Early detection and prediction of harmful algal blooms using low cost, networked IOT sensors and machine learning

EPA Contract Number: 68HERC21C0045
Title: Early detection and prediction of harmful algal blooms using low cost, networked IOT sensors and machine learning
Investigators: Lee, Christopher
Small Business: AquaRealTime, Inc.
EPA Contact: Richards, April
Phase: II
Project Period: April 1, 2021 through March 31, 2023
Project Amount: $396,192
RFA: Small Business Innovation Research (SBIR) - Phase II (2021) Recipients Lists
Research Category: SBIR - Water , Small Business Innovation Research (SBIR)

Description:

Harmful Algal Blooms (HABs) are an increasing problem in waterways all over the world, costing an average of $17 billion in damages each year. HABs have shut down water supplies to entire US cities in recent years.

Blooms occur when nutrient-rich waters stimulate cyanobacterial growth, resulting in unsightly sludges that discolor waterways, rendering them dangerous to humans, livestock and wildlife because of the cyanotoxins released. Cyanotoxins make HABs very costly to clean up; as the algae themselves must be removed, and the water purified before it is safe to drink, enter or even be close to because of the risk of toxin aerosolization by wind-blown spray.

The treatments for HABs are expensive, environmentally damaging and of limited efficacy unless applied early in a bloom. The solution to HABs is early detection/prediction so that action can be taken at the earliest possible moment: reducing costs; environmental and economic damage; and preserving access to clean drinking water.

Floating sensor buoys are the answer, but the current marketplace is crowded with complex sensor platforms that can cost $30,000 per unit and which require specialist knowledge to deploy, use and maintain. These platforms are beyond the means of millions of small and medium sized stakeholders who manage small lakes, reservoirs, ponds or stretches of beach and who are often hardest hit by HABs.

AquaRealTime was founded to provide a turnkey HAB monitoring solution for the small and medium sized stake-holder market, estimated at $900 million worldwide. Our innovative HAB sensor AlgaeTrackerTM, is affordable ($400), 8lbs in weight and can be deployed by a non-specialist in 30 minutes. AlgaeTrackerTM also has an optimized detector suite that is best-in-class for HAB monitoring. And because AlgaeTrackerTM transmits its data wirelessly over the cellular network and is accessed by a web-browser based dashboard, it is convenient and easy to use.

This grant aims to further develop the beta version of AlgaeTrackerTM to make it ready for the marketplace. In addition, we propose a predictive analytics system that will use machine learning to analyze the data collected by all AlgaeTrackersTM and allow us to make HABs predictions 7 to 14 days in advance. If funded it'll create a commercial network of HAB sensors whose data can be sold to US Government agencies, and other entities with an interest in controlling HABs. This will save billions of dollars as treatments happen earlier, cost less and have fewer environmental effects.


SBIR Phase I:

Early Detection and Prediction of Harmful Algal Blooms Using Low Cost, Networked IOT Sensors and Machine Learning  | Final Report