Optimal sampling locations to reduce uncertainty in contamination extent in water distribution systems

Area of Science:

• Environmental Engineering
• Water Resource Management
• Public Health

Background:

• Drinking water utilities depend on distribution system sampling for water quality assurance.
• Sampling is crucial for identifying contamination sources and extent during incidents.
• Water distribution system models face challenges due to uncertainties in contamination scenarios.

Purpose of the Study:

• To develop an optimization framework for identifying strategic sampling locations in water distribution systems.
• To enable rapid determination of contamination extent under various uncertain scenarios.
• To address the challenge of uncertainty in contamination parameters like location, amount, and duration.

Main Methods:

• An optimization framework was developed to identify optimal sampling locations.
• The framework considers multiple contamination scenarios and their uncertainties.
• Formulations were designed to solve for multiple sampling locations simultaneously and efficiently.

Main Results:

• The framework successfully identifies strategic sampling locations.
• It efficiently handles large systems and extensive uncertainty spaces.
• Demonstrated effectiveness through two case studies.

Conclusions:

• The proposed optimization framework enhances the ability to quickly assess contamination extent in water distribution systems.
• It provides a robust method for selecting sampling locations under significant scenario uncertainty.
• This approach is scalable and efficient for real-world water system management.