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Mobile Device Can Accurately and Inexpensively Monitor Air Quality Using Machine Learning


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The c-Air device prototype.

The new cost-effective mobile device designed to measure air quality by University of California, Los Angeles researchers 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 analyzes images of the pollutants.

Credit: UCLA Ozcan Research Group

Researchers at the University of California, Los Angeles (UCLA) have developed c-Air, a cost-effective mobile device designed to measure air quality.

The device functions by detecting pollutants and measuring their concentration and size using a mobile microscope connected to a smartphone and a machine-learning algorithm that automatically analyzes the images of the contaminants.

The researchers say c-Air could give many more people around the world the ability to accurately detect dangerous airborne particulate matter.

The UCLA team notes c-Air is just as accurate as conventional equipment, but could cost tens of thousands of dollars less.

The device consists of an air sampler and a holographic microscope about the size of a computer chip. The system wirelessly connects to a smartphone and works with a remote computer server using a machine-learning algorithm that analyzes and sizes the particles based on the images.

From University of California, Los Angeles
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