Artificial Intelligence-Based Early-Warning & Mitigation System for Harmful Algal Blooms

Increasing population, urbanization, drought, energy production, agricultural intensity, water reuse, and global temperatures all increase the likelihood of harmful algal and cyanobacterial blooms in the Catawba River Lakes that serve Charlotte Water and Mecklenburg County in North Carolina. While nuisance algal blooms typically cause objectionable taste and odor events in drinking water, cyanobacteria can produce cyanotoxins like microcystin-LR, resulting in harmful algal blooms (HABs) that pose health risks to humans and animals exposed to the contaminated water.

The primary goal of this project is to create new tools using the latest technology and all available sources of data to minimize the impact of HABs on finished water quality. The research objectives are to develop a transferable real-time HAB prediction dashboard for Charlotte Water using artificial intelligence to combine laboratory, field, weather, and satellite data; create a toolbox of HAB mitigation strategies; and implement and evaluate advanced algae sampling technologies including drones and unmanned boats.