Scientists from Duke University have developed an artificial intelligence tool that will help
The authors of the new AI tool are particularly interested in detecting levels of PM2.5 particles.
PM2.5 are solid particles less than 2.5 microns in size. Their diameter is 30 times smaller than that of a human hair. These include a mixture of particles of dust, ash, soot, and sulphates and nitrates suspended in the air. It is these substances that cause the turbidity of the air, typical for the centers of the largest metropolitan areas.
Particles PM2.5 are able to climb deep into the airways and settle in the lungs. Inhalation of these particles can cause irritation to the eyes, nose, throat, or lungs, as well as bouts of coughing, runny nose and choking. But this does not exhaust the danger of their impact. The World Health Organization's PM2.5 Particle Concentration Rate is 25 micrograms per cubic meter. Exceeding this limit can disrupt the normal functioning of the lungs and cause the development of many dangerous diseases such as lung cancer, respiratory tract infections and cardiovascular diseases.
New artificial intelligence algorithm chosethese satellite images are quarter-sized as local hotspots (top) and cool spots (bottom) for Beijing's air pollution. Credit: Tongshu Zheng, Duke University.
Global Burden of Diseases for 2020it is reported that 90% of the world's population lives in areas where PM2.5 is hazardous to health. At the same time, in most cities there are no ground-based air monitoring stations due to the high cost.
Moreover, they only give a general idea.about the conditions of air pollution in a certain region, but for residents of different areas of the city, these data are useless. To solve the problem, scientists created an instrument to measure PM2.5 in the 300-meter range (city block).
A new artificial intelligence algorithm has identified several hot spots and cool spots with air pollution in Delhi. Credit: Duke University School of Nursing.
Using satellite data, weather indicators andMachine learning researchers trained an algorithm to automatically find hot and cool spots of air pollution. The developers used the technique of residual learning. The algorithm first estimates PM2.5 levels using only weather data. It then measures the difference between these estimates and actual particle levels. As a result, the algorithm learns to use satellite images to improve forecasts.
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