ArticlePDF Available

Causes, Modeling and Mitigation of Urban Heat Island: A Review



Urbanization leads to loss of vegetation and converting pervious areas into built-up and impervious areas, and consequently, urban areas expose higher temperatures as compared to surrounding rural areas, which is called Urban Heat Island (UHI). The UHI affects the urban environment and causes heat-related diseases and mortality that have increased over the last centuries. Considering the severity of the UHI problem, enormous research has been conducted and an extensive range of literature is available on this topic. This paper reviews the causes, modelling and mitigation strategies of the UHI. The urban infrastructure and anthropogenic heat sources are the main driving factors in developing the UHI in cities. Many approaches including observation and modelling techniques are used to understand the formation, causes and mitigation of the UHI. The formation and causes of the UHI largely depend on the size, shape and urban infrastructure of the cities as well as climatic conditions. Although various modelling techniques are used to study UHI, there are still lacks in all models to precisely represent the physical phenomena and complex urban infrastructure. Many UHI mitigation strategies are examined by numerous studies, while the increased urban vegetation is a more environmentally friendly solution. The study summarizes the important features and limitations of different modelling techniques and mitigation measures of the UHI. This study also identifies research gaps and proposes areas for further research.
Earth Science
2021; 10(6): 244-264
doi: 10.11648/
ISSN: 2328-5974 (Print); ISSN: 2328-5982 (Online)
Review Article
Causes, Modeling and Mitigation of Urban Heat Island:
A Review
H. M. Imran
1, *
, Mahaad Issa Shammas
, Ataur Rahman
, Stephanie J. Jacobs
, A. W. M. Ng
Shobha Muthukumaran
Institute of Water and Environment, Dhaka University of Engineering & Technology, Gazipur, Bangladesh
Department of Civil and Environmental Engineering, College of Engineering, Dhofar University, Salalah, Sultanate of Oman
School of Engineering, Western Sydney University, Sydney, Australia
Mosaic Insights, Melbourne, Australia
College of Engineering, Information Technology and Environment, Charles Darwin University, Darwin, Australia
College of Engineering and Science, Victoria University, Melbourne, Australia
Email address:
Corresponding author
To cite this article:
H. M. Imran, Mahaad Issa Shammas, Ataur Rahman, Stephanie J. Jacobs, A. W. M. Ng, Shobha Muthukumaran. Causes, Modeling and
Mitigation of Urban Heat Island: A Review. Earth Sciences. Vol. 10, No. 6, 2021, pp. 244-264. doi: 10.11648/
Received: July 28, 2021; Accepted: October 20, 2021; Published: November 10, 2021
Urbanization leads to loss of vegetation and converting pervious areas into built-up and impervious areas, and
consequently, urban areas expose higher temperatures as compared to surrounding rural areas, which is called Urban Heat Island
(UHI). The UHI affects the urban environment and causes heat-related diseases and mortality that have increased over the last
centuries. Considering the severity of the UHI problem, enormous research has been conducted and an extensive range of
literature is available on this topic. This paper reviews the causes, modelling and mitigation strategies of the UHI. The urban
infrastructure and anthropogenic heat sources are the main driving factors in developing the UHI in cities. Many approaches
including observation and modelling techniques are used to understand the formation, causes and mitigation of the UHI. The
formation and causes of the UHI largely depend on the size, shape and urban infrastructure of the cities as well as climatic
conditions. Although various modelling techniques are used to study UHI, there are still lacks in all models to precisely represent
the physical phenomena and complex urban infrastructure. Many UHI mitigation strategies are examined by numerous studies,
while the increased urban vegetation is a more environmentally friendly solution. The study summarizes the important features
and limitations of different modelling techniques and mitigation measures of the UHI. This study also identifies research gaps
and proposes areas for further research.
UHI, Modeling, Mitigation, Urban Climate, Review
1. Introduction
Climate change has a range of consequences on human
health, urban temperature, and the environment. One of the
well-known concerns is the increasing frequency and
intensity of heatwaves linked with heat stress and increased
mortality rate [1], whereas the frequency and intensity will
increase in the future [2]. There are numerous definitions for
heatwaves [3], most involve a variety of extremely
high-temperature conditions for at least 3 consecutive days
where the averages of maximum and minimum temperatures
exceed the climatological 95
percentile referred to as a
heatwave [4]. Summertime heatwaves have also been shown
to exacerbate the UHI intensity [5, 6] and results exacerbate
biophysical hazards such as heat stroke and associated health
problems of the urban residents. Therefore, the UHI and
245 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
heatwaves as well as increased urban expansion are
considered as crucial and prominent issues for developing a
sustainable city.
The UHI phenomenon can be classified according to
different spatial scales. At the neighbourhood scale, the UHI
of individual features/blocks (including buildings and roads)
is described as the temperature differences in the intra-city
and referred to as the micro-scale UHI. The local/regional
scale UHI is described as variations of temperature across
the entire urban and surrounding rural areas. The
local/regional scale UHI is largely influenced by
geographical and climatic conditions. According to the
measurement altitudes, the UHI can be measured at
different heights within the urban boundary layer. For
instance, the surface UHI is calculated as the surface
temperature differences (usually using the surface skin
temperature) between urban and rural areas, which are
called Surface Urban Heat Island (SUHI). The near-surface
UHI can be measured as the temperature difference (air
temperature difference at 2 m above the surface) between
urban and rural areas, which is commonly referred to as the
UHI. The urban boundary layer UHI is defined as the air
temperature difference between surface and above the roof
level, while the canopy layer UHI is defined as atmospheric
temperature differences between the ground surface and
average building height of a city [7]. The UHI at the surface
and near-surface levels have been the subject of many
studies [8-11]. The SUHI generally shows the highest
intensity as compared to the pedestrian level UHI as the
surface is directly heated by solar radiation [8, 10]. The
characteristics of the UHI are strongly connected to the
incoming shortwave radiation during the day. As the rapid
cooling of the rural surrounding occurs at night and the
higher solar heat is stored by the impervious surfaces in
urban areas, the difference in air temperature at the
near-surface is highest during the evening and night.
Therefore, the UHI is most pronounced during the sunset
and at night.
The increased urbanization is the main driving factor for
losing vegetated surfaces and increasing impervious surfaces.
According to a population report of [12], 50% of the world
population lives in cities and this number will be increased
whereby 60% of people will settle in cities by 2030. As a
consequence, urbanization will be accelerated in the future.
High roughness structures reduce convective heat removal
and increase urban temperatures. The pervious areas are
replaced by high thermal conductivity materials like
concrete, bricks and bitumen. These materials in urban
infrastructure absorb and store substantial solar heat during
the day and emit heat at night, and this phenomenon is quite
favorable in developing the UHI [13]. Urban temperatures
are affected by multiple factors especially thermal
conductivity and capacities of specific heat of construction
materials, surface albedo, anthropogenic heat, urban
geometry, lack of green spaces and limited air circulation in
urban canyons [14, 15].
Considering the consequences of the UHI effects,
considerable studies have been conducted to characterize the
near-surface UHI using physics-based numerical climate
models in different scales. Furthermore, considerable UHI
mitigation strategies aiming to reduce the UHI effects have
been proposed in the literature such as modification of urban
geometry [16, 17] increasing emissivity and surface albedo
[18, 19] increasing urban vegetation and surface shading
[20-22]. Mitigation strategies aim to balance thermal energy
in the city by increasing thermal energy losses and reducing
the corresponding gains. The review of literature relevant to
the UHI mitigation strategies revealed that the increased
albedo of the urban environment and increased urban
vegetation could be promising mitigation measures [23-25].
Recent studies including these mitigation techniques have
shown a higher reduction of the UHI strength and provided
important climatic benefits [26, 27]. The objective of the
present study is to review the causes, modelling and
mitigation approaches of the UHI based on the available
literature. The study also suggests important research gaps
for future study.
Section 2 of this article describes the causes of the UHI,
while section 3 discusses the various modelling approaches of
the UHI. Section 4 examines various UHI mitigation
strategies adopted globally and section 5 recommends further
research gaps to be explored, and finally, section 6 presents a
2. Major Causes of UHI Generation
The presence of UHI has been studied in many cities
around the world and well documented over the past decade.
A wide range of factors is responsible for developing the
UHI such as urbanization, industrialization, urban
morphology, size of cities, meteorological factors and
anthropogenic activities. The detailed causes of the UHI are
discussed below.
2.1. Loss of Vegetation and Permeability
Urbanization is the major cause of the loss of vegetation
and permeability. During these development processes,
trees are cut, and naturally vegetated areas are replaced by
paving surfaces and construction materials. The loss of
vegetation in the urban areas is an important factor in
generating the UHI [28]. Vegetation cover plays a crucial
role in reducing the temperature by evapotranspirative and
shading processes (Figure 1). A significant portion of net
radiation is transformed into energy during the
evapotranspirative and photosynthesis processes when a
surface is covered with vegetation [29]. Within a vegetation
area, energy is divided largely into the latent heat fluxes and
remaining into the sensible heat fluxes (Figure 1). This
phenomenon favours decreasing air temperature, and the
division of energy mainly depends on available moisture
flow from soil, surface and atmosphere [30]. The ambient
air becomes cool during the evapotranspirative process.
More loss of naturally vegetated surfaces means a higher
loss of cooling in the city areas.
Earth Sciences 2021; 10(6): 244-264 246
Figure 1. The daytime energy exchanges between an isolated tree and its street canyon environment (Adapted from [31]).
Figure 2. Typical characteristics of (a) rural (pervious) and (b) urban
(impervious) areas in rural and city areas, and illustrates the formation of
higher temperatures in city areas.
The vegetated surfaces in rural areas display a higher rate of
evapotranspiration process, whereas the stormwater runoff is
higher in the urban areas due to impervious surfaces (Figure 2).
Therefore, sensible heat flux increases in the urban areas due to
a lower evapotranspiration process [32]. Hence, the cooling
process is mostly shut down due to lack of urban vegetation and
solar heat is stored in the urban surfaces during the day, and
consequently, the temperature in urban areas is highly increased
at night due to the emission of stored solar heat. In urban areas,
the impervious surfaces prevent absorption and filtration of
water through it and modify the natural pathway of stormwater
[33]. Urban areas always get less moisture content than rural
areas due to impervious surfaces as the construction materials
seal pervious areas, which make urban areas dry (Figure 2).
Sensible heat flux directly increases the temperature, while
latent heat flux decreases the temperature by increasing the
presence of moisture. Previous studies have found that
impervious surfaces reduce 35% of water infiltration rate and
increase 45% of stormwater runoff in the city areas [34]. As a
consequence, the natural cooling processes such as evaporation
and evapotranspiration become limited in urban areas and
cannot control the raising of urban temperature [35].
