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Open-source tool from MIT's Senseable City Lab lets people check air quality, cheaply.

Air pollution is a major public health problem: The World Health Organization has estimated that it leads to over 4 million premature deaths worldwide annually. Still, it is not always extensively measured. But now an MIT research team is rolling out an open-source version of a low-cost, mobile pollution detector that could enable people to track air quality more widely.

The detector, called Flatburn, can be made by 3D printing or by ordering inexpensive parts. The researchers have now tested and calibrated it in relation to existing state-of-the-art machines, and are publicly releasing all the information about it - how to build it, use it, and interpret the data.

The Flatburn concept at Senseable City Lab dates back to about 2017, when MIT researchers began prototyping a mobile pollution detector, originally to be deployed on garbage trucks in Cambridge, Massachusetts. The detectors are battery-powered and rechargable, either from power sources or a solar panel, with data stored on a card in the device that can be accessed remotely.

In both cases, the detectors were set up to measure concentrations of fine particulate matter as well as nitrogen dioxide, over an area of about 10 meters. Fine particular matter refers to tiny particles often associated with burning matter, from power plants, internal combustion engines in autos and fires, and more.

"The goal is for community groups or individual citizens anywhere to be able to measure local air pollution, identify its sources, and, ideally, create feedback loops with officials and stakeholders to create cleaner conditions," says Carlo Ratti, director of MIT's Senseable City Lab.

Journal Reference:
An Wang, Yuki Machida, Priyanka deSouza, Simone Mora, Tiffany Duhl, Neelakshi Hudda, John L. Durant, Fábio Duarte, Carlo Ratti, Leveraging machine learning algorithms to advance low-cost air sensor calibration in stationary and mobile settings [open], Atmospheric Environment, Volume 301, 2023, 119692, ISSN 1352-2310, DOI: https://doi.org/10.1016/j.atmosenv.2023.119692

Original Submission