Predicting Vehicular Emissions in High Spatial Resolution Using Pervasively Measured Transportation Data and Microscopic Emissions Model
Senseable City Lab :.:: Massachusetts Institute of Technology This paper might be a pre-copy-editing or a post-print author-produced .pdf of an article accepted for publication. For the definitive publisher-authenticated version, please refer directly to publishing house’s archive system SENSEABLE CITY LAB Atmospheric Environment 140 (2016) 352e363 Contents lists available at ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model Marguerite Nyhan a, *, Stanislav Sobolevsky b, Chaogui Kang c, Prudence Robinson a, Andrea Corti d, Michael Szell e, David Streets f, Zifeng Lu f, Rex Britter a, Steven R.H. Barrett g, Carlo Ratti a a Massachusetts Institute of Technology, SENSEable City Laboratory, Cambridge, MA, United States b Centre for Urban Science and Progress, New York University, New York City, United States c Wuhan University, Wuhan, Hubei, China d Politecnico di Milano, 32 Piazza Leonardo da Vinci, Milano, Italy e Center for Complex Network Research, Department of Physics, Northeastern University, Boston, United States f Argonne National Laboratory, National Aeronautics and Space Administration (NASA), Lemont, IL, United States g Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA, United States 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. CO , NO , VOCs and PM were modeled in high spatio-temporal resolution. 2 x Highly localized areas of elevated emissions were identified.
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