2.2. Urban Morphology and Size of Cities
Urban morphology refers to the 3-dimensional form,
spacing and orientation of urban infrastructure in the city areas,
which has a prominent role in raising the urban temperature.
Hoyano et al. [36] have demonstrated that the temperature
differences between air and building surfaces vary up to 20 -
30°C during the day in summer and 5 - 10°C at night in winter.
They have also found that the temperature differences
between the roof and ambient air vary from 14 - 25°C during
the day. Densely urban areas without vegetation can raise the
UHI intensity up to 3.8°C during extreme heatwave events in
subtropical cities [37]. They also obtained that decreasing
vegetated cover to zero resulted in a greater increase in air
temperature as compared to increasing building height. The
impact of urban morphology on the UHI was evaluated by
Tong et al. [38] for newly developed urban areas in northern
China, and their results showed the intensity of the UHI
reached up to 4.5 and 5.3 during the day and night,
respectively, due to extensive building surfaces that exposed
to solar radiation. Furthermore, Tong et al. [39] showed that
increasing building levels and reducing street width could
reduce the mean and maximum urban temperature during the
day but increase at night. The urban geometry substantially
influences wind flow, solar radiation, air humidity and water
budget. For example, large buildings and narrow streets or
247 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
openings trap the heat and it remains for a long time as it does
not get adequate ventilation facility [33]. A study has shown
that a building's wall thickness, aspect ratio, presence of
opening and surface clutter have considerable effects on the
heating and cooling of the urban surfaces [40]. The complex
urban geometry predetermines multiple reflections and
absorption of shortwave radiation due to lower albedo than the
rural environment [28] (Figure 2). Urban structures absorb
shortwave radiation (solar radiation), and consequently store
more heat themselves. This stored heat is re-radiated as
longwave radiation by the urban structures after the sun sets
[41], and the amount of heat re-radiation depends on the
nature of the underlying surfaces (e.g. geometry, albedo and
color). Typical urban albedo values generally range from 0.10
to 0.20 [15]. In addition, street orientation and its geometry
also influence urban surface temperatures. The ratios of
buildings height and road width (H:W), and the sky view
factor are two well-known factors that have considerable
importance in urban geometry [42]. The sky view factor plays
a vital role in reducing temperature around sunset while
night-time temperature was mostly influenced by the presence
of buildings [43]. The higher ratios between H:W can reduce
UHI effects at the near-surface level during the day due to
provide shading and create deep street canyons by tall
buildings [44] (Figure 2b). High-density urban areas have a
greater impact in increasing the intensity of the UHI than the
geographically expanded city [45], who obtained the lower
effect of the UHI in the geographically expanded urban areas
as compared to the areas with taller buildings and lower
height/width ratio of streets to buildings.
2.3. Properties of Construction Materials
The properties of construction materials such as thermal
emissivity (energy balance), solar reflectance (albedo) and
heat capacity greatly influence the formation of UHI. The
drastic use of manmade construction materials and release of
anthropogenic heat is the key driver in generating the UHI
[46]. Thermal emissivity measures the ability of a surface to
shed heat or emit long wave (infrared) radiation. Solar
reflectance determines the capability of a surface to reflect the
solar energy by a percentage while thermal capacity refers to a
material's ability to store solar heat. Emissivity and solar
reflectance represent the radiative properties and thermal
capacity represents the thermal properties of a material.
Low-albedo materials with high thermal conductivity raise the
temperature very quickly. Solar reflectance depends on the
materials of surfaces and their colors. Darker surfaces show
lower reflectance than lighter surfaces. Conventional roofing
materials have lower reflectance values ranging from 5 - 15%
and these materials can absorb larger energy and re-radiate 85
- 95% of that absorbed energy [47]. High emissivity surfaces
comparatively keep cooler as they release heat easily. Most
construction materials have high emissivity except metal.
Concrete and asphalt have an emissivity of about 0.90, and
consequently, these materials effectively store heat and then
slowly release heat energy [18]. Conventional roofs have
shown lower reflectance. However, these roofs have higher
thermal emissivity, while a black asphalt roof can reach
surface temperature up to 74 - 85°C [47]. In urban areas,
construction materials (e.g. stone, concrete and steel) show
higher heat capacity as compared to the materials (e. g. sand,
dry/bare soil) in rural areas, which results in double the
storage heat as compared to rural surroundings [48].
2.4. Anthropogenic Heat
Anthropogenic heat notably contributes to the formation of
the UHI. Heat can come from various human activities and is
calculated by summing the total energy used for vehicles,
heating and cooling systems, running building appliances,
power plants and industrial processes. Anthropogenic heat
production depends on the urban infrastructures and activity,
with high energy-intensive buildings and traffic volume [49].
Traffic volume is also related to urban morphology and the
vehicles have considerable influence in promoting heat as well
as air pollution in urban areas [50]. Air conditioners are
frequently used in cooling systems at home, workplace, public
places, and even in the car to maintain human thermal comfort
during hot days. The air conditioners generate substantial heat
during their compression and condensation function [51]. The
heat emitted from anthropogenic sources has a larger effect on
UHI formation particularly in denser urban areas [52].
2.5. Synoptic Conditions, Wind Flow and Cloud Cover
The uncontrollable meteorological variables, for instance,
wind flow and cloud cover, control the turbulence and radiative
exchanges in and around urban areas, which greatly influence
the urban temperature fluctuations [53]. The windy and cloudy
weather conditions reduce the occurrence and intensity of the
UHI [54], while low wind flow and little or no cloud weather
conditions promote the occurrence and higher intensity of the
UHI effect [53]. Morris et al. [53]. have shown that the wind
flow in excess of 2.0 ms
and increased cloud cover reduces the
intensity of the nocturnal UHI. Wind lessens the UHI by
advection of cool air from rural to urban areas. On the other
hand, the cloud provides natural insulation, which absorbs and
re-emits infrared radiation, and this downward radiation is
absorbed by the land surfaces and partially reduces the surface
radiative loss. This absorption makes slow the radiative cooling
process during the night and decreases temperature differences
between urban and rural areas. The strongest UHI forms under
the clear and calm and anti-cyclone (high atmospheric pressure)
weather conditions. Anti-cyclone weather conditions augment
the outgoing longwave radiation in the nocturnal radiation
budget by providing undistributed solar radiation which is
favourable for the UHI development [55]. Weaker UHIs are
formed under windy and cloudy or cyclonic (low atmospheric
pressure) weather conditions [56, 53]. Long-term (1961-1990)
changes of the UHI investigated in Prague under different
synoptic conditions [56], who obtained the increased UHI in all
seasons. The trend was larger during anti-cyclonic conditions
compared to cyclonic conditions. Another study has shown that
the strongest UHI has been developed under anti-cyclone
conditions, while a strong cyclone condition has eliminated the
Earth Sciences 2021; 10(6): 244-264 248
UHI formation [55]. Furthermore, the intensity of the UHI has
exacerbated 1.2 1.4°C at night during heatwaves than
non-heatwaves conditions [5, 6]. In contrast, environmental
factors such as wind speed & direction, surface moisture and
location of the cities affect the strength of the UHI. In contrast,
the impact of weather conditions was minor on the surface
temperature in a high-density subtropical city in Hong Kong
during the summer season [57].
3. Simulation Approaches
Numerical models have been developed to understand urban
meteorological and climatological problems including the UHI
phenomenon. However, major simplifications in the model are
mostly required because of the complex characteristics of the
UHI. Nevertheless, the computational facility has advanced
extensively in recent decades and this facility allows
researchers for solving mathematical models at large-scale.
These numerical simulation approaches are discussed below.
3.1. Energy Balance Model
The method in calculating energy budget uses the
conservation energy law for a specific volume and considers
atmospheric phenomena, velocity field and turbulence
fluctuations as heat fluxes. The urban energy budget was first
proposed by [58] at a city scale as follows:
Q = Q
+ Q
- Q
+ ∆Q
+∆ Q
Where Q is the net radiation, Q
represents the
anthropogenic energy release within a control volume, Q
the sensible heat flux while Q
is latent heat flux, ∆Q
is the
net advection through the lateral sides of the control volume,
and ∆Q
is the storage heat flux representing a mechanism for
all energy storage within elements of the control volume.
The energy balance equations are used to derive the Urban
Canopy Model (UCM) for a control volume such as two adjacent
buildings. The model estimates the exchanges of energy between
ambient and surface air in the Urban Canopy Layer (UCL). The
UCM model is capable to predict ambient and surface
temperatures of streets, pavements and buildings. All surface and
control volumes interact with each other like electric nodes, and
Equation (1) is used to each node to develop the matrix of
humidity and temperatures. The numbers of humidity and
temperature schemes depend on the nodes number in the walls of
a building [59]. The model can be developed as one, two and
three dimensional with single or multilayer schemes. A number
of energy models were developed and used in the UHI studies.
The summary of the most relevant energy balance models is
presented in Table 1 with concluding remarks. The absence of a
wind flow field is a great weakness of the energy balance model.
This model is not good enough to represent the atmospheric
phenomena and to determine the latent and sensible fluxes
because of the lack of flow pattern information.
3.2. Computational Fluid Dynamics (CFD) Model
The CFD techniques can be applied to model flows around
urban landscapes including the buildings and tree features. This
is a ground-up approach considering the lowest interactions at a
detailed level and buildings those up to a larger picture. The
CFD technique uses mathematical models following the
principle of Navier-Stokes equations, which describe the
motion of the fluids. There are three different ways as Direct
Numerical Simulation (DNS), Large Eddy Simulation (LES)
and Reynolds Averaged Navier-Stokes (RANS) to solve the
equations. These models consider all principal equations of
fluid simultaneously in urban areas, while energy balance
models consider temperature and velocity fields separately. The
CFD models simultaneously solve all the governing equations
such as temperatures, momentum and conservation of mass.
Therefore, CFD models can represent more realistic
information regarding the distribution of the UHI as compared
to the UCM model. One limitation of CFD models is that it is
impossible to model the turbulence at atmospheric and canopy
scales for the same time and length scale [60]. The CFD
simulations are generally divided into two scales (meso and
micro) based on different studies.
