Researchers at Duke University have developed a method that uses machine learning, satellite imagery and weather data to autonomously find hotspots of heavy air pollution, city block by city block, reports phys.org. The technique could be a boon for finding and mitigating sources of hazardous aerosols, studying the effects of air pollution on human health, and making better informed, socially just public policy decisions. The air pollutants in which the Duke team is most interested are tiny airborne particles called PM2.5. These are particles that have a diameter of less than 2.5 micrometres – about three per cent of the diameter of a human hair – and have a dramatic effect on human health because they can travel deep into the lungs.
https://phys.org/news/2021-04-ai-local-pollution-hotspots-satellite.html
