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[China Instrument Network Instrument Development] Researchers at The Ozcan Research Group at the University of California, Los Angeles (UCLA) have discovered a cost-effective way to measure air quality.
Specifically, they invented a mobile device called C-Air, which uses a microscope head that can be connected to a smartphone and uses a machine learning algorithm to analyze and determine the size and concentration of contaminants it detects in the air. Because this type of equipment can be carried around, researchers can more easily and easily detect hazardous particulate matter in the air in a more accurate and cost-effective manner.
Particles with an aerodynamic equivalent diameter below 2.5 microns (ie, PM2.5) have been classified by the World Health Organization (WHO) as carcinogenic. According to statistics of the WHO, the number of people who die from air pollution in the world is as high as 7 million every year. Therefore, how to easily measure air quality is of utmost importance.
With a weight of approximately 590 grams, the C-Air is equipped with an air sampler and a compact microscope head. Samples of 6.5 litres of air can be sampled and analyzed in 30 seconds and a machine learning algorithm can be used to generate sizing and analysis of particles in the air. The device sends images through a smartphone to a remote computer server for analysis. According to researchers, due to the application of machine learning, the device adapts more quickly than other detectors to detect PM2.5 particles such as molds and pollen.
It is understood that in the United States, air quality testing is usually conducted at air sampling stations and is regulated by the US Environmental Protection Agency (EPA). At present, the cost of the facilities used by air sampling stations is between 50,000 and 100,000 U.S. dollars, and it is cumbersome to use and requires technical personnel to perform regular maintenance. Although there are some relatively inexpensive portable particle counters on the market (prices range from $1,000 to $2,000), they cannot sample high-throughput air samples and accuracy is a big problem. At present, the cost of C-Air is slightly lower than the traditional large-scale equipment. It not only has the accurate analysis function of large-scale equipment, but also has fairly good portability.
Aydogan Ozcan, Principal of the School of Electrical Engineering and Bioengineering at UCLA and deputy director of the California Nanosystems Institute, said: “Learning more people with this lab-quality air testing device can collect and analyze high-quality data from more locations. This will help the government to formulate better policies and regulations to improve air quality. "
The team of students led by Aydogan Ozcan and his graduate student Yichen Wu used C-Air for air quality testing at multiple locations in Southern California in 2016. For example, they detected at the Los Angeles International Airport in September 2016 that the concentration of PM2.5 increased significantly near the aircraft's landing flight route. Even at five miles, the increase in PM2.5 was quite significant.
The study was funded by the National Science Foundation, Vodafone Americas Foundation and HHMI. The research was published in Nature on September 8.
(Original title: UCLA Invents a Handheld Machine Learning Device that Monitors PM2.5 Quality)