Real-time air pollution exposure and vehicle emissions estimation using IoT, GNSS measurements and web-based simulation models

Increasing urban air pollution level is one of the major concerns for citizens due to its impact on public health. In cities, vehicle emissions are one of the main contributors for nitrogen oxides (NOx) and particulate matter (PM) emissions. These emissions are not only related to the vehicle technology but also to the driving behavior of the user. For most people air pollution is barely noticeable and the driver's impact is still unknown. The contribution of this paper consists in using new information and communication technology to create an in-vehicle-IoT device giving the driver a real-time feedback on his emissions and on his exposure during his trips. This is achieved by coupling complex simulation models deployed on distant servers and the existing smartphone GNSS sensor in order to get a low-cost solution, compliant with large-scale deployment. Air quality exposure is estimated using an atmospheric urban dispersion model based on the driver real-time location. Vehicle pollutant emissions are computed for each second of the trip from the measured GNSS speed, position and altitude using models simulating the physical phenomena involved in pollutant formation. Vehicle technical features are taken into account by the way of a data bank of sub-system models automatically selected and tuned based on technical parameters retrieved from the license plate number.