Characteristics of anthropogenic heat with different modeling ideas and its driving effect on urban heat islands in seven typical Chinese cities

Jiangkang Qian; Qingyan Meng; Linlin Zhang; Uwe Schlink; Xinli Hu; Jianfeng Gao

doi:10.1016/j.scitotenv.2023.163989

Characteristics of anthropogenic heat with different modeling ideas and its driving effect on urban heat islands in seven typical Chinese cities

Sci Total Environ. 2023 Aug 15:886:163989. doi: 10.1016/j.scitotenv.2023.163989. Epub 2023 May 9.

Authors

Jiangkang Qian¹, Qingyan Meng², Linlin Zhang³, Uwe Schlink⁴, Xinli Hu³, Jianfeng Gao¹

Affiliations

¹ Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.
² Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China. Electronic address: mengqy@radi.ac.cn.
³ Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China.
⁴ Department of Urban and Environmental Sociology, Helmholtz Centre for Environmental Research-UFZ, Leipzig D-04318, Germany.

PMID: 37164103
DOI: 10.1016/j.scitotenv.2023.163989

Abstract

Anthropogenic heat (AH), an essential urban heat source, is often overlooked or simplified in research on the multiple temporal and spatial driving mechanisms of the urban heat island (UHI), and case studies investigating the impacts of different AH connotations are scarce. This study estimated the AH in seven typical Chinese cities based on a remote sensing surface energy balance model (AH_seb) and an energy consumption inventory-machine learning model (AH_inv). The intensity of the surface UHI was extracted using land surface temperatures, and then the linear mixed-effects model and geographic detectors were used to analyze the driving effect of AH on the UHI. Despite the similar shapes of the spatial profile curves, the AH derived from the two models differed in both temporal and spatial characteristics, which was more typical in winter and in urban centers, and AH_inv had a more notable central spread feature than AH_seb. The AH driving effects on UHI were notably influenced by spatial and temporal heterogeneity, particularly in regions with distinct background climates. However, after controlling for the random effects of the background climate, AH still exhibited a considerable enhancing effect on the UHI. AH_seb outperformed AH_inv in terms of linear positive correlation and interpretation rate for UHI. Meanwhile, interactions with other potential factors enhanced AH driving effects. Consequently, UHI mitigation must be tailored to the local context by integrating multiple drivers, and for the heating effects of AH, it is necessary to develop specific mitigation measures by limiting the conversion of AH_inv to AH_seb in addition to reducing the heat production. The findings offer guidance for analyzing and optimizing urban thermal climates with a focus on AH or energy consumption control.

Keywords: Anthropogenic heat; Driving analysis; Spatiotemporal heterogeneity; Urban heat islands.

MeSH terms

Cities
Climate*
Environmental Monitoring
Hot Temperature*
Seasons
Temperature