Researchers create automatic method to detect air pollution hotspots It's an algorithm that can help tackle air pollution from existing satellite data. Imagine there could be a way to autonomously identify hotspots of heavy air pollution, city block by city block. Governments could then detect problem areas and develop targeted measures and achieve optimal results. Researchers at Duke University have developed just that: a method that uses machine learning, satellite images, and weather data to track localized PM2.5 pollution. Image credit: Flickr / UN Air pollution is by far one of the most severe environmental problems, on all scales from local to global. Exposure to fine particulate matter (also known as PM2.5) has wide-ranging adverse health effects on human health, with adverse effects on cardiovascular, cardiopulmonary, and respiratory wellness, to list just a few problems. It can lead to higher risks of mortality and loss of life expectancy.