L2M – AI-Enable Regional Water Quality Assessment with Downscaled Climate Impact

This project is fundamentally about business development and identifying user needs in the face of climate change, a critical issue that poses significant threats to urban water infrastructure resiliency. The focus is on the anticipated challenges caused by changing precipitation patterns, increasing temperatures, and rising demand leading to freshwater scarcity and declining quality. Such climate variations are altering water flow, intensifying surface runoff, and impacting biogeochemical processes, all of which influence the quality of drinking water sources by altering contaminant dilution and transportation. Additionally, changes in watershed ecology and soil characteristics due to climate change are expected to affect contaminant transport and loads in source waters. These evolving conditions pose substantial challenges to existing drinking water treatment systems and infrastructure. Fluctuations in factors such as particulate concentrations, types of particulates, natural organic matter, pH, metal presence, and nitrogen and phosphorous levels significantly affect treatment efficiency, costs, and the quality of distributed water. To address these challenges, this project aims to employ advanced artificial intelligence (AI) techniques to predict and analyze water quality changes resulting from climate variability. Ensuring water quality is not just a matter of public health, but is also essential for economic stability and environmental sustainability, making it a key focus area for business development and understanding user needs.

Faculty Supervisor:

Nicolás Peleato

Student:

Partner:

I-INC Foundation for Business Development

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

The University of British Columbia - Okanagan

Program: