Chinese Geographical Science ›› 2021, Vol. 31 ›› Issue (3): 444-458.doi: 10.1007/s11769-021-1203-y

• Articles • Previous Articles    

Spatiotemporal Variations and Controls on Anthropogenic Heat Fluxes in 12 Selected Cities in the Eastern China

CAO Zheng1,2,3, WEN Ya4, SONG Song1,2,3, HUNG Chak Ho1,5, SUN Hui1,2   

  1. 1. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China;
    2. Guangdong Province Engineering Technology Research Center for Geographical Conditions Monitoring and Comprehensive Analysis, Guangzhou 510006, China;
    3. Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China;
    4. College of Resources and Environment, South China Agricultural University, Guangzhou 510642, China;
    5. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong 999077, China
  • Received:2020-06-04 Published:2021-04-29
  • Contact: WEN Ya, SONG Song E-mail:wenya26@scau.edu.cn;geossong@gzhu.edu.cn
  • Supported by:
    Under the auspices of National Natural Science Foundation of China (No. 41901219, 41671430, 41801326), Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (No. GML2019ZD0301)

Abstract: Spatiotemporal variations of anthropogenic heat flux (AHF) is reported to be associated with global warming. However, confined to the low spatial resolution of energy consumption statistical data, details of AHF was not well descripted. To obtain high spatial resolution data of AHF, Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light time-series product and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite monthly normalized difference vegetation index (NDVI) product were applied to construct the human settlement index. Based on the spatial regression relationship between human settlement index and energy consumption data. A 1-km resolution dataset of AHF of 12 selected cities in the eastern China was obtained. Ordinary least-squares (OLS) model was applied to detect the mechanism of spatial patterns of AHF. Results showed that industrial emission in selected cities of the eastern China was accountable for 63% of the total emission. AHF emission in megacities, such as Tianjin, Jinan, Qingdao, and Hangzhou, was most significant. AHF increasing speed in most areas in the chosen cities was quite low. High growth or extremely high growth of AHF were located in central downtown areas. In Beijing, Shanghai, Guangzhou, Jinan, Hangzhou, Changzhou, Zhaoqing, and Jiangmen, a single kernel of AHF was observed. Potential influencing factors showed that precipitation, temperature, elevation, normalized different vegetation index, gross domestic product, and urbanization level were positive with AHF. Overall, this investigation implied that urbanization level and economic development level might dominate the increasing of AHF and the spatial heterogeneousness of AHF. Higher urbanization level or economic development level resulted in high increasing speeds of AHF. These findings provide a novel way to reconstruct of AHF and scientific supports for energy management strategy development.

Key words: anthropogenic heat flux (AHF), Defense Meteorological Program/Operational Linescan System (DMSP/OLS) data, spatiotemporal variations, influencing factors, eastern China