3.2.1. Meso-scale Model
The average effects of meso-scale modelling encompass a
wider distance or entire neighborhoods of a city. The common
range of this scale is around 1 to 2 km, although the maximum
range is 2 to 2000 km. The meso-scale is subdivided into three
categories such as meso-α (200-2000 km), meso-β (20-200
km), and meso-γ (2-20 km) [61]. The meso-scale models are
larger than the micro-scale models and smaller as compared to
global-scale models. A number of meso-scale models are used
in many studies, and this study presents a brief description of
the most relevant models with special features (Table 1). The
horizontal resolution varies from one to several hundred
kilometres. The vertical depth of the model with the Planetary
Boundary Layer (PBL) also varies from 200 m to 2 km. The
PBL develops between the earth's surface and geostrophic
wind. Large-scale interactions within the PBL are resolved in
meso-scale models where the Navier-Stokes equations are
used based on hydrostatic or non-hydrostatic theory including
atmospheric stratification effect. The equation of motion is
simplified into a balanced equation between pressure terms
and buoyancy in the hydrostatic models, while the
non-hydrostatic models use the equation of motion with the
full Navier-Stokes equations. Planetary surface directly
affects the physics of PBL, in which different physical
phenomena take place and influence temperature, velocity,
moisture and turbulence fields. Many PBL models are
proposed in the literature [62] and most of the equations are
non-linear and greatly influenced by the atmospheric
interactions and land surface properties [63, 60]. Therefore,
further studies are essential in this area to overcome the
limitations. In addition, considerable soil models [64, 65] and
moisture schemes are developed and integrated with PBL
models. The interaction between radiation and cumulus is also
considered for meso-scale models. Therefore, the coupling of
soil, moisture, radiation, cumulus and PBL models is a wide
topic for further research [60]. The meso-scale models’
249 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
accuracy depends on the temperature and wind flow boundary
condition obtained from observational techniques [66, 23].
Building canopies are treated as aerodynamic roughness in
meteorological schemes [67]. The meso-scale models’
accuracy largely depends on the land use/land cover (LULC)
data availability. Another consideration of meso-scale models
is homogeneous surface and estimation of surface properties
such as emissivity, albedo and roughness. In the last decade,
several advanced and efficient urban canopy models such as
Single Layer Urban canopy Layer (SLUCM), Building Effect
Parameterization (BEP), Building Energy Model (BEM),
TERRA_URB, Double-canyon radiation scheme (DCEP) and
Building Effect Parameterization (BEP-Tree) have been
developed and coupled to the meso-scale models for UHI
studies. The essential features of these schemes are the ability
to parameterize surface and soil properties (e.g., albedo,
emissivity, radiation, urban canyon effects, roughness length
and anthropogenic heat) with high accuracy.
Table 1. Comparison of the most relevant modelling tools for UHI studies at meso-scale.
Reference Modeling Tool Type of Tool Dimension Equation
[68] URBMET Energy Balance Model 2D Hydrostatic
[67] HOTMAC Energy Balance Model -CFD 3D Hydrostatic
[69] CSUMM Energy Balance Model 3D Hydrostatic
[70] AIST-CM-MM Energy Balance Model-CFD 1D Hydrostatic
[71] FITNAH Energy Balance Model 1D Non-hydrostatic
[72] MC2 Energy Balance Model-CFD 3D Non- hydrostatic
[73] TEB Energy Balance Model 2D Non- hydrostatic
[74] SUMM Energy Balance Model 3D Non- hydrostatic
[75] MM5 CFD 3D Non- hydrostatic
[76] RAMS CFD 3D Non- hydrostatic
[77] WRF-UCM Energy Balance Model-CFD 3D Non- hydrostatic
[78] WRF-BEP+BEM Energy Balance Model-CFD 3D Non- hydrostatic
[79] COSMO-CLM/DCEP - 3D Non- hydrostatic
[80, 81] COSMO-CLM/TERRA_URB - 3D Non- hydrostatic
[82] COSMO-BEP-Tree - 3D Non- hydrostatic
Table 1. Continued.
Reference Land Surface Scheme /
Urban Canopy Model Turbulence Scheme Limitation
[68] Monin-Obukhov Drag Equation Anthropogenic heat was only considered at the lowest level of the model.
UHI effect was simulated by considering zero building height.
[67] UCM-Monin-Obukhov 2 Equations k-1,
The model has precise forecast capabilities in three dimensional spaces over
complex terrain.
Sub-grid density fluctuations neglected during mass budget calculation.
[69] Monin-Obukhov Drag Equation, -0 Equation
It cannot be used for complex terrain and small grid sizes.
It cannot reproduce observed frontal distortions induced by urban structures.
[70] Monin-Obukhov Drag Equation, -0 Equation
Not considered anthropogenic heat.
A small difference remained between model and obsered temperature.
[71] Non-slip for moment and
heat balance Drag Equation Suitable for atmospheric flow simulation over complex terrain.
[72] Monin-Obukhov Drag Equation, -0 Equation
[73] UCM-Monin-Obukhov Drag Equation Energy budget was divided across roofs, walls and road surfaces.
Any road orientation can be considered in this scheme.
[74] UCM-Monin-Obukhov Drag Equation Assumed only vertical heat conduction.
No heat exchange between various constituent surfaces.
[75] Monin-Obukhov 2 Equations k-1 It is a limited area model.
Finer resolution varies between 10 to 50 km.
[76] Monin-Obukhov 2 Equations k-1, LES Suitable for large complex terrain.
Relatively take a long time to handle the more complex physics.
[77] UCM-Monin-Obukhov 2 Equations k-1
The heterogeneous urban landscape is considered
Single layer vegetation model missed much detail urban features during latent
fluxes calculation
[78] UCM-Monin-Obukhov 2 Equations k-1 Over estimates energy in the urban canopy for tall buildings and narrow streets
[79] DCEP - Further evaluation is required for cities with large building heights
[80, 81] TERRA_URB - Pre-defined anthropogenic heat
[82] BEP-Tree - Not precise simulation of night-time boundary layer
Earth Sciences 2021; 10(6): 244-264 250
3.2.2. Micro-scale Model
Micro-scale urban climate modelling is a very young
research endeavour. The analyses in this scale allow the
resolution in detail over a short distance commonly in the
order of around 1-100 meters. The CFD models at micro-scale
resolve the conservation equations inside the surface layer,
while the meso-scale models are assumed bulk values of
different surface properties at a horizontal scale. The
micro-scale models simulate those properties with actual
geometry considering detailed surface layer interactions. In
micro-scale models, the simulations are conducted for a small
domain at a horizontal scale of some blocks of buildings (e. g.,
a few hundred meters). The vertical atmospheric interactions
are not considered in micro-scale models and it is an
appropriate technique to study the high-Rossby number
problems [60]. Comparatively, more measurements are
needed for parameters in micro-scale due to more complicated
boundary conditions, and the measurements are highly
fluctuated at the surface layer as compared to the meso-scale
models [83]. A short description of the most relevant and
available micro-scale models is presented in Table 2 focusing
on the models' special capability and their limitations.
Table 2. Comparison of the most relevant modeling tools for UHI studies at micro-scale.
Reference Modeling Tool Type of Tool Dimension Equation
[89] UCSS CFD 3D Non- hydrostatic
[90] RAUSSSM Energy Balance Model 1D Non- hydrostatic
[91] ENVI-Met CFD 3D Non-hydrostatic
Table 2. Continued.
Reference Land Surface Scheme Turbulence Scheme
[89] Monin-Obukhov 2 Equations k-ε Circulation in the atmospheric boundary layer is not model in detail.
Reproduction of the environment is difficult with complex terrain.
[90] UCM Drag Equation, -0
Only vertical atmospheric diffusion is considered.
For a particular area, sometimes the one-dimensional model is not adequate to predict
urban microclimate.
[91] UCM 2 Equations k-ε Unable to use external forcing data
Model source code is required to troubleshoot modelling results.
3.3. Turbulence Treatment
Considerable theories such as Direct Navier-Stokes (DNS),
Reynolds Average Navier-Stokes (RANS) and Large Eddy
Simulation (LES) have been proposed in the literature for
solving the turbulent dynamics [60]. DNS solves all the spatial
scales within the flows, and consequently, it is usually far too
computationally intense for all but the smallest simulation.
LES decreases the computational intensity by low-pass
filtering, which is filtering out smaller-scaled pieces of the
solution and focusing more on the larger scaled pieces.
Although DNS and LES can achieve high accuracy results, the
application of these schemes is computationally expensive
[84]. On the other hand, RANS is widely used for turbulent
modelling because of its lower computational cost. RANS
uses mathematical techniques to simplify the Navier-Stokes
equations by separating fluctuating and averaging pieces [85].
However, this scheme was not good for high accuracy
simulation of building canopies [86]. Therefore, accurate
modelling of turbulence phenomena is still a research gap in
CFD simulation. Furthermore, the buoyancy effect in the
meso-scale turbulence model is mostly produced by the urban
surface layer, which is significant as viscous turbulence.
Currently, different types of multiple equations turbulent
schemes have been proposed [87, 88] to consider the
buoyancy effect in turbulence models.
4. Mitigation Approaches
Many mitigation approaches of the UHI, such as increasing
urban vegetation cover, sustainable urban planning, changing
architectural design, proper transportation system and
managing natural resources are investigated according to
various professional fields. This study presents the different
mitigation approaches and their effectiveness in reducing UHI
effects as reported in the previous studies. The mitigation
approaches are classified into four main categories as follows:
1. Cooling by vegetation
2. Cooling by construction materials
3. Cooling by sustainable urban infrastructures
4. Cooling by anthropogenic heat reductions.
4.1. Cooling by Vegetation
4.1.1. Garden and Parklands
Vegetation planted around a building and specified land in
urban areas are called urban gardens and parks, which can
protect the urban infrastructure from solar radiation.
Vegetation keeps the soil surrounding the building cooler, and
protects, reflects and diffuses solar radiation [92]. In the
garden and parks, evapotranspiration and shading
significantly reduce temperature and even create cool islands
in the city [93]. A study showed that average air temperatures
in the park lands were 4°C lower than in the inner city during
summer nights [94]. Parklands not only keep parks cool but
also the surrounding areas and can reduce building cooling
loads by up to 10% [24]. A field experiment was carried out by
Ca et al. [95] to quantify the park lands effect on summer
climate in Tama of New York, and a coastal metropolitan area
in Japan. The results indicated that park lands reduced 1.5°C
air temperatures in a busy commercial area at noon. They also
251 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
proposed a park vegetation and shape index that can be used to
quantify the cool island in park areas. The index values will be
helpful for the urban planners to design a sustainable urban
development plan by understanding the formation of the cool
island. Some studies reported that park lands and green spaces
reduced high temperatures in summer and stabilized the
temperature fluctuation caused by building materials [96, 24].
A general conclusion was made from previous studies [97, 98]
that green spaces were always cooler than spaces without any
green cover. However, the cooling effect depends on the size
of the park lands and solar radiation; but there is no linear
relation between the cool islands and the size of parklands. A
examined the effectiveness of different types of parks in
mitigating the UHI, and who reports that mixed forest with
grassland can reduce UHI intensity by 0.6 to 3.4°C during the
night but no cooling benefit during the hottest part of the day.
