Quantification of Urban Heat Island Effect and Differences in Regional Influence Based on Footprint Analysis: A Case Study of the Beijing-2013;Tianjin-2013;Hebei Urban Agglomeration

被引:2
作者
Yu, Huisheng [1 ]
Sun, Dongqi [2 ]
机构
[1] Qingdao Univ Technol, Sch Management Engn, Qingdao 266520, Peoples R China
[2] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable DevelopmentModeling, Beijing 100101, Peoples R China
基金
中国国家自然科学基金;
关键词
Urban areas; Thermal pollution; Market research; Spatiotemporal phenomena; Land surface temperature; Mathematical models; Land surface; Driving factors; land surface temperature (LST); thermal environment; urban agglomeration; urban heat island (UHI) footprint; SURFACE; IMPACT; ENERGY;
D O I
10.1109/JSTARS.2024.3373409
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Extreme heat events occur frequently in urban areas, seriously affecting human well-being and productivity. Therefore, this article aimed to quantify the impact of and regional differences in the urban heat island (UHI) effect within the broader context of achieving sustainable development goals. To this end, we combined footprint analysis with principal component analysis and multivariate linear regression analysis to quantify the spatiotemporal distribution of heat island intensity and footprint within the Beijing-Tianjin-Hebei urban agglomeration as well as the impact of regional differences. We found that the surface urban heat island intensity (SUHII) value was higher during the daytime than at night. In 2005, 2010, 2015, and 2018, the average daytime values of SUHII were 0.21degree celsius, 0.03&degree celsius, 0.35degree celsius, and 0.53degree celsius higher than those at night, respectively. High daytime values of SUHII mainly occurred in larger cities (e.g., Beijing), and high nighttime values of SUHII mainly occurred at higher latitudes. In addition, we determined that the maximum values of the SUHIF were concentrated in densely populated areas such as Beijing, Tianjin, and Shijiazhuang. Furthermore, principal component analysis revealed that PM2.5 was negatively correlated with SUHII, whereas population density (PD) and enhanced vegetation index were positively correlated with SUHII. In contrast, PM2.5 and EVI were negatively correlated with SUHIF, whereas PD and SUHIF showed a negative correlation. This article elucidates the changes in and influencing mechanisms of the UHI intensity and footprint and provides an important reference for mitigating the UHI effect and rationally planning urban land use.
引用
收藏
页码:6910 / 6919
页数:10
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