A cost-effective mobile device for measuring air quality has been developed by UCLA Researchers. This device works by detecting pollutants and then determining their size and concentration by using a mobile microscope attached to a smartphone and a machine-learning algorithm capable of automatically analyzing the images of the pollutants.
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The invention is anticipated to provide more people from all over the world the potential to precisely detect hazardous airborne particulate matter. The World Health Organization states that 7 million people die prematurely every year due to the health hazards caused by air pollution.
Scientists in search of solutions for this global issue have found that accurate, rapid and high-throughput quantification and sizing of particulate matter in air is considered to be important for monitoring air pollution, stated Aydogan Ozcan, who headed the research team and is UCLA Chancellor 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.
Aydogan Ozcan, Head of the research team, UCLA Chancellor Professor of Electrical Engineering and Bioengineering and Associate Director of the California NanoSystems Institute
Particulate matter, referring to a mixture of liquid and solid particles in air, is a vital contributor to air pollution. Smaller particles are assumed to be predominantly dangerous; WHO has confirmed that particles in air that measure 2.5 μm or smaller cause cancer.
Presently, air quality testing is frequently carried out at air sampling stations, which are regulated by the Environmental Protection Agency in the U.S. and by comparable agencies in other countries. However, those facilities usually use majorly enhanced instruments that are expensive (in the range of $50,000 to $100,000) and burdensome, and need specifically trained personnel to maintain.
Commercially available portable particle counters are available at the other end of the spectrum, and these counters cost much less (on the order of $1,000 to 2,000), but are less accurate and cannot process huge volumes of air in a rapid manner.
The UCLA platform, known as c-Air, is just as precise as the current higher-end equipment, but could cost tens of thousands of dollars less. It is made up of an air sampler and a holographic microscope, which is the size of a computer chip.
It is capable of screening 6.5 liters of air in 30 seconds and produces images of the airborne particles. It is able to wirelessly connect to a smartphone and functions with a remote computer server by employing a machine-learning algorithm that has the potential to analyze and size the particles from the images produced.
Ozcan and his team, headed by Graduate Student Yichen Wu, made use of c-Air for measuring air quality in the summer of 2016 at a number of sites in Southern California, including during the so-called Sand Fire near Santa Clarita, California, in July 2016.
In September 2016, the team also measured air in neighborhoods near Los Angeles International Airport and discovered increased concentration of particulate matter even at about five miles away, and particularly along the flight path of landing planes.
The Researchers propose that because of c-Air machine-learning capability, it can rapidly adapt to identify particular particles in air, such as varied types of pollen and mold.
The research has been featured in journal Light: Science and Applications, which is an open access journal from Nature Publishing Group. The National Science Foundation, Vodafone Americas Foundation and HHMI supported the research of Ozcan Lab.