Associations between urban thermal environment and physical indicators based on meteorological data in Foshan City. (September 2020)

• Record Type: Journal Article
• Authors: Zhang, Qian; Xu, Duo; Zhou, Dian; Yang, Yujun; Rogora, Alessandro
• Is Part Of: Sustainable cities and society. Volume 60(2020)
• Publication Date: 2020-09
• Subjects: Urban spatial characteristics -- Urban heat island -- Correlation analysis -- Regression analysis -- Predictor; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102288
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Correlation and regression analyses were conducted on nearly 33000 meteorological data and 14 urban physical indicators. Maximum daytime temperature will increase by 0.91 °C for every 10% addition in building footprint ratio. Daytime thermal environment and nighttime thermal environment are affected by different urban physical indicators. Abstract: Recent trends in increasing global extreme weather patterns have led to a proliferation of studies about urban climate. However, most current studies have been conducted with insufficient urban climate and spatial data. Nearly 33, 000 meteorological data were obtained from Foshan Meteorological Bureau to accurately identify whether and how urban spatial form affects urban thermal environment. Correlation and regression analyses were conducted on these meteorological data and 14 selected urban physical indicators, eight of which exhibited correlations with urban thermal environment. Unitary regression analysis showed that building footprint ratio is the most reliable predictor among the 14 urban physical indicators. The building footprint ratio explained about 70 % of the variance in daytime air temperature (AT) and urban heat island temperature (UHIT). According to the fitting equation, maximum daytime temperature, average daytime UHIT and average night UHIT will increase by 0.91 °C, 0.39 °C and 0.44 °C, respectively for every 10 % addition in building footprint ratio. Besides, the most surprising aspect of the regression analysis is that the daytime thermal environment and nighttime thermal environment are affected by different urban physical indicators. It can be seen from the results of unitary regression analysis that water area ratio has a strong correlation with daytime thermal environment, while impervious surface area ratio has a more obvious influence on night thermal environment. Multiple regression analysis revealed that the model composed of building footprint, green area, and water ratios explained about 83.3 % of the variance in average daytime UHIT. The model composed of building footprint, impervious surface area, and green area ratios explained about 80 % of the variance in average night UHIT.