Multivariate Modeling to handle Urban Air Pollution Data observed trough Vehicular Sensor Networks
This work presents an interdisciplinary assessment that looks in-depth at the tracking of air quality in urban environments. The proposed application takes advantage of Vehicle Sensor Networks (VSN) by embedding sensor nodes to public transportation, spreading the sampling activity through different places visited during the route. We perform environmental modeling based on real data collected from the city of São Paulo, considering the multivariate spatial behavior of five different air pollutants from fossil-fueled vehicles (CO, O3, PM10, NO2 and SO2) simultaneously while it also varies in time. Finally, our VSN-based approach showed an improvement of 126 times lower error and 11 times higher coverage about conventional monitoring with air quality stations.
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