WeAIR: Wearable Swarm Sensors for Air Quality Monitoring to Foster Citizens’ Awareness of Climate Change

Abstract

The present study proposes the implementation of an air quality measurement tool through the use of wearable devices, named WeAIR, consisting of wearable sensors for measuring NO${}_x$, CO${}_2$, CO, temperature, humidity, barometric pressure and PM10. In particular through the use of our novel sensor prototype, we performed a measurement collection campaign, acquiring an extensive set of geo-localized air quality data in the city of Siena (Italy). We further implemented and applied an AI neural network based model, capable of predicting the localization of an observation, having as input the air monitoring parameters and using the new spatio-temporal collected datasets. The promising performances obtained with the AI prediction approach enhanced the importance and possibilities of using such spatio-temporal air quality monitoring datasets, suggesting their crucial role both for raising citizen awareness on climate change and supporting policymakers’ decisions, as for instance the ones related to the positioning of new fixed monitoring stations.