Using routing apps to model real-time road traffic emissions
Pearce, Helen ; Gong, Zhaoya ; Cai, Xiaoming ; Bloss, William
Measuring road traffic emissions of NO2 at high spatial and temporal resolutions is costly and logistically challenging. In this study, we develop a cheaper and universally applicable methodology to infer road transport emissions at the resolution of an individual road. We utilise the vast amount of data generated by the widespread use of mapping products to better understand traffic flows on city roads. While the total number of vehicles on a busy road link is relatively static as it is limited by road space and demand, the speed of vehicles on road links is highly variable due to congestion effects. However, this is often not included in traditional air quality models, where vehicles are assumed to be freely flowing at the legal speed limit of the road (e.g. Bright et al., 2013; Zhong et al., 2015). By capturing a real-time estimation of vehicle speed, a more accurate emission factor (gkm-1) for oxides of nitrogen (NOx) can be calculated for each vehicle on a road link. Total emissions for a road link are, in turn, calculated from the emission factor and the number of vehicles using the road link. The subsequent pollutant concentrations that people are exposed to arise from the meteorological, chemical and physical processing of emissions within the environment. This study focusses on improving the accuracy of traffic-related NOx emission calculations, whereas future work will model environmental interactions.</p>
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