Estimating CO Emissions from IoT Traffic Flow Sensors and Reconstruction.

CO emissions from burning fossil fuels make a relevant contribution to atmospheric changes and climate disruptions. In cities, the contribution by traffic of CO is very relevant, and the general CO estimation can be computed (i) on the basis of the fuel transformation in energy using several factors and efficiency aspects of engines and (ii) by taking into account the weight moved, distance, time, and emissions factor of each specific vehicle. Those approaches are unsuitable for understanding the impact of vehicles on CO in cities since vehicles produce CO depending on their specific efficiency, producer, fuel, weight, driver style, road conditions, seasons, etc. Thanks to today's technologies, it is possible to collect real-time traffic data to obtain useful information that can be used to monitor changes in carbon emissions. The research presented in this paper studied the cause of CO emissions in the air with respect to different traffic conditions. In particular, we propose a model and approach to assess CO emissions on the basis of traffic flow data taking into account uncongested and congested conditions. These traffic situations contribute differently to the amount of CO in the atmosphere, providing a different emissions factor. The solution was validated in urban conditions of Florence city, where the amount of CO is measured by sensors at a few points where more than 100 traffic flow sensors are present (data accessible on the Snap4City platform). The solution allowed for the estimation of CO from traffic flow, estimating the changes in the emissions factor on the basis of the seasons and in terms of precision. The identified model and solution allowed the city's distribution of CO to be computed.