Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment.

Journal: Environment international
PMID: 39250881

Abstract

BACKGROUND: Traffic-related air pollution (TRAP) is a major contributor to urban pollution and varies sharply at the street level, posing a challenge for air quality modeling. Traditional land use regression models combined with data from fixed monitoring stations may be unable to predict and characterize fine-scale TRAP, especially in complex urban environments influenced by various features. This study aims to estimate fine-scale (50 m) concentrations of nitrogen oxides (NO and NO₂) in Hong Kong using a deep learning (DL) structured model.

Authors

  • Peng Wei
    School of Basic Medicine, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.
  • Song Hao
    Department of Engineering Product Design, Singapore University of Technology and Design, 8 Somapah Road, 487372 Singapore.
  • Yuan Shi
    School of Architecture, Chinese University of Hong Kong, New Territories, Hong Kong.
  • Abhishek Anand
    Department of Mechanical Engineering, Carnegie Mellon University, United States.
  • Ya Wang
    Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China.
  • Mengyuan Chu
    Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China.
  • Zhi Ning
    Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China. Electronic address: zhining@ust.hk.

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