Seeing Green (and Predicting It)

By Dustin Renwick

Thick mat of green algae at the shore of a lakeWhen you live near a coast, summer means beaches. A relaxing inland getaway often involves the cool waters of a lake.

Except when the shoreline turns green.

Sometimes a mat of algae clogs fishing lines. Other times, lake foam makes the swimming area appear as if someone poured in a few St. Patrick’s Day green beers.

Those algae serve a natural function in the ecosystem. Yet an icky, slimy scene can ruin plans for a day on the water when conditions – generally warm, stagnant water rich in excess nitrogen, phosphorus,or other nutrient pollution – sparks rapid growth.

Ross Lunetta, EPA research physical scientist, leads a team of research and application scientists who proposed a Pathfinder Innovation Project to validate a new algorithm that uses satellite data for predicting algal blooms in freshwater systems.

Specifically, the team’s project targets cyanobacteria, known to make humans and animals sick with symptoms such as respiratory distress and skin rashes. On the basis of algae cell counts, more than a quarter of lakes nationwide have enough cyanobacteria for moderate to high risk according to the most recent National Lakes Assessment Report in 2009.

Tallying the density of cyanobacteria cells in a water body can provide an estimate of potential exposure risk. But sampling more than a handful of the nation’s lakes can be costly and slow. Plus, current satellite data and its analysis fall short.

Existing field measurement programs were not designed to provide data that researchers can readily use to calibrate and validate satellite-based observations. And satellites can’t discriminate between the sizes or the many species of cyanobacteria, some of which don’t produce toxins.

“It’s not necessarily the same species in Maine as it is in Florida,” Lunetta said. “These things can be very different in size.”

Not knowing the cell volume, which is species specific, makes calculations of blooms cell counts, impacts, and risks a challenge.

The team is working with TopCoder, an online community to bring in outside expertise and innovation through competitions and challenges and expand the search for solutions.

TopCoder represents nearly a half million software developers and algorithm specialists. The company’s process breaks large challenges into small chunks that can be coded, developed, or designed individually. Then TopCoder stacks all the pieces back together into a finished solution.

Imagine a neighborhood full of tinkerers, parts collectors and coding whiz-kids who could gather at your garage to diagnose and fix your car when the “check engine light” flashed. Each person could solve a problem within his or her specialty, and the cohesive result benefits from these specific skills.

The team’s predictive algorithm will take a few more months to design and even longer to validate, but the potential benefits are clear. Water forecasts and public health officials could alert anyone who might consider a day at the lake, and researchers could focus their efforts.

“If you have limited resources, and you can only collect five samples but you have 50 lakes,” Lunetta said, “you can pick the ones the model tells you will most likely become a problem.”

About the author: Dustin Renwick works as part of the innovation team in the EPA Office of Research and Development.

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