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Pengxiang Zhao


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Mapping microscale PM2.5 distribution on walkable roads in a high-density city


  • Chengzhuo Tong
  • Zhicheng Shi
  • Wenzhong Shi
  • Pengxiang Zhao
  • Anshu Zhang

Summary, in English

Monitoring pollution of PM2.5 on walkable roads is important for resident health in high-density cities. Due to the spatiotemporal resolution limitations of Aerosol Optical Depth (AOD) observation, fixed-point monitoring, or traditional mobile measurement instruments, the microscale PM2.5 distribution in the walking environment cannot be fully estimated at the fine scale. In this study, by the integration of mobile measurement data, OpenStreetMap (OSM) data, Landsat images, and other multi-source data in land-use regression (LUR) models, a novel framework is proposed to estimate and map PM2.5 distribution in a typical microscale walkable environment of the high-density city Hong Kong. First, the PM2.5 data on the typical walking paths were collected by the handheld mobile measuring instruments, to be selected as the dependent variables. Second, Geographic prediction factors calculated by Google Street View, OpenStreetMap (OSM) data, Landsat images, and other multi-source data were further selected as independent variables. Then, these dependent and independent variables were put into the LUR models to estimate the PM2.5 concentration on sidewalks, footbridges, and footpaths in the microscale walkable environment. The proposed models showed high performance relative to those in similar studies (adj R2, 0.593 to 0.615 [sidewalks]; 0.641 to 0.682 [footpaths]; 0.783 to 0.797 [footbridges]). This study is beneficial for mapping PM2.5 concentration in the microscale walking environment and the identification of hot spots of air pollution, thereby helping people avoid the PM2.5 hotspots and indicating a healthier walking path.


  • Centre for Geographical Information Systems (GIS Centre)
  • Dept of Physical Geography and Ecosystem Science

Publishing year







IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing



Document type

Journal article


IEEE - Institute of Electrical and Electronics Engineers Inc.


  • Health Sciences
  • Earth and Related Environmental Sciences


  • Air pollution
  • Atmospheric modeling
  • Instruments
  • Legged locomotion
  • Monitoring
  • Pollution measurement
  • Roads
  • Urban areas




  • ISSN: 1939-1404