Platform Pittsburgh: Air Quality Data Extraction from Images
Air pollution is a growing problem negatively impacting the health and lives of people worldwide. Over 3 million deaths in the world are due to air pollution and 25% of emergency room visits are directly related to air pollution. Moreover, asthma is the most chronic disease in children.
Fine inhalable particles with a diameter less than 2.5 micrometers (PM2.5) come from a variety of sources such as power plants, motor vehicles, airplanes, residential wood burning, forest fires, agricultural burning, volcanic eruptions, and dust storms. Some PM2.5 particles are emitted directly into the air while others are formed through interactions in the atmosphere, which is the main cause of haze in the United States.
No levels of PM2.5 particles are truly safe. Because the particles are so small and light 1) they stay in the atmosphere longer increasing the chances of inhalation by humans and 2) may penetrate deep into the lungs and get into the bloodstream. Numerous studies have found a close link between PM2.5 particles and premature death from heart and lung disease, asthma, heart disease, chronic bronchitis, emphysema and pneumonia.
Despite the major health risks associated with poor air quality, there are only a thousand air quality sensors across the United States. However, there are approximately 30 million surveillance cameras and 200 million people with smart phones in the U.S. We propose the development of computational methods for measuring air quality from digital images, a system for air quality monitoring, and a distributed vision-based test bed.
With the ubiquity of cameras in everyday life, our work has the potential to give people air quality measurements anytime, anywhere.
Carnegie Mellon University School of Computer Science
Carnegie Mellon University Robotics Institute
Pittsburgh Department of City Planning
The Heinz Endowments
National Science Foundation