They also found substantial human thermal comfort during the
night but not in the day [99]. Furthermore, Yan et al. [100]
showed the urban park reduced 2°C air temperatures in the
centre of the park. Similar results were obtained from Cheung
et al. [101], who reported a maximum 4.9°C reduction in air
temperatures in a larger park. They also concluded that the sky
view factor, shrub and tree cover were the vital parameter in
affecting air temperature. It is noteworthy that urban parklands
and vegetation show slight warming effects between 9.00 am
to 2.00 pm at local time [102, 99].
4.1.2. Trees
Street trees can be planted in two ways such as planting a
tree directly in the ground and using cell structures, which
facilitate the required development of roots under a partial
asphalt covering. But the installation cost of cell structures is
higher than direct tree planting in the ground and needed
skilled personnel. Some researchers determined the effects of
trees on building design and urban planning [103, 104] and
proposed a new type of architectural planning for greening in
urban areas, where the aim was to find out the optimal
percentage of greeneries ratio based on various land use
categories. The optimal greeneries were compared to existing
levels of greenery of each type of land use, and their study
suggested a leaf area index of 1.36 to 10 for the shrubs, lawns
and full-grown and dense trees. These values were helpful to
recompense for the loss of green spaces stemming from urban
development. Akbari et al. [105] conducted a study and
reported that shading of trees reduced the building surface
temperatures as effective as wind flow. Surfaces shaded by
trees and vegetation can be 11 - 25°C cooler than the peak
temperatures on unshaded surfaces on a sunny day [52].
A study found a negative effect of trees and vegetation on
urban microclimates of four cities in the USA in four different
climatic conditions [106]. Their study highlighted
plants such as Conifers, which increased heating cost up to 21%
in a cold climate, whereas deciduous trees (leafless) were less
important. On the other hand, dense shade on all surfaces
decreased cooling costs by 53 - 61% per year and peak cooling
loads by 32 – 49%. They reported that if trees and vegetation
are not strategically placed, this could impact wind flow. For
instance, the 50% reduction of wind flow decreases the heating
costs 8% per year in the Salt Lake City of USA, whereas
cooling costs were increased by 11% per year because of the
obstruction of summer breezes. Thus, it is imperative to have
good knowledge about plant species and local climatic
The effect of trees and vegetation was monitored, modelled
and simulated in many studies [107-115, 93]. Most of the
studies were case studies and conducted at city-scale or
individual building scale. These study results showed that
trees and vegetation reduced air temperatures in the range of
0.28 to 4°C while the shade factor provided 80% of the
cooling effect during the summer [93]. Furthermore, the
cooling benefit of trees was experimentally investigated by
several studies [116-119], who concluded that trees provided
notable cooling benefits ranged from 1.2 to 4.1°C during the
day with a substantial increase in human thermal comfort.
However, the research findings from these studies cannot be
applied to other regions due to different city and building
characteristics. Therefore, further studies are required for
developing a framework including a set of fundamental
features based on different climatic conditions and
geographical locations.
4.1.3. Green Roofs
Green roofs reflect solar radiation by shading and
consume energy via evapotranspiration and photosynthesis
processes [120]. In summer, green roofs absorb 70 to 90%
energy of the sun by leaves during the photosynthesis
process and the remaining energy is reflected back into the
atmosphere [121]. Green roofs decrease the substantial
amount of heat transferred from the roofs to the inside of
buildings by shading and evapotranspiration. The roofs’
vegetation also extends the roofs' life span by protecting UV
radiation, bad weather effects, and temperature fluctuations
[122]. Quantitative thermal impacts of green roofs have been
investigated through field experiments and mathematical
approaches by some studies [123-126]. The interesting
conclusion of the study by [123] was, green roofs act as an
insulation layer rather than cooling the roofs, while the
conclusion made by Eumorfopoulou and Aravantinos [124]
was, green roofs were able to improve the thermal
performance of buildings but not able to replace the
insulation layer. About 30% of green roofs could reduce the
surface temperature by up to 1°C in the
Baltimore-Washington metropolitan area, where the
performance of green roofs is slightly affected by soil
moisture [8]. Their study also illustrated that the surface UHI
during the peak temperature of the day was further reduced
by 0.55°C (when a soil moisture control limit was 0.45 m
) and 0.27°C (when a soil moisture control limit was 0.35 m
). On the other hand, when the soil moisture was near the
wilting point (0.15 m
) for a dry condition, the cooling
benefits from green roofs were almost zero. Furthermore,
some specific factors, for instance, geographic location, solar
exposure, growing medium and moisture availability also
Earth Sciences 2021; 10(6): 244-264 252
influence temperature over green roofs [121]. A study was
conducted by Rosenzweig et al. (2006) in the city of New
York to test UHI mitigation scenarios by using the climate
model MM5. Although their study did not explicitly
represent the buildings in the MM5 model, it assumed the
presence of buildings through the boundary layer which
controls the transportation of heat and moisture in the surface
layer. Their study found that vegetation had greater impacts
in reducing urban temperature than urban geometry. They
also obtained that 50% of green roofs covered with grass
reduced urban surface temperatures up to 0.1 - 0.8°C, while
in Toronto in Canada 50 km
green roofs reduced local
ambient air temperatures by up to 0.5 – 2°C [127]. Thermal
benefits of green roofs were quantified through the field
measurement data by Wong et al. [128] for the tropical
environment in Singapore. They obtained that the cooling
effect of plants was responsible for the highest reduction
(4.2°C) of ambient air temperature. Another experimental
study for 20 years of a permanent survey of the cooling effect
of the extensive green roof was conducted by Kohler and
Kaiser [129], who showed a stabilization cooling effect of
1.5°C and a constant temperature within the growing media
in the green roof as compared to a traditional roof.
Regional climate models offer a real solution for evaluating
the benefits of green roofs at city scale by parameterization of
urban canopy accounting effects of green roofs on sub-grid
scales. The coupled WRF-UCM modeling systems are widely
used to investigate the effectiveness of green roofs for UHI
mitigation. The major studies considered only albedo
adjustment and increased moisture availability rather than
directly parameterized green roofs for detail physical
processes. Smith and Roebber [130] investigated green roof
efforts in Chicago using the WRF-UCM model but they did
not directly parameterize the green roofs. Rather, they
assumed a uniform increase of moisture availability and
adjusted albedo within the entire urban domain at the roof
level. Furthermore, a few studies used practical approaches to
parameterize the physics processes of green roofs in the
numerical model. A more efficient modeling approach for
evaluating the performance of green roofs was applied by Li et
al. [8] in the metropolitan areas of Baltimore-Washington DC.
They developed and used the Princeton UCM coupled with
the WRF including well-tuned physical processes in the urban
areas for investigating changes in surface and near surface
UHI effects. A physical-based green roofs algorithm in the
coupled WRF-UCM model has been employed, which was
another solution for assessing green roof impacts in reducing
UHI effects [131, 10]. Their study found that temperature,
wind and relative humidity were changed in the lower
atmosphere due to UHI reductions, as green roofs reduced
vertical mixing and boundary layer depth during the day.
Finally, although considerable research has been conducted
to investigate the effectiveness of green roofs in reducing the
urban temperature, few studies have quantitatively determined
the cost and intangible benefits of green roofs [132], and
added them into the urban design process. The key challenges
in installing green roofs are the higher construction and
maintenance costs and leakage problems in the roofs [133].
Therefore, the cost-effective and sustainable quality green
roof design is a demand of time. Further efforts should be
made to transfer research results into real project design so
that society can benefit.
4.1.4. Green Walls
Green walls are designed as vertical vegetation systems and
classified into green facades and living walls. Plants are
planted in the ground in a green facade system and the plants
climb on the wall up to 30 m. A living wall is a combination of
plants planted in a growing medium attached to a wall, and it
is a comparatively complex structure and needs a particular
impermeable membrane to protect the wall from water
damage [134]. These green walls are possible to install on any
buildings and also on fences. But precaution must be
considered for supporting the weight of the vegetation by the
walls and for the type of vegetation and potential colonizers.
Maintenance of the green walls includes pruning, weeding and
inspection of the supporting walls. Green walls develop a
microclimate environment and can substantially reduce the
temperature on buildings and energy efficiency as well [134].
Green walls also provide other additional benefits such as
capturing the suspended particles, protecting the building
from UV radiation and protecting walls from graffiti.
The effects of vertical greeneries on the buildings in
reducing temperature and saving energy were evaluated by
Wong et al. [135], who found that increasing 100% green
cover on vertical glass facade building reduced the mean
radiant temperature between 0.32 and 1.0°C with cooling load
reduction of about 10 to 31%. Their results also showed that
there was a linear relationship between leaf area index and
shading coefficient. Furthermore, their study suggested that
the shading coefficient should be lower to reduce the demand
for cooling energy. According to their study result, 50%
vertical greeneries and a shading coefficient of 0.041
decreased the envelope thermal transfer value of a glass
façade up to 40.68%. The increased vertical greeneries were
very effective to reduce air temperatures throughout a large
area. Wong et al. [136] analyzed different green facades in
Singapore, a climate similar to Amaravati. They found that in
the best case scenario the reduction in temperature from the
green façade could only be felt 60 cm away from the wall.
Beyond that, there was no difference in temperature.
4.2. Construction Materials
Two parameters of construction materials play an important
role in reflecting solar radiation. The first is albedo which
refers to the ratio of incoming solar radiation to reflective solar
radiation and it is represented on a scale of 0 to 1. The second
is emissivity and it measures the effectiveness of a material in
emitting solar energy as thermal radiation at specific
temperatures and wavelengths. Emissivity is also represented
on a scale of 0 (perfect reflector) and 1 (perfect emitter).
Albedo and emissivity indicate the radiative properties of
construction materials. The higher value of albedo and
emissivity of materials indicates that they are less likely to
253 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
store heat and reflect into the atmosphere [15]. Urban
temperature distributions are affected by the radiation budget
over urban areas. The incoming solar radiation is absorbed by
major urban surfaces including streets, roofs, building
surfaces, squares, and transformed into sensible heat [137, 102,
8]. This storage heat is re-radiated as long wave radiation,
whereas the wave intensity depends on the albedo, emissivity,
thermal inertia and percentage of visible surfaces to the sky.
Further urban infrastructure made of different construction
materials and associated UHI is described in the following
4.2.1. Cool Roofs
Cool roofs have two most important features such as higher
albedo (indicates reflectance of solar radiation) and thermal
emissivity, which plays a substantial role in reducing
temperature. The materials of cool roofs should reflect solar
energy above 70% and solar emissivity 80% [138]. According
to the findings of Gartland [138], cool roofs heat up to 40 -
60°C. The differences between surface and ambient air
temperatures were greatly varied between cools roofs and
traditional roofs, where the differences were 50°C for
traditional roofs and 10°C for cool roofs [92, 139, 28, 140].
