MIT researchers create a novel low-cost and portable air pollution sensor
As per an estimation by WHO, air pollution causes around 4 million annual premature deaths all over the globe. Considering this issue, an MIT research team launched an open-source version of an economical, mobile pollution detector through which individuals can track the air-quality more broadly.
The detector, named Flatburn, can be fabricated through 3D printing or by ordering cheap parts. The researchers have now conducted tests and calibrated the detector concerning existing ultra-modern machines and are making people aware of how to assemble, use, and interpret the data.
“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,” said Carlo Ratti in a press release, director of MIT’s Senseable City Lab.
“We’ve been doing several pilots around the world, and we have refined a set of prototypes, with hardware, software, and protocols, to make sure the data we collect are robust from an environmental science point of view,” says Simone Mora, a research scientist at Senseable City Lab and co-author of a newly published paper detailing the scanner’s testing process.
The Bigger Picture
The Flatburn device is a small component of a larger project, called the city scanner, utilizing mobile devices to gain a better understanding of urban life.
“Hopefully with the release of the open-source Flatburn we can get grassroots groups, as well as communities in less developed countries, to follow our approach and build and share knowledge,” said An Wang, a researcher at Senseable City Lab and another of the paper’s co-authors.
The concept of Flatburn at Senseable City Lab dates back to around 2017 when the researchers of MIT started to prototype a mobile pollution detector, initially to be utilized on garbage trucks in Cambridge, Massachusetts.
These detectors are rechargeable and battery-powered, either from solar panels or power sources, with data stored on a card in the device, which is easily accessible from any remote location.
Testing air pollution in New York
Extending the current project, the researchers tested the devices in New York City and the Boston Area to compare their performance with the already deployed working pollution detection systems. In New York, the researchers used five detectors, which helped them collect 1.6 million data points over the course of four weeks in 2021, working with state officials to compare the results.
From the test, the researchers concluded the following:
“After following their deployment for a few months, we can confidently say our low-cost monitors should behave the same way [as standard detectors],” Wang says. “We have a big vision, but we still have to make sure the data we collect is valid and can be used for regulatory and policy purposes.”
Duarte adds: “If you follow these procedures with low-cost sensors, you can still acquire good enough data to go back to [environmental] agencies with it and say, ‘Let’s talk.’”
The paper was published in the journal Atmospheric Environment and can be found here.
Low-cost air sensing is changing the paradigm of ambient air quality management research and practices. However, consensus on a structured low-cost sensor calibration and performance evaluation framework is lacking. Our study aims to devise a standardized low-cost sensor calibration protocol and evaluate the performance of various calibration algorithms. Extensive collocation data were collected in stationary and mobile settings in two American cities, New York and Boston. We trained the calibration models using stationary data aggregated at various intervals to examine the performance of several commonly used calibration algorithms described in the literature. Linear models provide consistently satisfactory calibration results, indicating linear responses from the low-cost sensors in our stationary test environment. Its simplicity is recommended for citizen science and education usages. Models that can account for non-linear relationships, especially random forest, perform well and transfer between sensors better than generalized linear regression models for PM2.5calibration, which should be adopted for regulatory and scientific purposes. Data collected in a mobile validation campaign in Boston were passed through the best-performing calibration models to assess their transferability. The results indicate that models trained with data from a different urban environment and season in the stationary setting did not transfer well to a mobile setting. It is recommended that low-cost sensors should be calibrated more often than suggested in Environmental Protection Agency's air sensor performance evaluation guidelines and used in an environment that is as similar as possible to the calibration environment.