scholarly journals Possibility of Urban Heat Island Formation by Lifting Green Belts: Focusing on the Cases of Busan Metropolitan City, Korea

2021 ◽  
Vol 21 (5) ◽  
pp. 69-77
Author(s):  
Jiyong Park ◽  
Seungwon Kang ◽  
Sunmin Jun ◽  
Juchul Jung
Keyword(s):  
Urban Heat Island ◽  
Development Project ◽  
Policy Implications ◽  
Heat Island ◽  
Green Belt ◽  
New Towns ◽  
Metropolitan City ◽  
Industrial Complexes ◽  
Urban Heat ◽  
The Difference

We analyzed the temperature changes in cities and regions due to the lifting of the green belt in Busan Metropolitan City. The difference between the average temperature change in the summer surface due to the lifting of the green belt was analyzed in Local Spatial Autocorrelation Analysis (LISA) for the two points in 2000, when the green belt was fully preserved, and in 2018, when the recent record heatwave was recorded. Consequently, approximately 28.14% of the areas where the green belt was lifted from the cluster map became a hotspot. In Busan Metropolitan City, the changes in the soil quality, resulting from the development of new towns, creation of industrial complexes, reduction of green areas, and an increase in impervious areas due to largescale housing development projects following the lifting of the green belt, lead to the temperature rise in the city, thereby causing urban heat island phenomenon. This study contributes to the field by revealing that the urban heat island phenomenon can be affected in the long term depending on the urban development project involved in the lifting of the green belt. In addition to its original function of preserving the environment and preventing urban sprawl, the green belt can be a major urban planning tool for mitigating the rising urban heat island phenomenon caused by climate change, which demonstrates its policy implications.

Urban heat island estimation from improved selection of urban and rural stations by DTW algorithm

2021 ◽  
Author(s):  
Yonghong Hu ◽  
Gensuo Jia ◽  
Jinlong Ai ◽  
Yong Zhang ◽  
Meiting Hou ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Urban Climate ◽  
Global Scale ◽  
Heat Island ◽  
Climate Effect ◽  
Urban Heat ◽  
The Difference ◽  
Temperature Records ◽  
Dynamic Time ◽  
Selection Of

Abstract Typical urban and rural temperature records are essential for the estimation and comparison of urban heat island effects in different regions, and the key issues are how to identify the typical urban and rural stations. This study tried to analyze the similarity of air temperature sequences by using dynamic time warping algorithm (DTW) to improve the selection of typical stations. We examined the similarity of temperature sequences of 20 stations in Beijing and validated by remote sensing, and the results indicated that DTW algorithm could identify the difference of temperature sequence, and clearly divide them into different groups according to their probability distribution information. The analysis for station pairs with high similarity could provide appropriate classification for typical urban stations (FT, SY, HD, TZ, CY, CP, MTG, BJ, SJS, DX, FS) and typical rural stations (ZT, SDZ, XYL) in Beijing. We also found that some traditional rural stations can’t represent temperature variation in rural surface because of their surrounding environments highly modified by urbanization process in last decades, and they may underestimate the urban climate effect by 1.24℃. DTW algorithm is simple in analysis and application for temperature sequences, and has good potentials in improving urban heat island estimation in regional or global scale by selecting more appropriate temperature records.

A qualitative exploration on the awareness and knowledge of stakeholders towards Urban Heat Island phenomenon in Greater Kuala Lumpur: Critical insights for urban policy implications

2019 ◽  
Vol 86 ◽  
pp. 28-37 ◽  
Author(s):  
Logaraj Ramakreshnan ◽  
Nasrin Aghamohammadi ◽  
Chng Saun Fong ◽  
Amirhosein Ghaffarianhoseini ◽  
Li Ping Wong ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Urban Policy ◽  
Policy Implications ◽  
Heat Island ◽  
Kuala Lumpur ◽  
Urban Heat ◽  
Qualitative Exploration

scholarly journals A Spatio-Temporal Bayesian Model for Estimating the Effects of Land Use Change on Urban Heat Island

2019 ◽  
Vol 8 (12) ◽  
pp. 522 ◽  
Author(s):  
Xin Liu ◽  
Zuolin Xiao ◽  
Rui Liu
Keyword(s):  
Land Use ◽  
Urban Heat Island ◽  
Southwest China ◽  
Regional Scale ◽  
Heat Island ◽  
Bayesian Hierarchical ◽  
Temporal Autocorrelation ◽  
Urban Heat ◽  
Spatio Temporal ◽  
The Difference

