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Smart city article details

Title	Ml Approach To Predict Air Quality Using Sensor And Road Traffic Data
ID_Doc	37189
Authors	Datia N.; Pato M.P.M.; Taborda R.; Pires J.M.
Year	2022
Published	Studies in Computational Intelligence, 1014
DOI	http://dx.doi.org/10.1007/978-3-030-93119-3_15
Abstract	Air quality is an important issue that impacts who live in cities. During COVID-19 pandemic, it becomes clear that low air quality can increase the effects of the disease. It is very important that in this era of Big Data and Smart Cities we use technology to address health issues like air quality problems. Often, air quality is monitored using data collected using fixed selected stations in a region. Such approach only gives us a global notion of the air quality, but do not support a fine-grained comprehension about spots distant from the collector’s stations, specially in residential urban places. In this paper, we propose a visual analytics solution that provides city council decision-makers an interactive dashboard that displays air pollution data at multiple spatial resolutions, that uses real and predicted data. The real air quality data is collected using low-cost portable sensors, and it is combined with other environmental contextual data, namely road traffic mobility data. Estimated air quality data is obtained using a machine learning regression model, that is integrated into the interactive dashboard. The visual analytics solution was designed with the city council decision-makers in mind, providing a clutter-free interactive exploration tool that enables those users to improve the quality of life in the city, focusing on one of the most important cities’ health quality key issues. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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