In the 20th century, the disposal of industrial and hazardous waste in the deep ocean near coastlines was a widespread global practice, leaving a legacy of uncertainty about the quantity, location, and contents of dumped materials. The consequences of this historical practice continue to pose risks to marine ecosystems and human health.
Now, new research is using advanced machine learning techniques, combined with the capabilities of automated underwater vehicles, to better characterize these deep-ocean dump sites.
A team of researchers including USGS Research Geologist Jamie Conrad investigated an offshore dumpsite in San Pedro Basin, California, where an estimated 350-700 tons of the insecticide dichlorodiphenyltrichloroethane (DDT) was dumped between 1947 and 1961. DDT is extremely persistent in the environment; its half-life in aquatic environments is around 150 years. It accumulates in the fatty tissues of animals and is concentrated as it moves up food chains, in a process called bioaccumulation. The use of DDT was banned in the U.S. in 1972.
To shed light on the San Pedro dump sites, researchers employed cutting-edge analytical tools to characterize seabed variability and classify bottom types. They mapped and characterized the composition of these uncharted disposal sites, providing critical information for future environmental assessments and management strategies.
Automated underwater vehicles equipped with advanced sensors and imaging technologies played a pivotal role in the study. These vehicles captured high-resolution acoustic backscatter data, which was fed into state-of-the-art machine learning algorithms to determine the types and distribution of debris objects at the site.
The presence of a significant number of debris objects throughout the survey will require further in-depth characterization, including biological and chemical studies in the region, the researchers note. Additional research is also needed to understand how these contaminated sediments may be transported from the dump sites over time.
"Using remotely operated vehicles in conjunction with machine-learning techniques to map and characterize these dumped materials on the seafloor is crucial for developing effective strategies to mitigate potential environmental risks,” said Conrad. “This research represents a significant step forward in our ability to uncover and characterize these deep-water disposal sites."
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