The urban heat island (UHI) phenomenon has been identified and studied for over two centuries. As one of the most important factors, land use, in terms of both composition and configuration, strongly influences the UHI. As a result of the availability of detailed data, the modeling of the residual spatio-temporal autocorrelation of UHI, which remains after the land use effects have been removed, becomes possible. In this study, this key statistical problem is tackled by a spatio-temporal Bayesian hierarchical model (BHM). As one of the hottest areas in China, southwest China is chosen as our study area. Results from this study show that the difference of UHI levels between different cities in southwest China becomes large from 2000 to 2015. The variation of the UHI level is dominantly driven by temporal autocorrelation, rather than spatial autocorrelation. Compared with the composition of land use, the configuration has relatively minor influence upon UHI, due to the terrain in the study area. Furthermore, among all land use types, the water body is the most important UHI mitigation factor at the regional scale.

Spatiotemporal dynamics of land development intensity and summertime extreme surface urban heat island effect in Greater Shanghai (1990-2017)

2020 ◽  
Author(s):  
Hao Zhang ◽  
Xiao-yan Dai
Keyword(s):  
Urban Heat Island ◽  
Land Development ◽  
Spatiotemporal Dynamics ◽  
Policy Implications ◽  
Heat Island ◽  
Extreme Surface ◽  
Evolutionary Pattern ◽  
Urban Periphery ◽  
Urban Heat ◽  
Surface Urban Heat Island

<p>Greater Shanghai, one of China’s top megacities, is susceptible to extreme heat events during the summer. This study investigated the spatiotemporal dynamics of land development intensity and its influence on summertime extreme surface urban heat island (SUHI) effect in Greater Shanghai, during 1990 and 2017. Driven by the robust economic development and population growth, the formation of Greater Shanghai has dramatically changed from a traditionally compact city to an explosively urbanizing region in 1990–2017. The results revealed an overall increase in regional LDI of Greater Shanghai in the loss of cropland and water bodies. Simultaneously, the intensified SUHI effect was measured by the increasing relative SUHI intensity (from 1.81℃ in 1990 to 3.16℃ in 2017) and magnitude (from 306.80 km<sup>2</sup> in 1990 to 1570.56 km<sup>2</sup> in 2017). The urban-rural gradient analysis based on centric buffers and the representative transects further revealed the spatiotemporal heterogeneity of LDI and its linkage with the evolutionary pattern of the SUHI effect. As indicated, the areal extent of downtown Shanghai within the 0–15 km buffer increased by 201.70 km<sup>2</sup> in 1990–2017. However, its stably decreasing trends in LDI and associated SUHI effect were observed across the study period. In contrast, the urban periphery and exurban area, which attracted huge investment to develop the infrastructure required for population resettlement and the industrial restructure, experienced a dramatic increase in 1660.57km<sup>2</sup> of newly developed land. Concurrently, the remarkably increases in LDI and associated SUHI effect the urban periphery and exurban area were notable. Finally, focusing on the overall alarming situation of the summertime SUHI effect in Greater Shanghai, policy implications, and practical suggestions towards sustainable land development and UHI mitigation were discussed.   </p>

Characteristics of the urban heat island in a high-altitude metropolitan city, Ulaanbaatar, Mongolia

2013 ◽  
Vol 49 (4) ◽  
pp. 535-541 ◽  
Author(s):  
Gantuya Ganbat ◽  
Ji-Young Han ◽  
Young-Hee Ryu ◽  
Jong-Jin Baik
Keyword(s):  
High Altitude ◽  
Urban Heat Island ◽  
Heat Island ◽  
Metropolitan City ◽  
Urban Heat

scholarly journals Quantification of Urban Heat Island-Induced Contribution to Advance in Spring Phenology: A Case Study in Hangzhou, China

2021 ◽  
Vol 13 (18) ◽  
pp. 3684
Author(s):  
Yingying Ji ◽  
Jiaxin Jin ◽  
Wenfeng Zhan ◽  
Fengsheng Guo ◽  
Tao Yan
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Heat Island ◽  
Spring Phenology ◽  
Urban Heat ◽  
The Difference

