Researchers have used the capabilities of machine learning technology to predict what types of inorganic pollutants are likely in specific groundwater supplies. It could help public health officials determine which aquifers should be prioritized for water quality safety testing. The new method promises to provide a path to advanced proactive water safety measures throughout the world, says Paul Westerhoff an ASU Regents’ Professor in the School of Sustainable Engineering and the Built Environment, part of the Fulton Schools. Westerhoff and Andreas Spanias, a professor in the School of Electrical, Computing and Energy Engineering, also part of the Fulton Schools, are coauthors of a report on the process in the Environmental Science & Technology journal.