Mitigating intensity of urban heat island by better understanding on urban morphology and anthropogenic heat dispersion

2020 ◽  
Vol 176 ◽  
pp. 106876 ◽  
Author(s):  
Chao Yuan ◽  
Ayu Sukma Adelia ◽  
Shuojun Mei ◽  
Wenhui He ◽  
Xian-Xiang Li ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Urban Morphology ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Urban Heat ◽  
Heat Dispersion

scholarly journals Mitigating Intensity of Urban Heat Island by Better Understanding on Urban Morphology and Anthropogenic Heat Dispersion

2021 ◽  
Author(s):  
Chao Yuan ◽  
Shuojun Mei ◽  
Wenhui He ◽  
Ayu Sukma Adelia ◽  
Liqing Zhang
Keyword(s):  
Wind Speed ◽  
Urban Areas ◽  
Heat Emission ◽  
Urban Morphology ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Low Wind Speed ◽  
Urban Heat ◽  
Heat Dispersion ◽  
The Impact

<p>Anthropogenic heat is one of the key factors that causes intensive urban heat island due to its direct impact on ambient temperature in urban areas. Stagnated airflow due to closely packed tall buildings causes weak dilution and removal of anthropogenic heat. Consequently, research is critically needed to investigate the effect of urban morphology on anthropogenic heat dispersion and provide effective planning strategies to reduce UHI intensity, especially at the extreme scenario, such as with very low wind speed and high heat emission. This study provides scientific understanding and develops a GIS-based modelling tool to support decision-making in urban planning practice. We start from a computational parametric study at the neighbourhood scale to investigate the impact of urban morphology on heat dispersion. Site coverage ratio ( ), and frontal area density ( ) are two urban morphological parameters. Ten parametric cases with two heat emission scenarios are designed to study representative urban areas. Furthermore, based on the energy conservation within the urban canopy layer, we develop a semi-empirical model to estimate spatially-averaged in-canopy air temperature increment, in which the exchange velocity between the street canyon and overlying atmosphere is estimated by the Bentham and Britter model. The performance of the new model is validated by cross-comparing with CFD results from the parametric study. By applying this new model, the impact of anthropogenic heat on air temperature is mapped in residential areas of Singapore for both long-term annually averaged and short-term extreme low wind speed to improve urban climate sustainability and resilience.</p>

scholarly journals Quantitative Analysis of Factors Contributing to Urban Heat Island Intensity

2012 ◽  
Vol 51 (5) ◽  
pp. 842-854 ◽  
Author(s):  
Young-Hee Ryu ◽  
Jong-Jin Baik
Keyword(s):  
Urban Heat Island ◽  
Single Layer ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Impervious Surfaces ◽  
Additional Heat ◽  
Urban Geometry ◽  
Causative Factors ◽  
Urban Heat ◽  
Vertical Walls

AbstractThis study identifies causative factors of the urban heat island (UHI) and quantifies their relative contributions to the daytime and nighttime UHI intensities using a mesoscale atmospheric model that includes a single-layer urban canopy model. A midlatitude city and summertime conditions are considered. Three main causative factors are identified: anthropogenic heat, impervious surfaces, and three-dimensional (3D) urban geometry. Furthermore, the 3D urban geometry factor is subdivided into three subfactors: additional heat stored in vertical walls, radiation trapping, and wind speed reduction. To separate the contributions of the factors and interactions between the factors, a factor separation analysis is performed. In the daytime, the impervious surfaces contribute most to the UHI intensity. The anthropogenic heat contributes positively to the UHI intensity, whereas the 3D urban geometry contributes negatively. In the nighttime, the anthropogenic heat itself contributes most to the UHI intensity, although it interacts strongly with other factors. The factor that contributes the second most is the impervious-surfaces factor. The 3D urban geometry contributes positively to the nighttime UHI intensity. Among the 3D urban geometry subfactors, the additional heat stored in vertical walls contributes most to both the daytime and nighttime UHI intensities. Extensive sensitivity experiments to anthropogenic heat intensity and urban surface parameters show that the relative importance and ranking order of the contributions are similar to those in the control experiment.

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

scholarly journals Spatial and Temporal Structure of the Urban Heat Island in Seoul

2005 ◽  
Vol 44 (5) ◽  
pp. 591-605 ◽  
Author(s):  
Yeon-Hee Kim ◽  
Jong-Jin Baik
Keyword(s):  
Urban Heat Island ◽  
Temporal Structure ◽  
Temperature Data ◽  
Total Variance ◽  
Anthropogenic Heat ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Near Surface ◽  
Average Maximum ◽  
Urban Heat

