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Extreme heat events occur frequently in urban areas, seriously affecting human well-being and productivity. Therefore, this study aimed to quantify the impact of and regional differences in the 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&#x2013;Tianjin&#x2013;Hebei (BTH) 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 0.21&#x00B0;C, 0.03&#x00B0;C, 0.35&#x00B0;C, and 0.53&#x00B0;C 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. Additionally, 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 (EVI) 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 study elucidates the changes in and influencing mechanisms of the urban heat island intensity and footprint and provides an important reference for mitigating the UHI effect and rationally planning urban land use.

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Quantification of Urban Heat Island Effect and Differences in Regional Influence Based on Footprint Analysis: a Case Study of the Beijing&#x2013;Tianjin&#x2013;Hebei Urban Agglomeration