Monitoring Air Quality in Urban Areas Using a Vehicle Sensor Network (VSN) Crowdsensing Paradigm

We present new advances in monitoring particulate matter (PM) in urban areas within a participatory vehicle sensor network (VSN) that exploits the use of multiple mobile low-cost IoT devices. These devices send geolocated PM measurements to an IT infrastructure and enabled us to reconstruct, in real time, the spatial and temporal distribution of pollutants in the study area in a web-based environment. The newly acquired data were integrated with independent reference measurements available from governmental environmental agencies. We deployed the infrastructure in the city of Trieste (Italy), since the beginning of 2021, with the help of several volunteers and the local transportation authority (Trieste Trasporti). By analysing the data, we delineate areas with lower air quality and identify the possible causes of these anomalies. We were able to define a belt outside the urban center where an enhanced concentration of pollutants occurs due to a higher flux of vehicular traffic that tends to jam there. Overall, our results demonstrate that this approach can be helpful in supporting urban planning and can also stimulate the community to reflect on how they can improve air quality in the area they live by reducing the use of private cars in favour of more widespread public transportation usage.

Automated summary

This study presents new advances in monitoring particulate matter in urban areas using a participatory vehicle sensor network. By utilizing low-cost IoT devices, the researchers were able to reconstruct the spatial and temporal distribution of pollutants in real time. They integrated the newly acquired data with reference measurements from governmental environmental agencies and successfully identified areas with lower air quality and the possible causes of these anomalies. The study highlights the potential of this approach in supporting urban planning and promoting community reflection on improving air quality.