Elsevier

Science of The Total Environment

Volume 609, 31 December 2017, Pages 742-754
Science of The Total Environment

Temporal trends of surface urban heat islands and associated determinants in major Chinese cities

https://doi.org/10.1016/j.scitotenv.2017.07.217Get rights and content

Highlights

  • It is first time to study the temporal trends of SUHI at national scale.

  • The surface urban heat island is intensifying in China.

  • SUHII in SUA and UA was correlated with LST and urbanization, respectively.

Abstract

There are many studies focusing on spatial variations of surface urban heat islands (SUHIs) in literature. In this study, MODIS land surface temperature (LST) data and China's Land Use/Cover Datasets (CLUDs) were used to examine the temporal trends of SUHIs in 31 major Chinese cities during 2001–2015 using three indicators: SUHI intensity (SUHII), area of the SUHI (AreaSUHI) and percentage of area with increasing SUHII (PAISUHII). Correlation analyses between SUHII and background (rural) LST (extracted from MODIS LST), vegetation coverage (reflected by MODIS EVI data) and anthropogenic heat release (reflected by nighttime light data) were performed from temporal rather than spatial perspectives. Our findings showed that the SUHII and AreaSUHI in urbanized areas increased significantly in most cities in summer days, whereas they increased significantly in approximately half and more than half of the cities in summer and winter nights, respectively. In summer days, summer nights and winter nights, the PAISUHII was approximately 80% and over 50% in union areas and the 20 km buffer, respectively. Correlation analyses indicated that the SUHII in stable urban areas was negatively correlated with the background LST in summer and winter days for most cities, especially in northern China. A reduction in vegetation contributed to the increasing SUHII in urbanized areas in summer days and nights. The increasing anthropogenic heat release was an important factor for increases in the SUHII in urbanized areas.

Graphical abstract

Temporal trends of surface urban heat island intensity in China during 2001–2015. SUA: stable urban area. UA: urbanized area.

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Introduction

Urbanization is accelerating all over the world. The urban population accounted for 30% and 54% of the total world population in 1951 and 2015, respectively, and this percentage is expected to reach 60% by 2030 (United Nations, 2014). Urbanization can cause a series of environmental issues that have profound impacts on human life, including the urban heat island (UHI) effect, which is defined as a higher temperature in urban areas than in rural areas (Peng et al., 2012, Zhou et al., 2015). This phenomenon can be attributed to: a) the increases in impervious surfaces can lead to the transformation from latent heat flux into sensible heat flux; b) the low albedo and high heat storages of urban roads and buildings; c) anthropogenic heat release. The UHI effect can influence human life by increasing the risk of exposure to health-threatening heat (Zhou et al., 2015) and energy consumption (Akbari et al., 2015), and it can also reduce water and air quality (Grimm et al., 2008, Zhou et al., 2014), change land surface phenology (Zhou et al., 2016c, Yao et al., 2017) and decrease net primary production (Chen et al., 2017, Imhoff et al., 2004). Therefore, both the magnitude and temporal trends of UHIs must be comprehensively studied.

UHIs include atmospheric UHIs monitored by weather stations and surface UHIs (SUHIs) estimated by remote sensing (Zhou et al., 2014). Although the atmospheric UHIs is more closely related to human health, Both types of UHIs are still highly related (Arnfield, 2003, Zhou et al., 2016b). The use of remote sensing to monitor SUHIs has attracted increasing attention around the world in recent decades because of its free access and broad spatial coverage. Studies have used Landsat TM/ETM + to study the time series of SUHI in a single city because of the high spatial resolution and long time series of products, and results have indicated that the SUHI has been intensifying or expanding in certain cities of China, including Beijing (Qiao et al., 2014), Shanghai (Zhao et al., 2016), Nanjing (Tu et al., 2016), Wuhan (Shen et al., 2016) and Lanzhou (Pan, 2015). Studies have also used MODIS land surface temperature (LST) data to study the spatiotemporal variations of SUHIs at regional or global scales (Clinton and Gong, 2013, Peng et al., 2012, Wang et al., 2015a, Zhou et al., 2016a, Zhou et al., 2014, Weng et al., 2014). The results of such studies have indicated that SUHIs were characterized by large spatial, diurnal and seasonal heterogeneity. For example, Peng et al. (2012) studied the SUHI intensity (SUHII, temperature difference between urban and rural areas) in 419 large cities worldwide for the period 2003–2008, and the results showed that the daytime SUHII was higher than the nighttime SUHII (1.5 ± 1.2 °C vs. 1.1 ± 0.5 °C, p < 0.01). Zhou et al. (2014) and Wang et al. (2015a) investigated the SUHII in 32 major Chinese cities for the period 2003–2011 and 67 large Chinese cities for the period 2003–2010, respectively, and results showed that the daytime SUHII was higher in summer and lower in winter while the nighttime SUHII was relatively stable across seasons. The daytime SUHII in southern cities was higher than that in northern cities, although the opposite occurred at night. Zhou et al. (2015) showed that the LST decreased exponentially with increasing distances from urban areas and the UHI footprint for 31 major Chinese cities for the period 2003–2012 was 2.3 and 3.9 times larger than the area of the respective city during daytime and nighttime, respectively. However, in the context of rapid urbanization, few studies have performed a comprehensive analysis on the temporal trends of SUHI at regional or global scale. Only Zhou et al. (2016a) analyzed the temporal trends of SUHII according to the urban development intensity and both annual average daytime and nighttime SUHII increased significantly in only one-third of the cities in China for the period 2003–2012. Therefore, more studies about the temporal trends of SUHI and associated determinants across multiple cities should be conducted.