Synnefa et al. [140] experimentally investigated 14 different
reflective coatings for thermal performance and optical
properties. Temperature sensors, data logging systems and
infrared thermography procedures were applied to their
experimental study. Their study results showed cool coating
reduced 4°C surface temperature during summer conditions
and 2°C during the night. Also, the statistical analysis from
their study showed that the thermal behavior of cool coatings
and cool materials was affected by thermal reflectance during
the day and by emissivity during the night, which affects the
performance of the cool coatings and cool materials.
Furthermore, Synnefa et al. [141] conducted an experimental
study for measuring the temperature differences between 10
conventionally pigmented coatings and 10 prototype cool
colored coatings. The highest surface temperature difference
was 10.2°C during summer against the solar reflectance of
0.22 while the temperature difference was 1°C during winter.
Therefore, cool colored coatings avoid any heating penalty. A
study was carried out by the Lawrence Berkeley National
Laboratory and the Oak Ridge National Laboratory to
investigate the properties of various color pigments, and the
study discovered certain color pigments could reflect around
half of the solar radiation of close infrared radiation [142]. The
smooth white coatings can increase the lifespan of cool roofs
and albedo values from 0.04 to 0.80 as compared to roofs
covered by black asphalt surfaces [27]. Cool roofs require
regular cleaning and maintaining otherwise, their reflectance
will be reduced. For a white cool roof, solar reflectance was
reduced from 0.80 to 0.60 due to biomass and dust deposits
[143]. An experimental study was carried out by Watkins et al.
[144] in London to investigate the effectiveness of reflective
walls (albedo = 0.50), where the study result revealed that
reflective walls reduced surface temperature approximately 3
to 4°C at the hottest time of the day. Extensive field
experimentation and modelling studies on cool roof penalties
were conducted in the United States, Canada, Japan and
various parts of Europe. Indeed, considerable further studies
are needed to address the cool roof penalties in hot vs. cold
conditions based on different geographical locations. A few
studies focused on the decrease in reflectivity of cool roofs
because of weathering, microbial growth and accumulation of
dirt and showed that regular washing of cool roof could restore
90 - 100% of the initial reflectivity [145, 146, 112]. The
bioclimatic architectural building design is a very effective
approach to protect the building from overheating in summer,
in which a special effort is required to ensure thermal comfort
for the residents [147]. Susca [148] conducted a study to
assess the life cycle of black and white roofs, and it was found
that the life cycle of white roofs is longer than black roofs.
Cool roofs are an environmentally friendly and cost-effective
UHI mitigation strategy [149].
The performance of 5 colors of thin layer asphalt was
examined by Synnefa et al. [150] using computational fluid
dynamics modelling tool PHOENICS-CFD. The modelling tool
was developed based on Navier Stokes equations and it is an
efficient tool for simulating fluid flow. Their study found the
daily surface temperature fluctuation was 18°C for black
asphalt and 8°C for colored thin layer asphalt [150]. A review
on cool roofs for mitigating UHI effects and improving thermal
comfort revealed that the cool roof materials were responsible
for decreasing the flow of heat flux into the buildings and
reducing the high temperatures [27]. Some cool roof materials
such as cementitious or elastomeric cool coatings, cool shingle–
ply roofing systems made by various materials and clay
shingles ensuring higher albedo were suggested to use in cool
roof system [138]. Recently, modelling studies have
extensively evaluated the effectiveness of cool roofs in
mitigating UHI [131, 8, 10]. Li et al. [8] have reported the
increasing albedo (up to 0.9) of 50% cool roofs have a
substantial impact in reducing surface UHI (2.5°C) over
Baltimore-Washington metropolitan area. Imran et al. [131]
found that cool roofs can reduce roof surface temperature by 2.2
to 5.2°C for increased albedo 0.50 to 0.85°C during the day in
the city of Melbourne while the maximum roof surface
temperature reduced by 8°C in the high intense urban areas in
Chicago Metropolitan areas in the USA for 100% cool roofs
[10]. In addition, this study summarizes the UHI mitigation by
cool roofs in Table 3.
4.2.2. Cool Pavement
Cool pavements reflect a substantial amount of the solar
radiation away from the urban surface to the atmosphere
because of higher albedo. Albedo is defined as the ratio of
reflected radiation from a surface to incident radiation. Albedo
value indicates how much energy is absorbed or reflected
from the surface of a material. The higher albedo values
indicate the higher energy is reflected into the atmosphere and
lower energy is absorbed by the material. Therefore, it is very
important to select materials properties for new construction
or retrofitting of buildings to mitigate UHI effects [151]. Air
temperature in summer is reduced by up to 4°C by increasing
Earth Sciences 2021; 10(6): 244-264 254
surface albedo from 0.25 to 0.40 [15]. A substantial number of
studies examined the capabilities of cool pavements in
reducing the urban temperature by increasing albedo [152,
107, 153-157]. Their studies showed that cool pavements can
reduce surface temperature by up to 10°C when albedo
increased by 0.25 [152, 107]. Climate models revealed that the
infrared radiation from cool pavements was absorbed within
200m of the planetary boundary layer, and thus it affects
near-surface temperature [15]. A study carried out by
Pomerantz et al. [155] showed that cool pavement reduced air
temperature by 0.6°C if reflectance of pavement has increased
from 10% to 35%. Recently, high performance roof coating
materials such as light color gravel and tiles, elastomeric and
polyurea membranes have been developed by the roofing
materials industry in which all products have a higher albedo
than ordinary materials [142]. Furthermore, a brief
summarized mitigation strategy of the UHI by cool pavements
is presented in Table 3. Urban paved areas such as roads,
parking areas and school yards are frequently covered with
asphalt or other types of dark materials which can contribute
to reaching surface temperatures of 60 - 80°C on hot days in
summer, and these infrastructures make a significant
contribution to developing the UHI [153]. The paved areas can
occupy up to 45% area of a city [52]. In order to minimize
these effects, the following mitigation measures can be taken:
Reflective pigments can be added to concrete (e.g. colored
concrete) and asphalt (e.g. colored asphalt) to increase the
Generally concrete has good albedo values (0.30 - 0.40),
thus a concrete layer (2.5 cm to 10 cm) can be applied on the
top of an asphaltic pavement in good condition. This strategy
can keep the surface cooler and be able to accommodate
different types of vehicles [141].
Currently, asphalt roads are composed of around 85%
mineral aggregates including 15% bitumen. The albedo value
of bitumen can be increased by the reverse process of the
paving system, i.e. applying thinner layer bitumen on the
surface of high albedo aggregates placed on the road.
However, this pavement is only suitable for low-speed and
light-weight vehicles.
Several studies focused on the cost associated with cool
pavements construction [107, 92, 155, 158]. A few studies
examined the cost for individual cool roof technology, which
depends on several factors including climate type, geographic
location, and pavement size and design life. Therefore, further
research is required on a cost analysis that highly depends on a
range of these complex factors. The cost analysis of cool
pavements indicated that the construction cost of cool
pavements increased up to $10 psqf compared to traditional
asphalt pavement [155]. Most studies emphasized the range of
benefits and drawbacks (e.g. costing) which need to be
accounted for cool pavements.
4.2.3. Permeable Pavement
The primary cooling mechanisms of permeable pavements
are heat resistance, evaporative cooling and reflection if
reflective materials are used [159-161]. One of the promising
applications of permeable pavement is parking lands. The
parking areas are generally paved with asphalt and low-albedo
materials that absorb solar heat and increase the surface
temperatures. Parking areas can be covered and shaded by
planting vegetation around the perimeter and entire surfaces
providing different modular systems consisting of concrete or
PVC or other construction materials that permit plant growth.
All these modules are installed on a pervious soil layer for
promoting the natural percolation of storm water into the
groundwater systems. A study conducted by Doulos et al. [154]
considered 93 pavement materials that are used very often in
urban areas to reduce ambient air temperatures. Their study
determined that the albedo factor of each material was mainly
responsible for the variation of average daily temperature. It
was also found that dark and rough surfaces in the urban areas
absorbed more solar heat than smooth light-colored and flat
surfaces. Permeable pavements strategy is a good solution for
stormwater management and can be used to reduce UHI and
improve a comfortable thermal environment for the urban
residents by evaporative cooling. Table 3 additionally
summarizes different studies on permeable pavement and the
UHI. This cooling efficiency depends on the evaporation rate,
which is significantly affected by higher water availability
near the surface or large moisture exposure to the atmosphere
[8]. A surface with good permeability and increased air voids
is a good way to increase moisture in the atmosphere to
maintain the evaporative cooling process.
4.2.4. Thermal Inertia
Thermal inertia indicates its ability to absorb and store heat
and defer a certain time period for its release, known as a lag
phase. Inertia is measured by the penetration (diffusivity)
speed of heat into a material and ability of the material to
absorb or release (effusivity) heat. The higher diffusivity
allows more rapidly passing heat through the thickness of the
material. The higher effusivity materials absorbed more heat
without substantially warming up. High inertia materials
absorb and store excess heat and prevent heat from being
transmitted to the ambient air and, consequently, improving
thermal comfort [162]. It is necessary to place the high
thermal inertia walls where there is sunshine, and a minimum
of 50% of the walls of a building should have higher inertia to
get the maximum cooling benefits [163].
Table 3. The most effective UHI mitigation approaches in the literature.
Location Mitigation Approach Modeling Tool
[10] Chicago metropolitan area Green and cool roofs WRF-UCM
[8] Washington, USA Green roofs WRF-PUCM
[101] Hong Kong Urban Parks Experimental
[128] Singapore Rooftop garden Statistical Analysis
255 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
Location Mitigation Approach Modeling Tool
[140] Athens, Greece Reflective coatings ANOVA F-Statistical Analysis
[131] City of Melbourne, Australia Green and cool roofs WRF-UCM
[164] City of Melbourne, Australia Vegetated patches WRF-UCM
[118] Hong Kong Woodland Experimental
[158] Los Angeles, USA Vegetation, cool roofs and lighter color pavement DOE-2 and CSUMM
[102] City of Melbourne, Australia Vegetation and cool roofs WRF-PUCM
[117] Hong Kong Trees Experimental
[165] Los Angeles, USA Increased vegetation and albedo CSUMM
[159] California, USA Permeable asphalt pavement DBOLS-Statistical Analysis
[23] Hong kong, China Planting and vegetation MM5
[129] University of Applied Sciences Neubrandenburg
Green roof Experimental
[116] Melbourne Trees Experimental
[24] Singapore Park lands ENVI-met
[119] Hong Kong Trees Experimental
[166] Tokyo, Japan Green walls Coupled MM-CM-BEM
[167] City of Vienna, Austria Green and blue spaces MUKLIMO_3
[135] Singapore Vertical green walls STEVE Model
[168] Putrajaya, Malaysia Vegetation and water body WRF-UCM
[169] London, UK Stack ventilation 3TC Model
[170] Tokyo, Japan Photovoltaic Canopies Numerical PV-panel Heat Balance Model
[171] Nevada, USA Reflective Roofs Regression Analysis
[172] Spain, Madrid Green roofs Sima Pro-LCI
[173] Qatar Green roof and walls GCM and CCWorldWeatherGen tool
[174] Tokyo, Japan Humidification and albedo increase at
building-wall surfaces Coupled CM-BEM
[115] Kobe, Japan Green roof and high reflection roof Numerical Equation
[175] Arizona, USA Photovoltaic Canopies Statistical Analysis
[115] Kobe, Japan Green roof and high reflection roof Numerical Equation
Table 3. Continued.
