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Reisa, S., Seto, E., Northcross, A., Quinn, N.W.T., Convertino, M., Jones, R.L., Maier, H.R., Schlink, U., Steinle, S., Vieno, M. and Wimberly, M.C. (2015) Integrating Modelling and Smart Sensors for Environmental and Human Health. Environmental Modelling & Software, 74, 238-246. https://doi.org/10.1016/j.envsoft.2015.06.003

has been cited by the following article:

• TITLE: Traffic Maps and Smartphone Trajectories to Model Air Pollution, Exposure and Health Impact

  AUTHORS: Erik Skjetne, Hai-Ying Liu

  KEYWORDS: Traffic Map, Smartphone, Location Service, Trajectory, Traffic Pollution, Public Health, Road Traffic Exposure, Analytics, Big Data

  JOURNAL NAME: Journal of Environmental Protection, Vol.8 No.11, October 31, 2017

  ABSTRACT: In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emission, traffic density versus traffic velocity, traffic pollution concentration, exposure along individual trajectories, and health risk. A generic street with 100 km/h speed limit was used as an example to test the model. A single fixed-time trajectory had maximum exposure at velocity of 45 km/h at maximum pollution concentration. The street population had maximum exposure shifted to a velocity of 15 km/h due to the congestion density of vehicles. The shift is a universal effect of exposure. In this approach, nearly every modeling step of traffic pollution depended on traffic velocity. A traffic map is a super-efficient pre-processor for calculating real-time traffic pollution exposure at global scale using big data analytics.