Spatial-temporal variations of surface urban heat island intensity induced by different definitions of rural extents in China

Li, Kangning; Chen, Yunhao; Wang, Mengjie; Gong, Adu

doi:10.1016/j.scitotenv.2019.03.100

Cited by 67 publications

(27 citation statements)

References 72 publications

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“…While cities modify their local climate as they expand, the UHI intensity is usually quantified using snapshot measurements in the urban area and for some rural reference. For the SUHI, how to define this rural reference remains a contentious issue [45,53,54,55]. Generally, the urban-rural delineations are more clearly constructed for less sprawling cities with very little land management surrounding the city centers.…”

Section: Discussionmentioning

confidence: 99%

Reduction in Human Activity can Enhance the Urban Heat Island: Insights from the COVID-19 Lockdown

Chakraborty¹,

Sarangi²,

Lee³

2021

Preprint

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The COVID-19 lockdowns drastically reduced human activity, emulating a controlled experiment on human-land-atmosphere coupling. Here, using a fusion of satellite and reanalysis products, we examine this coupling during the lockdown in the Indo-Gangetic Basin, one of the world’s most populated and polluted regions. During the lockdown, the reduction (>10%) in columnar air pollution, expected to increase incoming solar radiation, was counteracted by a ~30% enhancement in cloud cover, causing little change in available energy at the surface. More importantly, the delay in winter crop harvesting increased surface vegetation cover, causing almost half the regional cooling (of -3.9 K) via evapotranspiration. Since this cooling was higher for rural areas, the daytime surface urban heat island (SUHI) intensity increased (by 0.20 to 0.41 K) during a period of reduced human activity. Our study provides strong observational evidence of the influence of agricultural activity on both urban and rural climate in this region.

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Section: Discussionmentioning

confidence: 99%

Reduction in Human Activity can Enhance the Urban Heat Island: Insights from the COVID-19 Lockdown

Chakraborty¹,

Sarangi²,

Lee³

2021

Preprint

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“…China is a large country located in East Asia that faces the western cost of the Pacific Ocean. Past research shows that there is a large precipitation gradient between Northwest and Southeast China [13], and different levels of the urban heat island (UHI) phenomenon have been observed among different areas in China. In this research, 36 major cities in China were selected as the research areas, and they contain 31 capital cities or direct-controlled municipalities and 5 important port cities (Shenzhen, Ningbo, Xiamen, Qingdao and Dalian).…”

Section: Study Areamentioning

confidence: 99%

Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

et al. 2020

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Urban heat islands (UHIs) are an important issue in urban sustainability, and the standardized calculation of surface urban heat island (SUHI) intensity has been a common concern of researchers in the past. In this study, we used the administrative borders (AB) method and an optimized simplified urban-extent (OSUE) algorithm to calculate the surface urban heat island intensity from 2001 to 2017 for 36 major cities in mainland China by using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The spatiotemporal differences between these two methods were analyzed from the perspectives of the regional and national patterns and the daily, monthly, and annual trends. Regardless of the spatial or temporal scale, the calculation results of these two methods showed extremely similar patterns, especially for the daytime. However, when the calculated SUHI intensities were investigated through a regression analysis with multiple driving factors, we found that, although natural conditions were the main drivers for both methods, the anthropogenic factors obtained from statistical data (population and gross domestic product) were more correlated with the SUHI intensity from the AB method. This trend was probably caused by the spatial extent of the statistical data, which aligned more closely with the rural extent in the AB method. This study not only explores the standardization of the calculation of urban heat intensity but also provides insights into the relationship between urban development and the SUHI.

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“…Definitions of rural vary. Example definitions of rural areas include: areas which are not urban 19 , areas of low population density and other indicators of rural life 20 , or based on surface urban heat islands 21 . We consider rural and urban to be extremes of a continuum of human environments with population density providing a quantitative metric of position along that continuum.…”

Section: Introductionmentioning

confidence: 99%

“…Different age ranges clustered with different indicator classes. Young people(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) clustered with crime. Older people (55 and above) clustered with mortality except for bone cancer.…”

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confidence: 99%

Rural–urban scaling of age, mortality, crime and property reveals a loss of expected self-similar behaviour

Sutton

Shahtahmassebi

Ribeiro

et al. 2020

Sci Rep

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The urban scaling hypothesis has improved our understanding of cities; however, rural areas have been neglected. We investigated rural–urban population density scaling in England and Wales using 67 indicators of crime, mortality, property, and age. Most indicators exhibited segmented scaling about a median critical density of 27 people per hectare. Above the critical density, urban regions preferentially attract young adults (25–40 years) and lose older people (> 45 years). Density scale adjusted metrics (DSAMs) were analysed using hierarchical clustering, networks, and self-organizing maps (SOMs) revealing regional differences and an inverse relationship between excess value of property transactions and a range of preventable mortality (e.g. diabetes, suicide, lung cancer). The most striking finding is that age demographics break the expected self-similarity underlying the urban scaling hypothesis. Urban dynamism is fuelled by preferential attraction of young adults and not a fundamental property of total urban population.

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Spatial-temporal variations of surface urban heat island intensity induced by different definitions of rural extents in China

Cited by 67 publications

References 72 publications

Reduction in Human Activity can Enhance the Urban Heat Island: Insights from the COVID-19 Lockdown

Reduction in Human Activity can Enhance the Urban Heat Island: Insights from the COVID-19 Lockdown

Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

Rural–urban scaling of age, mortality, crime and property reveals a loss of expected self-similar behaviour

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