scholarly journals The Impact of Anthropogenic Heat on Formation of Urban Heat Island and Energy Consumption Balance

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
P. Shahmohamadi ◽  
A. I. Che-Ani ◽  
K. N. A. Maulud ◽  
N. M. Tawil ◽  
N. A. G. Abdullah
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Urban Areas ◽  
Natural Ventilation ◽  
Anthropogenic Heat ◽  
Urban Structure ◽  
Heat Island ◽  
Urban Heat ◽  
The Impact ◽  
The City

This paper investigates the impact of anthropogenic heat on formation of urban heat island (UHI) and also determines which factors can directly affect energy use in the city. It explores literally the conceptual framework of confliction between anthropogenic heat and urban structure, which produced UHI intensity and affected energy consumption balance. It then discusses how these two factors can be affected and gives implication to the city and then focuses on whether actions should be taken for balancing adaptation and mitigation of UHI effects. It will be concluded by making the three important strategies to minimise the impact of UHI on energy consumption: landscaping, using albedo materials on external surfaces of buildings and urban areas, and promoting natural ventilation.

scholarly journals Review on Urban Heat Island in China: Methods, Its Impact on Buildings Energy Demand and Mitigation Strategies

2021 ◽  
Vol 13 (2) ◽  
pp. 762
Author(s):  
Liu Tian ◽  
Yongcai Li ◽  
Jun Lu ◽  
Jue Wang
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Building Energy ◽  
Design Stage ◽  
Heat Island ◽  
Building Energy Consumption ◽  
Rapid Urbanization ◽  
Substantial Impact ◽  
Urban Heat ◽  
The Impact

High population density, dense high-rise buildings, and impervious pavements increase the vulnerability of cities, which aggravate the urban climate environment characterized by the urban heat island (UHI) effect. Cities in China provide unique information on the UHI phenomenon because they have experienced rapid urbanization and dramatic economic development, which have had a great influence on the climate in recent decades. This paper provides a review of recent research on the methods and impacts of UHI on building energy consumption, and the practical techniques that can be used to mitigate the adverse effects of UHI in China. The impact of UHI on building energy consumption depends largely on the local microclimate, the urban area features where the building is located, and the type and characteristics of the building. In the urban areas dominated by air conditioning, UHI could result in an approximately 10–16% increase in cooling energy consumption. Besides, the potential negative effects of UHI can be prevented from China in many ways, such as urban greening, cool material, water bodies, urban ventilation, etc. These strategies could have a substantial impact on the overall urban thermal environment if they can be used in the project design stage of urban planning and implemented on a large scale. Therefore, this study is useful to deepen the understanding of the physical mechanisms of UHI and provide practical approaches to fight the UHI for the urban planners, public health officials, and city decision-makers in China.

scholarly journals Characterization of the subsurface urban heat island and its sources in the Milan city area, Italy

2021 ◽  
Author(s):  
Alberto Previati ◽  
Giovanni B. Crosta
Keyword(s):  
Urban Heat Island ◽  
Urban Areas ◽  
Heat Fluxes ◽  
Shallow Aquifer ◽  
Heat Island ◽  
Groundwater Temperature ◽  
City Area ◽  
Groundwater Environment ◽  
Urban Heat ◽  
The Impact

AbstractUrban areas are major contributors to the alteration of the local atmospheric and groundwater environment. The impact of such changes on the groundwater thermal regime is documented worldwide by elevated groundwater temperature in city centers with respect to the surrounding rural areas. This study investigates the subsurface urban heat island (SUHI) in the aquifers beneath the Milan city area in northern Italy, and assesses the natural and anthropogenic controls on groundwater temperatures within the urban area by analyzing groundwater head and temperature records acquired in the 2016–2020 period. This analysis demonstrates the occurrence of a SUHI with up to 3 °C intensity and reveals a correlation between the density of building/subsurface infrastructures and the mean annual groundwater temperature. Vertical heat fluxes to the aquifer are strongly related to the depth of the groundwater and the density of surface structures and infrastructures. The heat accumulation in the subsurface is reflected by a constant groundwater warming trend between +0.1 and + 0.4 °C/year that leads to a gain of 25 MJ/m2 of thermal energy per year in the shallow aquifer inside the SUHI area. Future monitoring of groundwater temperatures, combined with numerical modeling of coupled groundwater flow and heat transport, will be essential to reveal what this trend is controlled by and to make predictions on the lateral and vertical extent of the groundwater SUHI in the study area.

