Piedrahita, R., Xiang, Y., Masson, N., Ortega, J., Collier, A., Jiang, Y., Li, K., Dick, R., Lv, Q., Hannigan, M. and Shang, L. (2014) The Next Generation of Low-Cost Personal Air Quality Sensors for Quantitative Exposure Monitoring. Atmospheric Measurement Techniques Discussions, 7, 2425-2457. https://doi.org/10.5194/amtd-7-2425-2014

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.