Plant phenology is one of the key regulators of ecosystem processes, which are sensitive to environmental change. The acceleration of urbanization in recent years has produced substantial impacts on vegetation phenology over urban areas, such as the local warming induced by the urban heat island effect. However, quantitative contributions of the difference of land surface temperature (LST) between urban and rural (ΔLST) and other factors to the difference of spring phenology (i.e., the start of growing season, SOS) between urban and rural (ΔSOS) were rarely reported. Therefore, the objective of this study is to explore impacts of urbanization on SOS and distinguish corresponding contributions. Using Hangzhou, a typical subtropical metropolis, as the study area, vegetation index-based phenology data (MCD12Q2 and MYD13Q1 EVI) and land surface temperature data (MYD11A2 LST) from 2006–2018 were adopted to analyze the urban–rural gradient in phenology characteristics through buffers. Furthermore, we exploratively quantified the contributions of the ΔLST to the ΔSOS based on a temperature contribution separation model. We found that there was a negative coupling between SOS and LST in over 90% of the vegetated areas in Hangzhou. At the sample-point scale, SOS was weakly, but significantly, negatively correlated with LST at the daytime (R2 = 0.2 and p < 0.01 in rural; R2 = 0.14 and p < 0.05 in urban) rather than that at nighttime. Besides, the ΔSOS dominated by the ΔLST contributed more than 70% of the total ΔSOS. We hope this study could help to deepen the understanding of responses of urban ecosystem to intensive human activities.

scholarly journals The Urban Heat Island Analysis of Changsha-zhuzhou-xiangtan Urban Agglomeration Aased on Modis Data

2018 ◽  
Vol 53 ◽  
pp. 03045
Author(s):  
Jiao Yuan ◽  
Jingwen Li ◽  
Suxian Ye ◽  
Xiaoqiang Han ◽  
Yao Hu
Keyword(s):  
Urban Heat Island ◽  
Temperature Difference ◽  
Land Surface ◽  
Temporal Distribution ◽  
Maximum Temperature ◽  
Heat Island ◽  
Maximum Temperature Difference ◽  
High Temperature Area ◽  
Urban Heat ◽  
The Difference

Using a spatial resolution of MODIS land 1000m standard products, we can get the Land Surface Temperature.Researching for the Land Surface Temperature including spatial and temporal distribution characteristics influence factors.The results show that Spring,Summer and Autumn temperatures mainly concentrated in the central region,Winter temperature mainly concentrated in the South region.From 2001 to 2015,the maximum temperature difference is summer daytime and the difference is 17.58°C,the minimum temperature difference is autumn daytime and the difference is 11.3°C.According to the thermal field intensity distribution,compared 2005 with 2015,Urban Heat Island intensity gradually increased in 2015,the high temperature area increased and distributed more concentrated,and diffusion weakened from the city to the surrounding,the urban heat field is higher than the thermal field.That index by calculating the thermal landscape,account for a dominant position in the middle of heat distribution,and all types index in 2015 are higher than in 2005.

Using localised weather files to assess overheating in naturally ventilated offices within London's urban heat island

2011 ◽  
Vol 33 (4) ◽  
pp. 351-369 ◽  
Author(s):  
C Demanuele ◽  
A Mavrogianni ◽  
M Davies ◽  
M Kolokotroni ◽  
I Rajapaksha
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Urban Environments ◽  
Policy Implications ◽  
Weather Data ◽  
Heat Island ◽  
Urban Buildings ◽  
Air Temperatures ◽  
Urban Heat ◽  
The Impact

Urban environments typically experience increased average air temperatures compared to surrounding rural areas – a phenomenon referred to as the Urban Heat Island (UHI). The impact of the UHI on comfort in naturally ventilated buildings is the main focus of this article. The overheating risk in urban buildings is likely to be exacerbated in the future as a result of the combined effect of the UHI and climate change. In the design of such buildings in London, the usual current practice is to view the use of one generic weather file as being adequate to represent external temperatures. However, the work reported here demonstrates that there is a considerable difference between the overheating performance of a standard building at different sites within London. This implies, for example, that a building may wrongly pass or fail criteria used to demonstrate compliance with building regulations as a result of an inappropriate generic weather file being used. The work thus has important policy implications. Practical application: The Greater London Authority has recently developed, with the Chartered Institute of Building Services Engineers, guidance for developers to address the risk of overheating in buildings via the provision of weather files for London relating to three zones. While such an initiative is welcomed, it may be that a weather file tailored to the building location would be preferable. Of course, this would add further complexity to the process and a view would have to be taken as the viability of such an approach. The work presented in this article, however, suggests that serious consideration should be given to the use of tailored weather data for regulatory purposes.

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