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 Identifying Heat Health Risks in the Urban Areas of Western Australia (WA) – An Enhanced Heat Vulnerability Assessment

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Qian Sun ◽  
Grace Yun ◽  
Ting Ling
Keyword(s):  
Risk Factors ◽  
Urban Heat Island ◽  
Health Risks ◽  
Western Australia ◽  
Urban Areas ◽  
Heat Island ◽  
Climate Change Research ◽  
Urban Heat ◽  
Heat Vulnerability ◽  
The Impact

<p><strong>Abstract.</strong> The impact of heat on health can be more significant in urban areas with more population and where the microclimate is often unintentionally modified to create the Urban Heat Island (UHI) effect. Extreme heat and UHI pose a risk to the health of vulnerable individuals, such as the elderly, the very young, and those need care. Vulnerability has become a central concept in climate change research and policy. To assess it, many studies have used equal weighted cumulative indices to aggregate multiple factors into a composite HVI (Heat Vulnerability Index) and analyse the differences and intensity across local areas and regions. However, the aggregation and equal weighting rationality, and the disregard of spatial correlation can result in inaccurate explanation on local vulnerabilities.</p><p>This study develops an enhanced index of population heat vulnerability (HVI) in Perth metropolitan area, Western Australia (WA), using environmental, demographic, and health-related risk factors for heat exposure, sensitivity and adaptive capability. Satellite derived urban heat island data and community profiles were integrated by a spatial risk assessment methodology to highlight potential heat health risk areas and build the foundations for mitigation and adaptation plans. Principal component analysis (PCA) was used to identify the key risk factors for heat vulnerability. Geographically weighted regression (GWR) were used to model the spatial relationships between temperature and other contributing factors to produce weights for calculating HVI. The index was finally mapped to produce a spatial representation of risk. The maps of spatial heat health vulnerability provide information to target heat-related health risks by aiding policy advisors, healthcare professionals, and ancillary services to develop heatwave preparedness plans at a local scale.</p>

scholarly journals Data and techniques for studying the urban heat island effect in Johannesburg

2015 ◽  
Vol XL-7/W3 ◽  
pp. 203-206 ◽  
Author(s):  
C. H. Hardy ◽  
A. L. Nel
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Sensing Data ◽  
Island Effect ◽  
Urban Heat ◽  
The City

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg’s residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

scholarly journals Opposite Effects of Aerosols on Daytime Urban Heat Island Intensity between Summer and Winter

2020 ◽  
Author(s):  
Wenchao Han ◽  
Zhanqing Li ◽  
Fang Wu ◽  
Yuwei Zhang ◽  
Jianping Guo ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Air Pollution ◽  
Urban Heat Island ◽  
Planetary Boundary Layer ◽  
Urban Areas ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Different Seasons ◽  
Urban Heat ◽  
The Impact

Abstract. The urban heat island intensity (UHII) is the temperature difference between urban areas and their rural surroundings. It is commonly attributed to changes in the underlying surface structure caused by urbanization. Air pollution caused by aerosol particles can affect the UHII by changing the surface energy balance and atmospheric thermodynamic structure. By analyzing satellite data and ground-based observations collected from 2001 to 2010 at 35 cities in China and using the WRF-Chem model, we found that aerosols have very different effects on daytime UHII in different seasons: reducing the UHII in summer, but increasing the UHII in winter. The seasonal contrast in the spatial distribution of aerosols between the urban centers and the suburbs lead to a spatial discrepancy in aerosol radiative effect (SD-ARE). Additionally, different stability of the planetary boundary layer induced by aerosol is closely associated with a dynamic effect (DE) on the UHII. SD-ARE reduces the amount of radiation reaching the ground and changes the vertical temperature gradient, whereas DE increases the stability of the planetary boundary layer and weakens heat release and exchange between the surface and the PBL. Both effects exist under polluted conditions, but their relative roles are opposite between the two seasons. It is the joint effects of the SD-ARE and the DE that drive the UHII to behave differently in different seasons, which is confirmed by model simulations. In summer, the UHII is mainly affected by the SD-ARE, and the DE is weak, and the opposite is the case in winter. This finding sheds a new light on the impact of the interaction between urbanization-induced surface changes and air pollution on urban climate.

