As we begin to learn more about the benefits that machine learning can do for us, we’re seeing it in applications everywhere. One new development to come to light this month has done so through the help of a group of UCLA researchers as they’ve developed a mobile device to measure the air quality that’s cost-effective and could potentially save the lives of millions across the world.
The way in which this new device works by detecting pollutants and figuring out their exact size and concentration using a smartphone with a mobile microscope attached to it. There’s also a machine learning algorithm that analyzes the images of the pollutants automatically. Around 7 million people a year die from air pollution health issues. If we can help even a proportion of those people that these kinds of ideas need to be pursued.
Experts on the case have found that to monitor air pollution effectively, rapid, accurate and high-throughput sizing and quantification of a particular matter is needed. “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,” says Aydogan Ozcan, leader of the research team and UCLA Chancellor Professor of Electrical Engineering and Bioengineering and associate director if the California NanoSystems Institute.
Current air quality testing systems are mostly carried out at regulated sampling stations that are overseen by the Environmental Protection Agency in the US, and similar authorities in other countries. However, the problem is that these stations require the use of very expensive equipment that are hard to maintain and they require specially trained personnel in which to operate. At the other end of the scale, there are cheaper, commercially available mobile detectors, but are far less accurate and are unable to process large quantities of air at one time.
The new device that’s been developed by the UCLA researchers is called C-Air and has the accuracy of the higher end systems yet the cost (potentially) of the lesser quality systems. Using an air sampler and a tiny microscope it can screen 6.5 liters of air in just half a minute and create an image of the particles too. Connecting to a smartphone the device uses a machine learning algorithm to size and analyze the concentration of the particle from the image. Fingers crossed the researchers can now push the idea further and get it out to those areas of the world that really need it most.
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