For a new study, the researchers used a machine learning algorithm to classify more than 110 types of plastics, including commercial and lab-made varieties, to better understand how they might degrade in the ocean, says Robert Mathers, professor of chemistry at Penn State University.

There are more than 150 million metric tons of plastic in the ocean, with 8 million metric tons more entering the ocean each year, according to the Ocean Conservancy.

A number of factors in the ocean can help break down this plastic, including ultraviolet radiation from the sun, wind, waves, seawater, water temperature, and bacteria, the researchers say.

They found that certain types of plastics did break down quicker than others when subjected to these conditions.

While knowing the molecular structure of the more susceptible plastics could give engineers a chance to develop plastics with less environmental impact, the economics of producing those plastics at scale would still be an issue, Mathers says.

There are so many types of plastics and so many experimental conditions, machine learning became instrumental in helping the researchers both sort through the large amount of data, as well as classify that information, says Joseph Cuiffi, an assistant teaching professor who worked with Mathers.

“I think that the modern tools available for data analysis allow us to explore large varied data sets easier than ever before,” he says. “I also appreciate interdisciplinary efforts in this field, with this study for example, because external researchers can look at the data agnostically.

Read the full article about machine learning and the ocean plastic problem by Matthew Swayne at Futurity.