Machine learning mobile microscope measures air quality
10 Sep 2017
Researchers at UCLA have developed what they say is a cost-effective mobile microscope designed to measure air quality.
The device, called c-Air, is intended to give more people around the world the ability to accurately detect dangerous airborne particulate matter, the researchers said.
It works by detecting pollutants and determining their concentration and size using a mobile microscope connected to a smartphone and a machine-learning algorithm that automatically analyses the images of the pollutants.
According to the researchers, c-Air is just as accurate as current higher-end equipment, but could cost tens of thousands of dollars less. It comprises an air sampler and a holographic microscope about the size of a computer chip. It can screen 6.5 litres of air in 30 seconds and generates images of the airborne particles.
UCLA professor Aydogan Ozcan, who led the research team, said: “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 analysed.
“That can then help governments develop better policies and regulations to improve air quality.”
A full account of the research has been published in journal Light: Science and Application.
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