Abstract The spatial and temporal structure of the urban heat island in Seoul, Korea, is investigated using near-surface temperature data measured at 31 automatic weather stations (AWSs) in the Seoul metropolitan area for the 1-yr period from March 2001 to February 2002. The urban heat island in Seoul deviates considerably from an idealized, concentric heat island structure, mainly because of the location of the main commercial and industrial sectors and the local topography. Relatively warm regions extend in the east–west direction and relatively cold regions are located near the northern and southern mountains. Several warm cores are observed whose intensity, size, and location are found to vary seasonally and diurnally. Similar to previous studies, the urban heat island in Seoul is stronger in the nighttime than in the daytime and decreases with increasing wind speed and cloud cover, but it is least developed in summer. The average maximum urban heat island intensity is 2.2°C over the 1-yr period and it is 3.4°C at 0300 local standard time (LST) and 0.6°C at 1500 LST. The reversed urban heat island is occasionally observed in the afternoon, but its intensity is very weak. An empirical orthogonal function (EOF) analysis is performed to find the dominant modes of variability in the Seoul urban heat island. In the analysis using temperature data that are averaged for each hour of the 1-yr period, the first EOF explains 80.6% of the total variance and is a major diurnal mode. The second EOF, whose horizontal structure is positive in the eastern part of Seoul and is negative in the western part, explains 16.0% of the total variance. This mode is related to the land use type and the diurnal pattern of anthropogenic heat release. In the analysis using temperature data at 0300 LST, the leading four modes explain 72.4% of the total variance. The first EOF reflects that the weakest urban heat island intensity is in summer. It is found that the urban heat island in Seoul is stronger on weekdays than weekends.

scholarly journals Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation

Climate ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 75 ◽  
Author(s):  
Ilias Agathangelidis ◽  
Constantinos Cartalis ◽  
Mat Santamouris
Keyword(s):  
Climate Change ◽  
Heat Flux ◽  
Urban Heat Island ◽  
Urban Form ◽  
Heat Exposure ◽  
Geographical Information ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Anthropogenic Heat Flux ◽  
Urban Heat

Cities worldwide are getting warmer due to the combined effects of urban heat and climate change. To this end, local policy makers need to identify the most thermally vulnerable areas within cities. The Local Climate Zone (LCZ) scheme highlights local-scale variations; however, its classes, although highly valuable, are to a certain extent generalized in order to be universally applicable. High spatial resolution indicators have the potential to better reflect city-specific challenges; in this paper, the Urban Heat Exposure (UHeatEx) indicator is developed, integrating the physical processes that drive the urban heat island (UHI). In particular, the urban form is modeled using remote sensing and geographical information system (GIS) techniques, and used to estimate the canyon aspect ratio and the storage heat flux. The Bowen ratio is calculated using the aerodynamic resistance methodology and downscaled remotely sensed surface temperatures. The anthropogenic heat flux is estimated via a synergy of top–down and bottom–up inventory approaches. UHeatEx is applied to the city of Athens, Greece; it is correlated to air temperature measurements and compared to the LCZs classification. The results reveal that UHeatEx has the capacity to better reflect the strong intra-urban variability of the thermal environment in Athens, and thus can be supportive for adaptation responses. High-resolution climate projections from the EURO-CORDEX ensemble for the region show that the adverse effects of the existing thermal inequity are expected to worsen in the coming decades.

scholarly journals Impact of a Detailed Urban Parameterization on Modeling the Urban Heat Island in Beijing Using TEB-RAMS

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Lei Jiang ◽  
Lixin Lu ◽  
Lingmei Jiang ◽  
Yuanyuan Qi ◽  
Aqiang Yang
Keyword(s):  
Land Use ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Metropolitan Area ◽  
Atmospheric Modeling ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Urban Heat ◽  
Four Levels ◽  
Geometric Morphology

The Town Energy Budget (TEB) model coupled with the Regional Atmospheric Modeling System (RAMS) is applied to simulate the Urban Heat Island (UHI) phenomenon in the metropolitan area of Beijing. This new model with complex and detailed surface conditions, called TEB-RAMS, is from Colorado State University (CSU) and the ASTER division of Mission Research Corporation. The spatial-temporal distributions of daily mean 2 m air temperature are simulated by TEB-RAMS during the period from 0000 UTC 01 to 0000 UTC 02 July 2003 over the area of 116°E~116.8°E, 39.6°N~40.2°N in Beijing. The TEB-RAMS was run with four levels of two-way nested grids, and the finest grid is at 1 km grid increment. An Anthropogenic Heat (AH) source is introduced into TEB-RAMS. A comparison between the Land Ecosystem-Atmosphere Feedback model (LEAF) and the detailed TEB parameterization scheme is presented. The daily variations and spatial distribution of the 2 m air temperature agree well with the observations of the Beijing area. The daily mean 2 m air temperature simulated by TEB-RAMS with the AH source is 0.6 K higher than that without specifying TEB and AH over the metropolitan area of Beijing. The presence of urban underlying surfaces plays an important role in the UHI formation. The geometric morphology of an urban area characterized by road, roof, and wall also seems to have notable effects on the UHI intensity. Furthermore, the land-use dataset from USGS is replaced in the model by a new land-use map for the year 2010 which is produced by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS). The simulated regional mean 2 m air temperature is 0.68 K higher from 01 to 02 July 2003 with the new land cover map.

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