A machine learning approach to freshwater analysisFrom protecting biodiversity to ensuring the safety of drinking water, the biochemical makeup ...
Published on by Water Network Research, Official research team of The Water Network
From protecting biodiversity to ensuring the safety of drinking water, the biochemical makeup of rivers and streams around the United States is critical for human and environmental welfare. Studies have found that human activity and urbanization are driving salinization (increased salt content) of freshwater sources across the country. In excess, salinity can make water undrinkable, increase the cost of treating water, and harm freshwater fish and wildlife.
Along with the rise in salinity has also been an increase in alkalinity over time, and past research suggests that salinization may enhance alkalinization. But unlike excess salinity, alkalinization can have a positive impact on the environment due to its ability to neutralize water acidity and absorb carbon dioxide in the Earth's atmosphere—a key component to combating climate change. Therefore, understanding the processes at play which are affecting salinity and alkalinity have important environmental and health implications.
A team of researchers from Syracuse University and Texas A&M University have applied a machine learning model to explore where and to what extent human activities are contributing to the hydrogeochemical changes, such as increases in salinity and alkalinity in U.S. rivers.
The group used data from 226 river monitoring sites across the U.S. and built two machine learning models to predict monthly salinity and alkalinity levels at each site. These sites were selected because long-term continuous water quality measurements have been recorded for at least 30 years.
From urban to rural settings, the model explored a diverse range of watersheds, which are areas where all flowing surface water converges to a single point, such as a river or lake. It evaluated 32 watershed factors ranging from hydrology, climate, geology, soil chemistry, land use and land cover to pinpoint the factors contributing to rising salinity and alkalinity. The team's models determined human activities as major contributors to the salinity of U.S. rivers, while rising alkalinity was mainly attributed more to natural processes than human activities.
The team, which included Syracuse University researchers Tao Wen, assistant professor in the College of Arts and Sciences' Department of Earth and Environmental Sciences (EES), Beibei E, a graduate student in EES, Charles T. Driscoll, University Professor of Environmental Systems and Distinguished Professor in the College of Engineering and Computer Science, and Texas A&M assistant professor Shuang Zhang, recently had their findings published in the journal Science of the Total Environment.
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https://phys.org/news/2023-06-machine-approach-freshwater-analysis.htmlTaxonomy
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