In addition, the relationships between SUHII and associated driving forces should be further studied. Previous studies have shown that albedo contributes greatly to the nighttime SUHII (Peng et al., 2012, Zhou et al., 2014) because urban roads and buildings have large heat storage, and the heat absorbed during the day is released slowly at night (Wang et al., 2016a). Vegetation can reduce the SUHII (Wang et al., 2016a), while anthropogenic heat release can increase SUHII in cities (Zhou et al., 2014, Peng et al., 2012). The relationships between SUHII and air temperature differed greatly in different areas (Du et al., 2016, Peng et al., 2012, Zhou et al., 2014). Although previous studies have conducted correlation analyses between SUHII and associated determinants across space (Du et al., 2016, Peng et al., 2012, Wang et al., 2015a, Zhou et al., 2016a, Zhou et al., 2016b, Zhou et al., 2014, Weng et al., 2008), the results of correlation analyses would differ if the analyses were conducted across different years. However, few studies have performed such studies at regional scales.

China has different climate zones that range from tropical in the south to subarctic in the north (Liang et al., 2016), and experiences a wide precipitation gradient that increases from the northwest to southeast (Zhou et al., 2014). As the largest developing country in the world, China has undergone continuous rapid urbanization in recent decades. The urban population in China has increased from 459 million in 2000 to 779 million in 2015 (United Nations, 2014), and the urban area has expanded from 4.85 × 104 km2 in 1990 to 9.08 × 104 km2 in 2010 (Kuang et al., 2016). Rapid urbanization has considerable impacts on the environment in China, and these impacts must be thoroughly investigated, particularly the temporal trends. To provide comprehensive and systematic research on the temporal trends of SUHI and fill the current research gaps, this study aimed to (1) examine the 15-year average SUHI in China; (2) investigate the temporal trends of SUHI for the period 2001–2015; and (3) analyze the relationships between SUHII and its driving forces.

Section snippets

Data

Two urban agglomerations and 29 municipalities or provincial capitals were selected in this study (Fig. 1). The two urban agglomerations were Pearl River Delta urban agglomeration (including Shenzhen, Dongguan, Guangzhou, Foshan, Zhongshan, Zhuhai, Xianggang and Jiangmen) and Yangtze River Delta urban agglomeration (including Shanghai, Suzhou, Changzhou and Wuxi). These areas have shown relatively faster urbanization in China and played an important role in China's economic development in

Mean SUHII and AreaSUHI in 31 major Chinese cities

The SUHII was evident in China (Fig. 3), the mean SUHII in SUA averaged for 31 cities was 4.09 °C in summer days, and the highest SUHII was observed in Shenyang (6.69 °C for SUA in summer days). Across all cities, the SUHII was significant higher in the SUAs than the UAs in summer days (p < 0.01), summer nights (p < 0.01) and winter nights (p < 0.01) (Table 1). However, significant differences were not observed in SUHII between the SUAs and UAs in winter days (p > 0.05). Spatially, the SUHII was higher

Mean SUHII and AreaSUHI in major Chinese cities

In this study, the SUHII was significantly higher in SUAs than UAs in summer days, summer nights and winter nights, which was likely due to the fact that the SUAs had higher anthropogenic heat release, lower vegetation coverage and albedo than the UAs. In addition, the SUHII was higher in southern cities than northern cities in summer days and winter days, although the opposite results were found in summer nights and winter nights. These results are consistent with previous studies (Wang et

Conclusion

In this study, MODIS LST data and CLUDs were used to investigate the temporal trends of SUHI in 31 major cities in China for the period 2001–2015. Correlation analyses were conducted between SUHII and background LST, vegetation coverage and anthropogenic heat release in each city across the years studied. The SUHI averaged from 2001 to 2015 was characterized by large spatiotemporal heterogeneity, and the highest SUHII and largest AreaSUHI and AreaSSUHI were observed in summer days, whereas the

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