Hyper resoluted Air Quality maps in urban environment with crowdsensed data from intelligent low cost sensors

Abstract

Air pollution is a complex phenomenon driven by emissions and their (photo)chemistry along with weather conditions and physical characteristics of the affected scenario. Urban areas remain an extremely challenging scenario for canyon effects and traffic car emissions which exacerbate the spatio temporal variability of air pollutants concentrations. Low cost Air Quality Sensors based crowdsensing offers a solution for hyper resolution AQ mapping for spatial analysis of the phenomenon. This work presents the design of an IoT crowdsensing platform supporting intelligent low cost sensors and spatial data processing along with its deployment and results for hyper resolution AQ mapping in an urban scenario.