Reference Max
Air Temp. Reduction Max
Surface Temp. Reduction Max
Energy Saving
[10] - 8.34°C and 10.09°C
[8] 1°C and 0.7°C ~7°C for both roofs -
[101] 4.9°C - -
[128] - 4.2°C -
[140] - 4°C -
[131] 1.4°C and 1.6°C 3.8°C and 5.2°C
[164] 3.7°C 4.2°C -
[118] 4.1°C - -
[158] 3°C 10%
[102] ~ 2.4°C and 1°C - -
[117] 2.1°C - -
[165] 2°C 10%
[159] 0.45°C 1.6°C -
[23] 1.6°C -
[129] 1.5°C - -
[116] 1.5°C - -
[24] 1.3°C 10%
[119] 1.2°C - -
[166] 1.2°C 40%
[167] 1°C -
[135] 1°C -
[168] 0.53°C -
[169] Significant 10%
[170] - 10%
[171] - 31-39 (Wh/m
[172] - 10%
[173] - - 3%
[174] Significant 3%
[115] Significant -
[175] Significant - -
[115] Significant -
Earth Sciences 2021; 10(6): 244-264 256
4.3. Sustainable Urban Infrastructure
4.3.1. Urban Design and Solar Radiation
Urban areas are composed of building surfaces, roofs, and
pavements that store short wave solar heat during the day and
re-radiate as longwave radiation during the night. Capturing
solar radiation, light exploration and protection systems are
important urban design considerations to reduce energy
demand, environmental pollution and UHI effects [176, 177].
The changes in building design, orientation and urban
geometry have affected solar access [178]. Mesa et al. [179]
suggested that the optimum separation ratio between the
buildings and their heights is from 0.67 to 1. Densely urban
morphology enables interior spaces to achieve a lower level of
natural illumination quality and intensity [179]. Modified
buildings reduced the temperature effect and improved the
thermal comfort of buildings for the residents [180]. The
effects of design, geometry and orientation of buildings with
respect to capturing solar radiation and using them for energy
in building facades and roofs were analyzed [181] who
illustrated that the 30% and above 50% area of the facade and
roof were suitable for passive and active solar techniques
application, respectively. The relation between built forms,
density and solar potential was examined by [182], who
suggested that the optimum design was those with randomness
in the vertical and horizontal layout with less site coverage.
Another study established a relationship between building
height and width [183], who showed an increased building
width decreased the influence of the orientation of lower
height buildings.
4.3.2. Urban Design and Air Flow
Urban canyon illustrated by taller buildings and narrow
streets is responsible for creating multiple reflections of the
solar radiation when solar heat incidents on the top of the
canyon during the day (Figure 2b). After that, the reflected
energy was attracted by the exterior walls, and as a result, it
decreased the overall albedo in the city and raised the air
temperature at the top canyon [184]. In addition, this
narrow urban canyon reduced wind velocity and prevented
free air circulation at night, and consequently, generated
nocturnal UHI effects [185]. On the other hand, tall
buildings provided shelter for the lower canyon during the
day and reduce air temperatures 3 - 5°C at the near-surface
level as compared to the corresponding top canyon [184].
Therefore, the height and density of buildings substantially
affect the UHI. Furthermore, roughness length plays an
important role in estimating the wind flow over urban
canyons. Wind flow and pollutants dispersion are greatly
affected by canyons' geometry (height to width ratio) [186,
187]. The high degree of pollutants mixing needs higher
turbulence. Therefore, it is better to orthogonally orient a
building towards the wind flow to control the turbulences
and wind at street level [188]. Therefore, wind speed and
direction also play a vital role in influencing the UHI
4.3.3. Windows of Buildings
Windows control the thermal insulation of a building during
summer and winter. Windows reduce the temperature
difference between the inner and outside of the buildings and
reduce the UHI intensity in the building. Smart low
e-windows can reduce gaining solar heat inside buildings.
These windows can adapt to seasonal variation and the
inclination angle of the incident of solar radiation. It limits
solar radiation in summer and allows light to pass during
winter [189]. Double and triple glazed windows have 16 to 20
mm air space which provides higher insulation capacity by
conduction and convection processes and hence reduces UHI
intensity for the residents in the building. According to the
findings of a study, self-adhesive plastic films show excellent
performance, which can reduce 98% of UV radiation and 75%
of thermal solar heat [190]. Their study also illustrated that the
use of low solar gain windows saved heating and cooling costs
by 8-10%, whereas high solar gain windows reduced by up to
13-17%. In addition, lower and higher solar gain windows can
resolve the overheating in summer.
4.4. Anthropogenic Heat Reduction
4.4.1. Reduction of Air Conditioner Heat
Solar cooling systems can be an excellent alternative to air
conditioners. Solar collectors gather solar energy and act as a
low-temperature (80 to 120°C) source using absorption
machines [189], which are potential alternatives to traditional
compression machines generally used in conventional air
conditioning systems. As a result, this solar cooling system
will be able to reduce the UHI by absorbing solar heat and
additionally, heat emission will be reduced from air
conditioners. Furthermore, ground source heat pumps or
geothermal heat exchangers can be used as very effective
energy savers throughout the year for building cooling and
heating operations [191]. The system uses ground energy
rather than fossil fuel for the cooling and heating operations of
the buildings, which helps reduce carbon emissions and
consequently reduces urban warming. Geothermal heat
exchangers can reduce air temperature up to 5 - 8°C while
solar air conditioning reduces from 7 - 12°C [189]. Therefore,
the use of air conditioners can be reduced by using alternative
cooling options, which will result in a reduction of
anthropogenic heat and the UHI effects.
4.4.2. Reduction of Urban Vehicles
Anthropogenic heat contributed to raising the UHI up to 2 -
3°C in the CBD of a city both during the day and night [15].
Building appliances and vehicles also contribute to generating
UHI effects. Halogen and incandescent bulbs produce
considerable heat which is absorbed and stored by building
walls and materials inside them. Therefore, natural ventilation
(e.g. windows, walls with air vents) should be designed in
such a way where buildings will need less energy for lighting
and cooling systems. The more practice of public transport
and less practice of individual cars can reduce temperatures in
257 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
cities. Motorized vehicles emit heat in the lower atmosphere.
A car uses more than four times as much energy per person per
kilometre as a bus and twice as much as a train [189]. Heat
emission by vehicles is trapped in poorly ventilated urban
canyons and promotes to form of urban smog and increases
global warming [144]. The CNG (Converted Natural Gas) can
be better fuel for cars instead of diesel and petrol for reducing
air temperature and pollution as well [33]. Therefore, an
appropriate urban transportation system is important to
minimize the heat resulting from motorized vehicles.
Therefore, more energy-efficient and lower polluting public
transport services can reduce UHI effects and improve air
quality [192]. In addition, the development of an active
transport infrastructure offers easy access for people to travel
around by bicycle and on foot which reduces not only
anthropogenic heat production by motorized vehicles but is
also helpful for human health.
5. Research Gaps
The UHI problems are manifold. It has substantial effects
on the urban environment, climate, services, public health and
national economy. Considerable studies have been carried out
to model urban climate and evaluate the effectiveness of
various mitigation approaches in reducing UHI effects.
Increased vegetation cover has been considered the most
beneficial and sustainable mitigation approach among all
mitigation strategies. However, the present study has
identified the following research gaps on UHI studies for
future research:
1) Further research should be conducted to find out the
effective techniques to integrate the meso-scale and
micro-scales models.
2) Further improvement is still needed in representing the
physical processes in the urban canopy layer in the
Physics-based climate models.
3) The coupling techniques of radiation, soil, moisture,
cumulus and PBL models are possible topics for further
4) Observational studies are still limited because of time
and costs. The total life cycle analyses of different types
of urban forest are necessarily based on local
construction practices, ecosystem, and climatic factors.
5) Further research should be carried out to quantify the
life-cycle cost and benefits of green and cool roofs in
improving quality of life, ensuring biodiversity and
sustainable ecology, and promoting recreational use at
different climatic and geographic locations.
6) Field experiments should be done to collect observed
climate data for more refinement of climate models to
estimate the direct and indirect benefits of green roofs.
7) Research should be carried out to characterize the
complex set of variables such as green roof cover ratio,
tree size, tree type, orientation, climate types and
geographical locations in developing UHI mitigation
8) Research should be carried out to examine the
effectiveness of trees for UHI mitigation considering a
complex set of variables: trees size, species of trees,
trees health, locations and climatic conditions. Still,
there is a need for further research on cost-benefit
analysis in terms of the life-cycle of different types of
trees in mitigating UHI in urban areas.
9) Many studies highlight only the cooling potential of
green walls and vertical greeneries. Still, there is scope
for further studies on the cost-benefit analysis.
10) Further research should be carried out by focusing on
the effectiveness of cool pavements and their benefits at
a large city scale, considering complex urban settings
(e.g. building materials and sky view factors).
11) Research should be conducted to development of low
thermal conductivity materials and their impacts on the
6. Conclusion
This review presents basic concepts, various modelling
techniques and mitigation strategies in relation to the UHI
phenomenon. The major factors and their significance have
been discussed for generating and mitigating the UHI. Solar
radiation and anthropogenic heat due to urbanization play a
key role in generating the UHI in cities. Currently,
researchers are focusing on the simulation approaches due to
advanced computing systems. However, recently developed
modelling tools have some limitations such as complexity of
urban details, theoretical weakness, high computational cost
and shortcomings of high-resolution model inputs. A
combination of different models and further development of
modelling tools would be a better way to overcome these
shortcomings. Furthermore, urban greeneries are a very
effective and sustainable UHI mitigation strategy for
compact cities marked by taller buildings and paving covers.
