Investigadores del MIT han creado una versión de código abierto del detector de contaminación móvil «City Scanner» que permite a las personas comprobar la calidad del aire en cualquier lugar y de forma económica. En la imagen se muestran algunos ejemplos de la última versión del dispositivo, llamado Flatburn, junto con un investigador que amarra un prototipo a un automóvil. Crédito: Carlo Ratti, Simone Mora, An Wang, et. Alabama
Una herramienta de código abierto desarrollada por[{» attribute=»»>MIT’s Senseable City Lab allows individuals to easily and affordably monitor air quality.
Air pollution poses a major threat to public health, with the World Health Organization attributing over 4 million premature deaths globally each year to poor air quality. Despite this, comprehensive measurement remains limited. However, an MIT research team is now introducing an open-source, affordable, and portable pollution detection device that could expand air quality monitoring capabilities.
Named Flatburn, this detector can be produced through 3D printing or by ordering inexpensive parts. The researchers have calibrated and tested it against cutting-edge machines and are publicly releasing all the information about it — how to build it, use it, 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,” says Carlo Ratti, 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 Flatburn device is part of a larger project, known as City Scanner, using mobile devices to better understand 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,” says An Wang, a researcher at Senseable City Lab and another of the paper’s co-authors.
The paper was recently published in the journal Atmospheric Environment.
In addition to Wang, Mora, and Ratti the study’s authors are: Yuki Machida, a former research fellow at Senseable City Lab; Priyanka deSouza, an assistant professor of urban and regional planning at the University of Colorado at Denver; Tiffany Duhl, a researcher with the Massachusetts Department of Environmental Protection and a Tufts University research associate at the time of the project; Neelakshi Hudda, a research assistant professor at Tufts University; John L. Durant, a professor of civil and environmental engineering at Tufts University; and Fabio Duarte, principal research scientist at Senseable City Lab.
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.
The current extension of that project involved testing the devices in New York City and the Boston area, by seeing how they performed in comparison to already-working pollution detection systems. In New York, the researchers used 5 detectors to collect 1.6 million data points over four weeks in 2021, working with state officials to compare the results. In Boston, the team used mobile sensors, evaluating the Flatburn devices against a state-of-the-art system deployed by Tufts University along with a state agency.
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 research team found that the mobile detectors estimated somewhat lower concentrations of fine particulate matter than the devices already in use, but with a strong enough correlation so that, with adjustments for weather conditions and other factors, the Flatburn devices can produce reliable results.
“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 dice. «Tenemos una gran visión, pero aún debemos asegurarnos de que los datos que recopilamos sean válidos y puedan usarse con fines regulatorios y de políticas».
Duarte agrega: «Si sigue estos procedimientos con sensores de bajo costo, aún puede adquirir datos lo suficientemente buenos como para volver a [environmental] agencias con él, y decir: «Hablemos de eso».
Los investigadores descubrieron que el uso de las unidades en un entorno móvil, además de los automóviles, significa que actualmente tendrán una vida útil de seis meses. También identificaron una serie de posibles problemas a los que se enfrentarán las personas cuando utilicen detectores Flatburn en general. Estos incluyen lo que el equipo de investigación llama «deriva», el cambio gradual en las lecturas del detector a lo largo del tiempo, así como el «envejecimiento», el deterioro más fundamental en la condición física de una unidad.
Aún así, los investigadores creen que las unidades funcionarán bien y brindan instrucciones completas en su versión de Flatburn como una herramienta de código abierto. Incluso incluye consejos para trabajar con gerentes, comunidades y partes interesadas para procesar los hallazgos y tratar de dar forma a la acción.
“Es muy importante comprometerse con las comunidades, para permitirles reflexionar sobre las fuentes de contaminación”, dice Mora.
“La idea original del proyecto era democratizar los datos ambientales, y ese sigue siendo el objetivo”, agrega Duarte. «Queremos que las personas tengan las habilidades para analizar datos e interactuar con las comunidades y los funcionarios».
Referencia: “Apalancamiento aprendizaje automático algoritmos para avanzar en la calibración de sensores de aire de bajo costo en entornos fijos y móviles” por An Wang, Yuki Machida, Priyanka deSouza, Simone Mora, Tiffany Duhl, Neelakshi Hudda, John L. Durant, Fábio Duarte y Carlo Ratti, 1 de marzo de 2023, Ambiente atmosférico.
DOI: 10.1016/j.atmosenv.2023.119692