IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i21p4102-d279977.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/21/4102/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/16/21/4102/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiajia Chen & Huanfeng Shen & Tongwen Li & Xiaolin Peng & Hairong Cheng & Chenyan Ma, 2019. "Temporal and Spatial Features of the Correlation between PM 2.5 and O 3 Concentrations in China," IJERPH, MDPI, vol. 16(23), pages 1-17, November.
    2. Yi Yang & Jie Li & Guobin Zhu & Qiangqiang Yuan, 2019. "Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM 2.5 in China During 1998–2016," IJERPH, MDPI, vol. 16(7), pages 1-24, March.
    3. Zhiyu Fan & Qingming Zhan & Chen Yang & Huimin Liu & Meng Zhan, 2020. "How Did Distribution Patterns of Particulate Matter Air Pollution (PM 2.5 and PM 10 ) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
    4. Liang Zhang & Xianfan Shu & Jiaojiao Luo, 2022. "The Formation of a Polycentric City in Transitional China in a Three-Level Analysis Framework: The Case Study of Hangzhou," Land, MDPI, vol. 11(11), pages 1-17, November.
    5. Wenbo Chen & Fuqing Zhang & Saiwei Luo & Taojie Lu & Jiao Zheng & Lei He, 2022. "Three-Dimensional Landscape Pattern Characteristics of Land Function Zones and Their Influence on PM 2.5 Based on LUR Model in the Central Urban Area of Nanchang City, China," IJERPH, MDPI, vol. 19(18), pages 1-18, September.
    6. Haiou Yang & Wenbo Chen & Zhaofeng Liang, 2017. "Impact of Land Use on PM 2.5 Pollution in a Representative City of Middle China," IJERPH, MDPI, vol. 14(5), pages 1-14, April.
    7. Wei Wen & Tongxin Hua & Lei Liu & Xiaoyu Liu & Xin Ma & Song Shen & Zifan Deng, 2023. "Oxidative Potential Characterization of Different PM 2.5 Sources and Components in Beijing and the Surrounding Region," IJERPH, MDPI, vol. 20(6), pages 1-18, March.
    8. Weisong Li & Wanxu Chen & Jiaojiao Bian & Jun Xian & Li Zhan, 2022. "Impact of Urbanization on Ecosystem Services Balance in the Han River Ecological Economic Belt, China: A Multi-Scale Perspective," IJERPH, MDPI, vol. 19(21), pages 1-18, November.
    9. Xiangxue Zhang & Changxiu Cheng, 2022. "Temporal and Spatial Heterogeneity of PM 2.5 Related to Meteorological and Socioeconomic Factors across China during 2000–2018," IJERPH, MDPI, vol. 19(2), pages 1-15, January.
    10. Shixiong Cheng & Jiahui Xie & De Xiao & Yun Zhang, 2019. "Measuring the Environmental Efficiency and Technology Gap of PM 2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model," IJERPH, MDPI, vol. 16(4), pages 1-22, February.
    11. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.
    12. Jiejun Zhang & Pengfei Liu & Hongquan Song & Changhong Miao & Jie Yang & Longlong Zhang & Junwu Dong & Yi Liu & Yunlong Zhang & Bingchen Li, 2022. "Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    13. Hongbin Dai & Guangqiu Huang & Jingjing Wang & Huibin Zeng & Fangyu Zhou, 2022. "Spatio-Temporal Characteristics of PM 2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016–2021," IJERPH, MDPI, vol. 19(10), pages 1-20, May.
    14. Xiao Gong & Jianing Mi & Chunyan Wei & Ruitao Yang, 2019. "Measuring Environmental and Economic Performance of Air Pollution Control for Province-Level Areas in China," IJERPH, MDPI, vol. 16(8), pages 1-19, April.
    15. Ruiling Sun & Yi Zhou & Jie Wu & Zaiwu Gong, 2019. "Influencing Factors of PM 2.5 Pollution: Disaster Points of Meteorological Factors," IJERPH, MDPI, vol. 16(20), pages 1-31, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:21:p:4102-:d:279977. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.