The key mechanism was shading and evapotranspiratve
cooling. The larger size of urban parks and a greater
percentage of urban vegetation provide substantial cooling
benefits and human thermal comfort as compared to smaller
parks. Green and cool roofs show notable performance in
reducing UHI effects, but fewer studies investigated green
walls' cooling benefit and life-cycle cost-benefit analysis. It
is recommended to combine several UHI mitigation
strategies (e.g., parks, trees, cool pavement, and green roofs)
rather than an individual mitigation strategy. It is also
concluded that further studies are still needed in developing
sustainable UHI mitigation (e.g. increased vegetation cover)
considering cost-benefit analysis with long-term real
measurements at micro and meso-scales. Cities in the world
are vastly different. Therefore, the mitigation measures
should meet the requirements of the individual city
Conflict of Interest
All the authors do not have any possible conflicts of
Earth Sciences 2021; 10(6): 244-264 258
[1] Tan, J., Zheng, Y., Song, G., Kalkstein, L., Kalkstein, A., Tang,
X., 2007. Heat wave impacts on mortality in Shanghai, 1998 and
2003. International journal of biometeorology 51, 193-200.
[2] Perkins, S. E., Alexander, L. V., Nairn, J. R., 2012. Increasing
frequency, intensity and duration of observed global heatwaves
and warm spells. Geophysical Research Letters 39 (20).
[3] Perkins-Kirkpatrick, S., Alexander, L., 2013. On the
Measurement of Heat Waves. Journal of Climate 26, 4500-4517.
[4] Nairn, J., Fawcett, R., 2011. Defining heatwaves: heatwave
defined as a heat-impact event servicing all. Europe 220, 224.
[5] Li, D., Bou-Zeid, E., 2013. Synergistic Interactions between
Urban Heat Islands and Heat Waves: The Impact in Cities Is
Larger than the Sum of Its Parts. Journal of Applied
Meteorology and Climatology 52, 2051-2064.
[6] Rogers, C., Gallant, A., Tapper, N., 2019. Is the urban heat
island exacerbated during heatwaves in southern Australian
cities? Theor. Appl. Climatol. 137.
[7] Oke, T. R. (1995). The Heat Island of the Urban Boundary Layer:
Characteristics, Causes and Effects. Wind Climate of Cities. J. E.
e. a. Cermak. Dordrecht, Boston, Kluwer Academic Publishers:
[8] Li, H., Harvey, J., Ge, Z., 2014. Experimental investigation on
evaporation rate for enhancing evaporative cooling effect of
permeable pavement materials. Construction and Building
Materials 65 (0), 367-375.
[9] Morris, K. I., Chan, A., Morris, K. J. K., Ooi, M. C., Oozeer, M.
Y., Abakr, Y. A., Al-Qrimli, H. F. (2017). Impact of urbanization
level on the interactions of urban area, the urban climate, and
human thermal comfort. Applied geography, 79, 50-72.
[10] Sharma, A., Conry, P., Fernando, H. J. S., Hamlet, A. F.,
Hellmann, J. J., Chen, F., 2016. Green and cool roofs to mitigate
urban heat island effects in the Chicago metropolitan area:
evaluation with a regional climate model. Environmental
Research Letters 11 (6), 064004.
[11] Yang, L., Niyogi, D., Tewari, M., Aliaga, D., Chen, F., Tian, F.
and Ni, G. (2016). Contrasting impacts of urban forms on the
future thermal environment: example of Beijing metropolitan
area. Environmental Research Letters, 11 (3), 034018.
[12] Bureau, P. R., 2005. World Population Data Sheet. Population
Reference Bureau.
[13] Arnfield, A. J., 2003. Two decades of urban climate research: a
review of turbulence, exchanges of energy and water, and the urban
heat island. International Journal of Climatology 23 (1), 1-26.
[14] Oke, T. R., Johnson, G. T., Steyn, D. G., Watson, I. D., 1991.
Simulation of surface urban heat islands under ‘ideal’ conditions
at night part 2: Diagnosis of causation. Boundary-Layer
Meteorol 56 (4), 339-358.
[15] Taha, H., 1997. Urban climates and heat islands: albedo,
evapotranspiration, and anthropogenic heat. Energy and
Buildings 25 (2), 99-103.
[16] Fahmy, M., Sharples, S., 2009. On the development of an urban
passive thermal comfort system in Cairo, Egypt. Building and
Environment 44 (9), 1907-1916.
[17] Pearlmutter, D., Bitan, A., Berliner, P., 1999. Microclimatic
analysis of “compact” urban canyons in an arid zone.
Atmospheric Environment 33 (24–25), 4143-4150.
[18] Golden, J. S., Kaloush, K. E., 2006. Mesoscale and microscale
evaluation of surface pavement impacts on the urban heat island
effects. International Journal of Pavement Engineering 7 (1),
[19] Sailor, D. J., 1995. Simulated Urban Climate Response to
Modifications in Surface Albedo and Vegetative Cover. Journal
of Applied Meteorology 34 (7), 1694-1704.
[20] Sailor, D. J., Dietsch, N., 2007. The urban heat island Mitigation
Impact Screening Tool (MIST). Environmental Modelling &
Software 22 (10), 1529-1541.
[21] Synnefa, A., Dandou, A., Santamouris, M., Tombrou, M.,
Soulakellis, N., 2008. On the Use of Cool Materials as a Heat
Island Mitigation Strategy. Journal of Applied Meteorology and
Climatology 47 (11), 2846-2856.
[22] Yaghoobian, N., Kleissl, J., Krayenhoff, E. S., 2009. Modeling
the Thermal Effects of Artificial Turf on the Urban Environment.
Journal of Applied Meteorology and Climatology 49 (3),
[23] Tong, H., Walton, A., Sang, J., Chan, J. C. L., 2005. Numerical
simulation of the urban boundary layer over the complex terrain
of Hong Kong. Atmospheric Environment 39 (19), 3549-3563.
[24] Yu, C., Hien, W. N., 2006. Thermal benefits of city parks.
Energy and Buildings 38 (2), 105-120.
[25] Zoulia, I., Santamouris, M., Dimoudi, A., 2008. Monitoring the
effect of urban green areas on the heat island in Athens.
Environmental Monitoring and Assessment 156 (1), 275.
[26] Fintikakis, N., Gaitani, N., Santamouris, M., Assimakopoulos, M.,
Assimakopoulos, D. N., Fintikaki, M., Albanis, G., Papadimitriou,
K., Chryssochoides, E., Katopodi, K., Doumas, P., 2011.
Bioclimatic design of open public spaces in the historic centre of
Tirana, Albania. Sustainable Cities and Society 1 (1), 54-62.
[27] Santamouris, M., Synnefa, A., Karlessi, T., 2011. Using
advanced cool materials in the urban built environment to
mitigate heat islands and improve thermal comfort conditions.
Solar Energy 85 (12), 3085-3102.
[28] Rizwan, A. M., Dennis, L. Y. C., Liu, C., 2008. A review on the
generation, determination and mitigation of Urban Heat Island.
Journal of Environmental Sciences 20 (1), 120-128.
[29] Wong, N. H., Yu Chen, 2005. Study of green areas and urban
heat island in a tropical city. Habitat International 29 (3),
[30] McPherson, E. G., 1994. Cooling urban heat islands with
sustainable landscapes, In: Platt RH, Rowntree RA and Muick
PC (eds.). The Ecological City: Preserving and Restoring Urban
Biodiversity, University of Massachusetts Press, pp. 151-172.
[31] Oke, T. R., Crowther, J. M., McNaughton, K. G., Monteith, J.
L., Gardiner, B., 1989. The Micrometeorology of the Urban
Forest [and Discussion]. Philosophical Transactions of the
Royal Society of London B: Biological Sciences 324 (1223),
[32] Kleerekoper, L., van Esch, M., Salcedo, T. B., 2012. How to
make a city climate-proof, addressing the urban heat island
effect. Resources, Conservation and Recycling 64 (0), 30-38.
259 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
[33] Coutts, A., Beringer, J., Tapper, N., 2010. Changing Urban
Climate and CO
Emissions: Implications for the Development
of Policies for Sustainable Cities. Urban Policy and Research 28
(1), 27-47.
[34] USEPA, 2007. Reducing stormwater costs through low impact
development (LID) strategies and practices. Available from
[35] Brattebo, B. O., Booth, D. B., 2003. Long-term stormwater
quantity and quality performance of permeable pavement
systems. Water Research 37 (18), 4369-4376.
[36] Hoyano, A., Asano, K., Kanamaru, T., 1999. Analysis of the
sensible heat flux from the exterior surface of buildings using
time sequential thermography. Atmospheric Environment 33
(24–25), 3941-3951.
[37] Chapman, S., Thatcher, M., Salazar, A., Watson, J. E.,
McAlpine, C. A., 2018. The effect of urban density and
vegetation cover on the heat island of a subtropical city. Journal
of Applied Meteorology and Climatology, 57 (11), pp.
[38] Tong, S., Wong, N. H., Tan, C. L., Jusuf, S. K., Ignatius, M., Tan,
E., 2017. Impact of urban morphology on microclimate and
thermal comfort in northern China. Solar Energy, 155, pp.
[39] Tong, S., Wong, N. H., Jusuf, S. K., Tan, C. L., Wong, H. F.,
Ignatius, M., Tan, E., 2018. Study on correlation between air
temperature and urban morphology parameters in built
environment in northern China. Building and Environment, 127,
pp. 239-249.
[40] Hall, D. J., Walker, S., Spanton, A. M., 1999. Dispersion from
courtyards and other enclosed spaces. Atmospheric
Environment 33 (8), 1187-1203.
[41] Rosenzweig, C., Solecki, W., Slosberg, R., 2006. Mitigating
New York City’s heat island effect with urban forestry, living
roofs and light surfaces. Preprints, Sixth Symp. on the
UrbanEnvironment, Atlanta, GA, Amer. Meteor. Soc., J3.2.
[42] Grimmond, C. S. B., Potter, S. K., Zutter, H. N., Souch, C.,
2001. Rapid methods to estimate sky-view factors applied to
urban areas. International Journal of Climatology 21 (7),
[43] Konarska, J., Holmer, B., Lindberg, F., Thorsson, S., 2016.
Influence of vegetation and building geometry on the spatial
variations of air temperature and cooling rates in a high-latitude
city. International Journal of Climatology, 36 (5), pp.
[44] Offerle, B., Eliasson, I., Grimmond, C. S. B., Holmer, B., 2007.
Surface heating in relation to air temperature, wind and
turbulence in an urban street canyon. Boundary-Layer Meteorol
122 (2), 273-292.
