Geospatial Open Data for Clean Air: A Reproducible Machine-Learning Framework for National Pollution Assessments_拉斯维加斯赌城

Institute of Geography
2026-03-23T15:04:43+00:00
Universit?t Augsburg

A new study by scientists and master's students at the University of Augsburg fills gaps in air monitoring. Using machine learning and open geospatial data, they found that although NO? levels are falling, 97% of monitoring stations still exceed WHO guidelines. This open-source project makes important environmental data accessible to everyone, supporting global efforts for clean air.