scholarly journals Changes in Urbanization and Urban Heat Island Effect in Dhaka City

2021 ◽  
Author(s):  
A S M Shanawaz Uddin ◽  
Najeebullah Khan ◽  
Abu Reza Md. Towfiqul I ◽  
Mohammad Kamruzzaman ◽  
Shamsuddin Shahid
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Urban Areas ◽  
Clustering Algorithm ◽  
Planning Process ◽  
Urban Expansion ◽  
Heat Island ◽  
Dhaka City ◽  
Urban Heat ◽  
The City

Abstract Urbanization changes the local environment, resulting in urban heat island (UHI) effect and deteriorating human life quality. Knowledge of urban environments and temperature changes is important to outline the urban planning process for mitigation of UHI effect. The study aimed to assess the changes in urban areas and UHI effects in Dhaka city, Bangladesh from 2001to 2017, using Moderate Resolution Imaging Spectroradiometer (MODIS) daily day- and nighttime land surface temperature (LST) data from 2001to 2017. The expansion of the city was calculated using the city clustering algorithm (CCA). The temperature of the identified urbanized area was analyzed and compared with the adjacent regions. The changes in urban temperature were estimated using non-parametric statistical methods. The results showed that the Dhaka city area has grown by 19.12% and its inhabitants by 76.65% during 2001–2017. Urban expansion and dense settlements caused an increase in average temperature in some areas of Dhaka city nearly 3°C compared to that at its boundary. The day and night temperatures at Dhaka city's warmest location were nearly 7 and 5ºC, respectively, more than the coolest point outside the city. The city's annual average day- and nighttime temperature was increasing at a rate of 0.03° and 0.023°C/year over the period 2001–2017. The rising temperature would increase the UHI effect in the future, which combined with high humidity, may cause a significant increase in public health risk in the city if mitigation practices are not followed.

scholarly journals Large seasonal and diurnal anthropogenic heat flux across four Australian cities

2016 ◽  
Vol 66 (3) ◽  
pp. 342
Author(s):  
S. Chapman ◽  
J.E.M. Watson ◽  
C.A. McAlpine
Keyword(s):  
Heat Release ◽  
Urban Heat Island ◽  
Anthropogenic Heat ◽  
City Centre ◽  
Heat Island ◽  
Capital Cities ◽  
Australian Capital ◽  
Daily Average ◽  
Urban Heat ◽  
The City

Anthropogenic heat release is a key component of the urban heat island. However, it is often excluded from studies of the urban heat island because reliable estimates are not available. This omission is important because anthropogenic heat can contribute up to 4ºC to the urban heat island, and increases heat stress to urban residents. The exclusion of anthropogenic heat means the urban heat island effect on temperatures may be under-estimated. Here we estimate anthropogenic heat for four Australian capital cities (Brisbane, Sydney, Melbourne and Adelaide) to inform the management of the urban heat island in a changing climate. Anthropogenic heat release was calculated using 2011 population census data and an inventory of hourly traffic volume, building electricity and gas use. Melbourne had the highest annual daily average anthropogenic heat emissions, which reached 376 W/m2in the city centre during the daytime, while Brisbane’s emissions were 261 W/m2 and Sydney’s were 256W/m2. Adelaide had the lowest emissions, with a daily average of 39 W/m2 in the city centre. Emissions varied within and among the four cities and decreased rapidly with distance from the city centre, to 2 at 20 km from the city in Brisbane, and 15 km in Adelaide. The highest emissions were found in the city centres during working hours. The peak emissions reached in the centre of Melbourne are similar to the peak emissions in London and Tokyo, where anthropogenic heat is a large component of the urban heat island. This indicates that anthropogenic heat could be an important contributor to the urban heat island in Australian capital cities, and needs to be considered in climate adaptation studies. This is an important problem because climate change, combined with an ageing population and urban growth, could double the deaths from heatwaves in Australian cities over the next 40 years.

