In Lake Erie, harmful algal blooms (HABs) typically begin as nutrient-rich water from the Maumee River drains into the warm, shallow western part of the lake. This creates ideal conditions for the growth of a type of algae that creates a toxin called “microcystin.” In 2014, such a bloom caused the microcystin levels in Toledo tap water to exceed what is recommended by the World Health Organization, triggering a two-day “Do Not Drink” advisory.
Thanks to a grant through the IOOS Ocean Technology Transition project, GLOS is partnering with NOAA Great Lakes Environmental Research Laboratory and National Centers for Coastal Ocean Science, LimnoTech, The Ohio State University, Cleveland Water Alliance, Cooperative Institute for Great Lakes Research, and RPS Group to build an early warning system that will help to address what is increasingly becoming a regional health and safety concern.
The project aims to create a system that will keep people informed when portions of the lake develop harmful algal blooms (HABs). This is possible by bringing together live data from forecasts and a network of in-water sensors and other monitoring equipment, processing that data, and sending actionable text message alerts when conditions worsen. By making high quality, live information readily available, decision makers in the western basin can better anticipate HABs and react more effectively.
A network of sensors relays real-time information on where algal blooms form. See more.
At the core of the system is a network of sensors or “sondes” distributed through the at-risk areas. This network has expanded as more water treatment facilities deploy sensors, many purchased with project funding. While helpful in detecting algae that could produce toxins, this sensor network cannot answer the question, “Is the water toxic right now?”
Environmental sample processors or ESP’s from NOAA can help to answer this. An ESP, or “lab in a can,” automatically tests water for toxins, a process that, until recently, had to be performed by hand. This means that samples can be taken more frequently and increases the accuracy of harmful algal bloom predictions. When added to the network, this data will help enrich information sent via alerts.
A third important piece of the system is the NOAA Lake Erie HAB forecast which predicts conditions up to three days in advance.
By combining these disparate data sets, alerts can become a high-value, unified decision support tool that actively alerts users when they need to pay close attention to changing conditions, freeing them from continually monitoring multiple information sources.
Currently in the third year of the project, the team is focusing on the continued development of the sensor network and the backend technology necessary to receive and process multiple sources of data. This will move the system, currently in prototype stage, towards being operational.
Photos: (Top Left) Satellite imagery captured on Aug. 1, 2014 shows the harmful algal bloom that caused a two-day shutdown of Toledo’s municipal water supply. NASA image courtesy Jeff Schmaltz, LANCE/EOSDIS MODIS Rapid Response Team at NASA GSFC link | (Top Right) NOAA GLERL staffer submerges the ESPniagara during a test deployment in June of 2016. Courtesy of NOAA Great Lakes Environmental Research Laboratory.
Partners and Collaborators
- NOAA Great Lakes Environmental Research Lab
- NOAA National Centers for Coastal Ocean Science
- The Ohio State University
- Cleveland Water Alliance
- Cooperative Institute for Great Lakes Research
- RPS Group
More on Funding
Funding for this project has been provided by the U.S. IOOS Ocean Technology Transition Project. This national project sponsors the transition of emerging marine observing technologies, for which there is an existing operational requirement and a demonstrated commitment to integration and use by the ocean and Great Lakes observing community, to operational mode.
Want to get in touch? Contact Becky Pearson at firstname.lastname@example.org