According to the United Nations Environment Programme, plastic pollution in oceans is a major environmental issue and a top pollution challenge. Although most ocean plastic comes from rivers, plastic pollution in lakes and rivers has received less attention.
Researchers aim to change this focus. Previous methods for removing plastic waste have been labor-intensive, time-consuming, and expensive.
To help with those challenges, researchers at the University of Minnesota Twin Cities have used remote sensing technology to monitor plastic debris in rivers and lakes. This first-of-its-kind study shows how remote sensing can help monitor and remove plastic debris from freshwater environments like the Mississippi River.
The technology uses spectral reflectance properties, or specific wavelengths in the electromagnetic spectrum, to identify different types of plastic. By detecting the unique wavelength of plastic materials, the technology can distinguish them from natural materials in freshwater, like seaweed, sediments, driftwood, and water foam.
The St. Anthony Falls Laboratory is situated on Hennepin Island in the Mississippi River in Minneapolis. The river runs through the lab’s space, allowing the researchers to test their theory using actual conditions of the river.
They used a spectroradiometer and a DSLR camera to monitor and classify different types of debris based on their spectral signatures. This method could help effectively remove plastic waste.
The researchers believe that developing this technology in Minnesota, at the headwaters of the Mississippi, could help prevent plastic pollution from reaching downstream states and the ocean. As plastics spread, controlling them becomes increasingly difficult.
The researchers hope to continue this on a larger scale to increase their understanding of where this plastic debris comes from, how it moves across river systems, and how they can remove it.
Journal Reference:
- Olyaei, M., Ebtehaj, A. & Ellis, C.R. A Hyperspectral Reflectance Database of Plastic Debris with Different Fractional Abundance in River Systems. Sci Data 11, 1253 (2024). DOI: 10.1038/s41597-024-03974-x