scholarly journals Features of Urban Heat Island in Mountainous Chongqing from a Dense Surface Monitoring Network

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 67 ◽  
Author(s):  
Ping Jiang ◽  
Xiaoran Liu ◽  
Haonan Zhu ◽  
Yonghua Li
Keyword(s):  
Urban Heat Island ◽  
Urban Areas ◽  
Surface Wind ◽  
Warm Season ◽  
Monitoring Network ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Near Surface ◽  
Urban Heat ◽  
Surface Monitoring

The spatial and temporal features of urban heat island (UHI) intensity in complex urban terrain are barely investigated. This study examines the UHI intensity variations in mountainous Chongqing using a dense surface monitoring network. The results show that the UHI intensity is closely related to underlying surfaces, and the strongest UHI intensity is confined around the central urban areas. The UHI intensity is most prominent at night and in warm season, and the magnitude could reach ~4.5 °C on summer night. Our quantitative analysis shows a profound contribution of urbanization level to UHI intensity both at night and in summer, with regression coefficient b = 4.31 and 6.65, respectively. At night, the urban extra heat such as reflections of longwave radiation by buildings and release of daytime-stored heat from artificial materials, is added into the boundary layer, which compensates part of urban heat loss and thus leads to stronger UHI intensity. In summer, the urban areas are frequently controlled by oppressively hot weather. Due to increased usage of air conditioning, more anthropogenic heat is released. As a result, the urban temperatures are higher at night. The near-surface wind speed can serve as an indicator predicting UHI intensity variations only in the diurnal cycle. The rural cooling rate during early evening transition, however, is an appropriate factor to estimate the magnitude of UHI intensity both at night and in summer.

Analysis of temperature trends and urban heat island in Madrid

2020 ◽  
Author(s):  
Gregorio Maqueda ◽  
Carlos Yagüe ◽  
Carlos Román-Cascón ◽  
Encarna Serrano ◽  
Jon Ander Arrillaga
Keyword(s):  
Urban Heat Island ◽  
Urban Areas ◽  
Surface Energy Budget ◽  
City Centre ◽  
Synoptic Situation ◽  
Heat Island ◽  
Temperature Trends ◽  
Measuring Period ◽  
Urban Heat ◽  
The City

&lt;p&gt;The temperature in the cities is affected by both global climate change and local changes due to human activities and the different land use compared to rural surroundings. These local changes, which modify the surface energy budget in urban areas, include the replacement of the natural surfaces by buildings and pavements and the heat of anthropogenic origin (heating, air conditioning, traffic). Madrid city (Spain) has a current population of near 3.3 million people and a larger metropolitan area reaching around 6.5 million people. Hence, it is affected by the phenomenon called urban heat island (UHI), which indicates that a higher temperature is found in the city compared with the surrounding rural areas. UHI is defined as the temperature difference between the urban observatory and the rural one and especially affects the minimum temperatures since urban areas cool down to a lesser extent than the neighbouring rural sites. Moreover, the intensity of the UHI is modulated by the meteorological conditions (wind, cloudiness, surface pressure, precipitation), highly associated with different synoptic situations. In this work, we use the Madrid-Retiro meteorological station as the urban one, which has regular and homogeneous data from the beginning of XX century; and the station at Barajas airport (12 km from the city centre) as well as other stations out of Madrid city (but within a range of 20 km from the city centre) as the rural stations. They all have a common measuring period from 1961 until present. The main objectives of the work are: 1) to identify temperature trends in the meteorological stations (both urban and rural); 2) to evaluate the intensity of the UHI for the different rural stations; 3) to apply a systematic and objective algorithm to classify each day in different categories (related to synoptic situation) that produce a different degree of UHI intensity; and, 4) to evaluate possible trends in the UHI intensity.&lt;/p&gt;

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