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Improved spatiotemporal air pollution mapping using transportation IT data

YuvalDavid Broday, Shimi Chen

At the first phase of this project we used output from a traffic allocation model (incl. traffic volumes and vehicle speeds per road section) to calculate spatiotemporal air pollution fields, based on regional wind speed and direction measurements and AQMN data. The preliminary algorithm has been developed in Year 3 for NOx, and was adjusted for PM2.5 in Year 4, not including time sequences of dust events. In Year 5 an error correction scheme was added to the model. Data using this new scheme were already supplied for one epidemiological study (see item 11). The scheme has been applied for two additional models (Ilan Levy's LUR model and the CHIMERE model, which is run by the Ministry for Environmental Protection). Moreover, in Year 5 we started to use a very rich dataset of GPS signals that come from vehicle fleets and supply (after some processing by Decell Technologies Ltd, see details in Year 4 report) traffic volumes and along-the-roads traffic speed for most of Israel. The data was processed and assimilated in a traffic model by Assoc. Prof. Shlomo Bekhor (Transportation Engineering Department, CEE) and has been integrated, independently, in the model (first phase to date, to be followed by two more phases) by Ph.D student Shimi Chen.