Multivariate modeling to handle urban air pollution data observed trough vehicular sensor networks

Abstract

This work presents an interdisciplinary assessment that looks in-depth at the tracking of air quality in urban environments. This kind of application is well suited to be approached with wireless sensor networks’ paradigm in their overall variations. Therefore a robust and diverse set of solutions have been developed following the technology capabilities advance. The proposed modeling application takes advantage of Vehicle Sensor Networks (VSN) by embedding sensor nodes to public transportation, addressing this study case with bus lines so that the mobiles spread the sampling activity through a large number of different places visited during the route. Simultaneously, it alleviates power management restrictions, packaging dimensions (size and weight), and general maintenance issues. We perform environmental modeling based on real data considering a temporal and spatial multivariate behavior on observed phenomena. We consider the city of São Paulo in our case study and parse the asserted data to create a multivariate map of samples, showing the behavior of five different air pollutants (CO, O3, PM10, NO2 and SO2) simultaneously while it also varies in time. The current development stage covers handling processes over input data that has unformatted or missing information due to being sourced from real sensors and creating the map mentioned above. Our methodology addresses: 1) the mentioned environmental simulation, 2) deploying mobile sensor nodes and perform sensing process, 3) implement network activity and delivery of collected data, 4) visualization of monitored environment based on gathered data using Voronoi Diagrams to fill blank data at non reached areas. Finally, some outspread of our work, in the research area, are the evaluation system-level performance and operational constraints through an eventbased simulation, taking into account a detailed description of roads, bus lines, vehicle itineraries, and general traffic information.