|
Description
|
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. (2017-01-01)
***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to https://doi.org/10.1371/journal.pone.0171705 for the original and latest version of the dataset and data downloads*** (2025-12-02)
|
