基于POI和多源遥感数据估算中国人为热排放

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Abstract Statistics on energy consumption, transportation, population and GDP had been used to estimate anthropogenic heat emissions from industries, transportation, buildings, and human metabolism at the prefecture level for mainland China in 2019. Using the top-down energy inventory method and Cubist machine learning algorithm, anthropogenic heat flux (AHF) models were built by integrating points-of-interest (POI) datafrom a variety of categories and multisource remote sensing data. A gridded anthropogenic heat ﬂux (AHF) benchmark dataset at 1km spatial resolution had generated for China in 2019. The relationships between various geographic predictors and AHF from different heat sources were further analyzed. Results showed that high-value areas of AHF were generally distributed to metropolitan areas and cities with relatively developed industries. The values of AHF in urban centers in mainland China ranged between 80 and 200W/m². The highest values were reported for some heavy industrial areas up to 519.2W/m². Cubist models can explore the nonlinear relationships between individual predictors and AHF from different heat sources and give the relative importance of the predictors. POI-related variables are the most important predictors and can further reﬁne the estimation of AHF. Compared with previous studies, the spatial distribution of AHF from different heating components was effectively distinguished, highlighting the potential of POI data in improving the precision of AHF mapping. The new gridded AHF dataset in 2019 for mainland China can serve as important input for urban climate and urban environment numerical models.

Key words： anthropogenic heat emission points-of-interest Cubist energy-consumption inventory approach China

Received: 17 September 2022

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	QIAN Jing
	MAO Li-wei
	YANG Xu-chao
	CHEN Qian
	LI Fei-xiang
	WU Xin-tong
	CHEN Bo-ru

Cite this article:

QIAN Jing,MAO Li-wei,YANG Xu-chao等. Estimation of anthropogenic heat in China using points-of-interest and multisource remote sensing data[J]. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(6): 3183-3193.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2023/V43/I6/3183

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