Researchers have created a machine learning framework that can predict high-risk groundwater sites likely to contain harmful inorganic pollutants, allowing for more targeted water quality monitoring. Using extensive water quality data from North Carolina and Arizona spanning over 140 years and 20 million data points, the model can estimate the presence of pollutants based on limited available data. Initial results suggest that pollutants may exceed safety standards in more areas than previously identified, with the model indicating a much lower percentage of truly safe sites compared to traditional testing. This predictive tool, expected to improve with more diverse data inputs, offers the potential for better allocation of testing resources and supports efforts to manage groundwater safety and phosphorus sustainability across ecosystems.
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