Journal of Atmospheric and Environmental Optics, Volume. 18, Issue 2, 181(2023)

Deep learning architecture based on satellite remote sensing data for estimating ground-level NO2 across Beijing-Tianjin-Hebei Region

FAN Xuanshuo1... WU Haibin2,*, CHEN Xinbing2 and SONG Wei2 |Show fewer author(s)
Author Affiliations
  • 1Institute of Material Science and Information Technology, Anhui University, Hefei 230601, China
  • 2School of Physics and Material Science, Anhui University, Hefei 230601, China
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    Figures & Tables(7)
    Study area and distribution of ground-level monitor sites
    DNN network
    Model structure
    Scatter plots of Sample-CV (a) and Site-CV (b)
    Spatial distribution of NO2. (a) September 5, 2019; (b) September 16, 2019; (c) October 18, 2019
    • Table 1. Data information

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      Table 1. Data information

      DataSpatial resolutionTime resolutionSource
      Monitoring sites NO2/(μg·m-3)-hourCNEMC
      NO2 column densities/(mol·m-2)7.5 km × 3.5 kmdayS5P
      Meteorological0.25°hourERA5
      Elevation/m90 m-SRTM3
      Land use type30 m5 yearsGlobelLan30
      Population density /(people·km-2)30 arc-second5 yearsGPWv4
    • Table 2. Comparison of the various models

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      Table 2. Comparison of the various models

      ModelSample-CVSite-CV
      R2RMSEMAER2RMSEMAE
      LR0.689.427.370.5511.529.05
      GWR0.738.976.460.719.717.20
      SVR0.758.396.330.729.356.69
      GRNN0.758.046.320.738.856.28
      RF0.807.745.390.719.706.84
      DNN0.807.725.310.748.956.01
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    Xuanshuo FAN, Haibin WU, Xinbing CHEN, Wei SONG. Deep learning architecture based on satellite remote sensing data for estimating ground-level NO2 across Beijing-Tianjin-Hebei Region[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(2): 181

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    Paper Information

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    Received: Jun. 4, 2021

    Accepted: --

    Published Online: Jul. 7, 2023

    The Author Email: WU Haibin (whb62@163.com)

    DOI:10.3969/j.issn.1673-6141.2023.02.009

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