Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

Abstract

The thermal environment is closely related to human well-being. Determinants of surface urban heat islands (SUHIs) have been extensively studied. Nevertheless, some research fields remain blank or have conflicting findings, which need to be further addressed. Particularly, few studies focus on drivers of SUHIs in massive cities with different sizes under various contexts at large scales. Using multisource data, we explored 11 determinants of surface urban heat island intensity (SUHII) for 1449 cities in different ecological contexts throughout China in 2010, adopting the Spearman and partial correlation analysis and machine learning method. The main results were as follows: (1) Significant positive partial correlations existed between daytime SUHII and the differences in nighttime light intensity and built-up intensity between cities and their corresponding villages except in arid or semiarid western China. The differences in the enhanced vegetation index were generally partially negatively correlated with daytime and nighttime SUHII. The differences in white sky albedo were usually partially negatively correlated with nighttime SUHII. The mean air temperature was partially positively correlated with nighttime SUHII in 40% of cases. Only a few significant partial relationships existed between SUHII and urban area, total population, and differences in aerosol optical depth. The explanation rates during daytime were larger than during nighttime in 72% of cases. The largest and smallest rates occurred during summer days in humid cold northeastern China (63.84%) and in southern China (10.44%), respectively. (2) Both the daytime and nighttime SUHII could be well determined by drivers using the machine learning method. The RMSE ranged from 0.49°C to 1.54°C at a national scale. The simulation SUHII values were always significantly correlated with the actual SUHII values. The simulation accuracies were always higher during nighttime than daytime. The highest accuracies occurred in central-northern China and were lowest in western China during both daytime and nighttime.

Spatial-Temporal Changes and Associated Determinants of Global Heating Degree Days

“…This was because materials with lower albedos could absorb more energy during the daytime. The stored heat can be emitted upward into the atmosphere during the nighttime, contributing to higher ambient temperatures, as stated in previous studies [40][41][42]. The HDDs were positively partially correlated with the mean EVI during winter (r = 0.17, p = 0.000).…”

Urban heat islands: a review of contributing factors, effects and data

“…Long-time records and high observation frequency are two advantages of station observation. The longest AT records at certain stations can be over a century old [28].…”

“…In another study, retrieving and determining SUHI intensity values for 1449 Chin cities, the mean AT was partially positively correlated with night-time SUHII in 40% of cases using monthly LST data at a 1 km resolution and enhanced vegetation index data at 250 m resolution from MODIS/Aqua from December 2009 to November 2010. Central northern China had the highest accuracies, whereas the lowest was in western China during both daytime and night-time [28]. Multispectral satellite images with high spatial resolution were acquired over the Athens metropolitan region in Greece, and LST data was obtained from ASTER thermal data.…”

Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China

“…Regarding the diversity of methods used at the city level [10], many studies have used land surface temperature (LST) retrieval algorithms, spatial and temporal evolution characteristics, model analysis, linear regression analysis, and other approaches to explore the driving mechanisms of UHIs [11][12][13]. In these studies, the authors introduced various simulation methods (such as numerical simulation [14] and simulation models [15], regression fitting [16,17], and ecological landscape methods [18,19]).…”