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Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM 2.5 Mapping

Author

Listed:
  • Huanfeng Shen

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
    The Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China)

  • Man Zhou

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

  • Tongwen Li

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

  • Chao Zeng

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

Abstract

Fine spatiotemporal mapping of PM 2.5 concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM 2.5 influencing factors pose challenges for accurate mapping. To address these issues, we innovatively combined social sensing data with remote sensing data and other auxiliary variables, which can bring both natural and social factors into the modeling; meanwhile, we used a deep learning method to learn the nonlinear relationships. The geospatial analysis methods were applied to realize effective feature extraction of the social sensing data and a grid matching process was carried out to integrate the spatiotemporal multi-source heterogeneous data. Based on this research strategy, we finally generated hourly PM 2.5 concentration data at a spatial resolution of 0.01°. This method was successfully applied to the central urban area of Wuhan in China, which the optimal result of the 10-fold cross-validation R 2 was 0.832. Our work indicated that the real-time check-in and traffic index variables can improve both quantitative and mapping results. The mapping results could be potentially applied for urban environmental monitoring, pollution exposure assessment, and health risk research.

Suggested Citation

  • Huanfeng Shen & Man Zhou & Tongwen Li & Chao Zeng, 2019. "Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM 2.5 Mapping," IJERPH, MDPI, vol. 16(21), pages 1-18, October.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:21:p:4102-:d:279977
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    References listed on IDEAS

    as
    1. Tianhao Zhang & Wei Gong & Wei Wang & Yuxi Ji & Zhongmin Zhu & Yusi Huang, 2016. "Ground Level PM 2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO 2 and Enhanced Vegetation Index (EVI)," IJERPH, MDPI, vol. 13(12), pages 1-12, December.
    2. Qianqian Yang & Qiangqiang Yuan & Tongwen Li & Huanfeng Shen & Liangpei Zhang, 2017. "The Relationships between PM 2.5 and Meteorological Factors in China: Seasonal and Regional Variations," IJERPH, MDPI, vol. 14(12), pages 1-19, December.
    3. Li Tian & Wei Hou & Jiquan Chen & Chaonan Chen & Xiaojun Pan, 2018. "Spatiotemporal Changes in PM 2.5 and Their Relationships with Land-Use and People in Hangzhou," IJERPH, MDPI, vol. 15(10), pages 1-14, October.
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