A team of researchers has 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.