflows (CSOs) are a major cause of microbial contamination in urban rivers, especially during summer low flow periods. This study analyzes the vulnerability of drinking water intakes (DWIs) to CSO-derived microbial contamination in an urban river located in Quebec, Canada, under global change. The vulnerability assessment of DWIs was based on the Escherichia coli (E. coli) concentrations and conducted using a novel bottom-up approach. Unlike the traditional top-down approach, the bottom-up approach incorporates a wide range of climate information sources, including historical data, stochastic climate simulations, and outputs from General Circulation Models, without the need for extensive recalibration or reliance on downscaled models. It also allows local capacities and system-specific factors to be taken into account, providing a more adaptable framework for regions with limited data. E. coli concentrations from CSOs were generated stochastically, while hydrographs were generated by a deterministic method. A hydrodynamic and water quality models were used to investigate the impact of simultaneous overflows, their duration, E. coli concentrations, and peak overflow and river flow. The study revealed a significant impact of simultaneous overflows on the mean and maximum simulated E. coli concentrations at DWIs, particularly during extended CSO durations and with higher discharged E. coli concentrations. The extreme-low river flow rates significantly increased mean and peak E. coli concentrations, altering the pollutograph shape at DWIs. Future climate projections indicate a decrease in summer low flows, potentially exacerbating the vulnerability of water sources to CSO contamination. Source water protection plans need to consider vulnerable periods, characterized by reduced contaminant dilution alongside high numbers of simultaneous overflows, high contaminants concentration, and prolonged durations. The bottom-up approach proposed in this study can be applied in jurisdictions with limited data and covers a range of potential risks using probabilistic scenarios, including extreme scenarios, without using a hydroclimatic model.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".