Mobile Device Measures Air QualityMay 12, 2017
Want to know if it is safe to breathe? In an attempt to answer that question, researchers at UCLA have developed an inexpensive mobile device that accurately measures air quality.
Using a mobile microscope attached to a smartphone and a machine-learning algorithm, the device, called c-Air, detects pollutants and determines their concentration and size.
The device was designed to give people around the world the power to measure the levels of airborne particulate matter (a significant contributor to air pollution), potentially helping to reduce the World Health Organization's estimate of 7 million people dying each year because of health hazards related to air pollution.
"Scientists seeking solutions to this global issue have found that rapid, accurate and high-throughput sizing and quantification of particulate matter in the air is crucial for monitoring air pollution," said Aydogan Ozcan, who led the research team and is UCLA's Chancellor's Professor of Electrical Engineering and Bioengineering and associate director of the California NanoSystems Institute. "With lab-quality devices in the hands of more people, high-quality data on pollutants as a function of time from many more locations can be collected and analyzed. That can then help governments develop better policies and regulations to improve air quality."
Traditionally, air quality is tested at air sampling stations regulated by the Environmental Protection Agency (EPA) in the U.S. and regulated by comparable agencies in other countries. Typically, the business of air quality measurement is costly with devices ranging from $50,000 to $100,000 and that require trained personnel to operate. Although there are less costly devices available, they are often unable to quickly process large volumes of air and are less accurate.
The UCLA device is just as accurate as the sophisticated devices used at the regulated air sampling stations but costing significantly less. The device can screen 6.5 liters of air in 30 seconds while also being able to generate images of the airborne particles. The machine-learning algorithm measures and analyzes the particles from the captured images.
The researchers suggest that because of c-Air's machine-learning capability, it can quickly adapt to detect specific particles in the air, such as different types of pollen and mold.
The device is detailed in Light: Science & Applications.