Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model. (September 2016) - Nyhan, Marguerite; Sobolevsky, Stanislav; Kang, Chaogui; Robinson, Prudence; Corti, Andrea; Szell, Michael; Streets, David; Lu, Zifeng; Britter, Rex; Barrett, Steven R.H.; Ratti, Carlo
Abstract: Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15, 000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters in high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO2 ), nitrogen oxides (NOx ), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset. Results demonstrated that high-resolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate measured microscopic vehicle movement in tandem with microscopic emissions modeling for a substantial study domain. Highlights: We present a novel method for predicting air pollution emissions using transport data. Study uses measured microscopic transport data and a microscopic emissions model. GPS data from over 15, 000 vehicles were analyzed to quantify speeds and accelerations. CO2, NOx, VOCs and PM were modeled in high spatio-temporal resolution. Highly localized areas of elevated emissions were identified.