AI Model Improves Air Pollution Monitoring by Fusing Satellite and Ground Data

Researchers developed PollutionNet, a Vision Transformer framework that combines satellite imagery with ground sensors to measure NO₂ and SO₂ pollution with 14% higher accuracy than existing methods. The approach addresses gaps in traditional monitoring by capturing complex pollution patterns, offering scalable assessments for regions lacking dense sensor networks.