[45] Chapman, S., Watson, J. E., Salazar, A., Thatcher, M.,
McAlpine, C. A., 2017. The impact of urbanization and climate
change on urban temperatures: a systematic review. Landscape
Ecology, 32 (10), pp. 1921-1935.
[46] Mohajerani, A., Bakaric, J., Jeffrey-Bailey, T., 2017. The urban
heat island effect, its causes, and mitigation, with reference to
the thermal properties of asphalt concrete. Journal of
environmental management, 197, pp. 522-538.
[47] USEPA, 2008c. Reducing urban heat islands: compendium of
strategies - cool roofs. Available from
[48] Christen, A., Vogt, R., 2004. Energy and radiation balance of a
central European city. International Journal of Climatology 24
(11), 1395-1421.
[49] Voogt, J., 2002. Urban Heat Island. In Munn, T. (ed.)
Encyclopedia of Global Environmental Change, Vol. 3.
Chichester: John Wiley and Sons.
[50] Oke, T. R., 1988. Street design and urban canopy layer climate.
Energy and Buildings 11 (1–3), 103-113.
[51] Bourque, A., Simonet, G., 2007. Québec, Chap. 5. Dans: Vivre
avec les changements climatiques au Canada, Lemmen, D. S.,
Warren, F. J., Lacroix, J., Bush, E., Gouvernement du Canada,
Ottawa. pp. 171-226.
[52] USEPA, 2008a. Reducing urban heat islands: compendium of
strategies - urban heat island basics. Available from
[53] Morris, C. J. G., Simmonds, I., Plummer, N., 2001.
Quantification of the Influences of Wind and Cloud on the
Nocturnal Urban Heat Island of a Large City. Journal of Applied
Meteorology 40 (2), 169-18.
[54] Oke, T. R., 1982. The energetic basis of the urban heat island.
Quarterly Journal of the Royal Meteorological Society 108
(455), 1-24.
[55] Szegedi, S., Kircsi, A., 2003. Effects of synoptic conditions on
the development of the urban heat island in Debrecen, Hungary.
Acta Climatologica et chorologica 36-37, 111-120.
[56] Beranová, R., Huth, R., 2005. Long-term changes in the heat
island of Prague under different synoptic conditions. Theor.
Appl. Climatol. 82 (1-2), 113-118.
[57] Cheung, P. K., Jim, C. Y., 2019. Effects of urban and landscape
elements on air temperature in a high-density subtropical city.
Building and Environment, 164, p. 106362.
[58] Nunez, M., Oke, T. R., 1977. The Energy Balance of an Urban
Canyon. Journal of Applied Meteorology 16 (1), 11-19.
[59] Kusaka, H., Kondo, H., Kikegawa, Y., Kimura, F., 2001. A
Simple Single-Layer Urban Canopy Model For Atmospheric
Models: Comparison With Multi-Layer And Slab Models.
Boundary-Layer Meteorol 101 (3), 329-358.
[60] Mirzaei, P. A., Haghighat, F., 2010. Approaches to study Urban
Heat Island Abilities and limitations. Building and
Environment 45 (10), 2192-2201.
[61] Pielke, R. A., 1984. Mesoscale meteorological modeling.
Academic Press, 612. New York.
[62] Fan, H., Sailor, D. J., 2005. Modeling the impacts of
anthropogenic heating on the urban climate of Philadelphia: a
comparison of implementations in two PBL schemes.
Atmospheric Environment 39 (1), 73-84.
[63] Imran, H. M., Kala, J., Ng, A. W. M., Muthukumaran, S., 2018b.
An evaluation of the performance of a WRF multi-physics
ensemble for heatwave events over the city of Melbourne in
southeast Australia. Climate Dynamics 50 (7), 2553-2586.
Earth Sciences 2021; 10(6): 244-264 260
[64] Chen, F., Dudhia, J., 2001. Coupling an Advanced Land
Surface–Hydrology Model with the Penn State–NCAR MM5
Modeling System. Part II: Preliminary Model Validation.
Monthly Weather Review 129 (4), 587-604.
[65] Xiu, A., Pleim, J. E., 2001. Development of a Land Surface
Model. Part I: Application in a Mesoscale Meteorological
Model. Journal of Applied Meteorology 40 (2), 192-209.
[66] Saitoh, T. S., Shimada, T., Hoshi, H., 1996. Modeling and
simulation of the Tokyo urban heat island. Atmospheric
Environment 30 (20), 3431-3442.
[67] Yamada, T., Bunker, S., 1987. Development of nested grid,
second moment turbulence closure model and application to the
1982 ASCOT brush creek data simulation. Journal of Applied
Meteorology 27, 562-578.
[68] Bornstein, R. D., 1975. The two-dimensional URBMET urban
boundary layer model. Journal of Applied Meteorology 14 (8),
[69] Pielke, R. A., 1974. A three-dimensional numerical model of the
sea breezes over south Florida. Monthly weather review 102 (2),
[70] Kondo, H., Genchi, Y., Kikegawa, Y., Ohashi, Y., Yoshikado, H.,
Komiyama, H., 2005. Development of a multi-layer urban
canopy model for the analysis of energy consumption in a big
city: Structure of the urban canopy model and its basic
performance. Boundary-Layer Meteorol 116 (3), 395-421.
[71] Wippermann, F. K., Gross, G., 1986. The wind-induced shaping
and migration of an isolated dune: A numerical experiment.
Boundary-Layer Meteorol 36 (4), 319-334.
[72] Laprise, R., Caya, D., Bergeron, G., Giguère, M., 1997. The
formulation of the André Robert MC2 (mesoscale compressible
community) model. Atmosphere-Ocean 35 (sup1), 195-220.
[73] Masson, V., 2000. A physically-based scheme for the urban
energy budget in atmospheric models. Boundary-Layer
Meteorol 94 (3), 357-397.
[74] Kanda, M., Kawai, T., Kanega, M., Moriwaki, R., Narita, K.,
Hagishima, A., 2005. A simple energy balance model for regular
building arrays. Boundary-Layer Meteorol 116 (3), 423-443.
[75] Dudhia, J., Bresch, J. F., 2002. A global version of the PSU–
NCAR Mesoscale Model. Monthly Weather Review 130 (12),
[76] Pielke, R. A., Cotton, W., Walko, R. e. a., Tremback, C., Lyons,
W. A., Grasso, L., Nicholls, M., Moran, M., Wesley, D., Lee, T.,
1992. A comprehensive meteorological modeling
system—RAMS. Meteorology and atmospheric Physics 49
(1-4), 69-91.
[77] Chen F., Kusaka H., Bornstein R., Ching J., Grimmond C. S. B.,
Grossman-Clarke S., Loridan T., Manning K. W., Martilli A.,
Miao S., Sailor D., Salamanca F. P., Taha H., Tewari M., Wang
X., Wyszogrodzki A. A., Zhang C. (2011). The integrated
WRF/urban modeling system: Development, evaluation, and
applications to urban environmental problems. International
Journal of Climatology, 31 (2), 273–288.
[78] Salamanca, F., Martilli, A., 2010. A new building energy model
coupled with an urban canopy parameterization for urban
climate simulations—Part II. Validation with one dimension
off-line simulations. Theoretical and Applied Climatology, 99
(3), pp. 345-356.
[79] Schubert, S., Grossman-Clarke, S., Martilli, A., 2012. A
double-canyon radiation scheme for multi-layer urban canopy
models. Boundary-layer meteorology, 145 (3), pp. 439-468.
[80] Wouters, H., Blahak, U., Helmert, J., Raschendorfer, M.,
Demuzere, M., Fay, B., Trusilova, K., Mironov, D., Reinert, D.,
Lüthi, D., Machulskaya, E., 2015. Model developments in
TERRA_URB, the upcoming standard urban parametrization of
the atmospheric numerical model COSMO (-CLM). In EGU
General Assembly Conference Abstracts (p. 8990).
[81] Wouters, H., Demuzere, M., Blahak, U., Fortuniak, K., Maiheu,
B., Camps, J., Tielemans, D., van Lipzig, N. P., 2016. The
efficient urban canopy dependency parametrization (SURY) v1.
0 for atmospheric modelling: description and application with
the COSMO-CLM model for a Belgian summer. Geoscientific
Model Development, 9 (9), pp. 3027-3054.
[82] Mussetti, G., Brunner, D., Henne, S., Allegrini, J., Krayenhoff,
E. S., Schubert, S., Feigenwinter, C., Vogt, R., Wicki, A.,
Carmeliet, J., 2020. COSMO-BEP-Tree v1. 0: a coupled urban
climate model with explicit representation of street trees.
Geoscientific Model Development, 13 (3), pp. 1685-1710.
[83] Mochida, A., Yoshino, H., Miyauchi, S., Mitamura, T., 2006.
Total analysis of cooling effects of cross-ventilation affected
by microclimate around a building. Solar Energy 80 (4),
[84] Mochida, A., Lun, I. Y. F., 2008. Prediction of wind
environment and thermal comfort at pedestrian level in urban
area. Journal of Wind Engineering and Industrial Aerodynamics
96 (10–11), 1498-1527.
[85] Yamada, T., Koike, K., 2011. Downscaling mesoscale
meteorological models for computational wind engineering
applications. Journal of Wind Engineering and Industrial
Aerodynamics 99 (4), 199-216.
[86] Tominaga, Y., Mochida, A., Yoshie, R., Kataoka, H., Nozu, T.,
Yoshikawa, M., Shirasawa, T., 2008. AIJ guidelines for practical
applications of CFD to pedestrian wind environment around
buildings. Journal of Wind Engineering and Industrial
Aerodynamics 96 (10–11), 1749-1761.
[87] Ferrero, E., Castelli, S. T., Anfossi, D., 2003. Turbulence fields
for atmospheric dispersion models in horizontally
non-homogeneous conditions. Atmospheric Environment 37
(17), 2305-2315.
[88] Vu, T. C., Ashie, Y., Asaeda, T., 2002. Turbulence closure model
for the atmospheric boundary layer including urban canopy.
Boundary-Layer Meteorol 102, 459-490.
[89] Ashie, Y., Ca, V. T., Asaeda, T., 1999. Building canopy model
for the analysis of urban climate. Journal of wind engineering
and industrial aerodynamics 81 (1-3), 237-248.
[90] Tanimoto, J., Hagishima, A., Chimklai, P., 2004. An approach
for coupled simulation of building thermal effects and urban
climatology. Energy and Buildings 36 (8), 781-793.
[91] Bruse, M., Fleer, H., 1998. Simulating surface–plant–air
interactions inside urban environments with a three dimensional
numerical model. Environmental modelling & software 13 (3-4),
[92] Akbari, H., Pomerantz, M., Taha, H., 2001. Cool surfaces and
shade trees to reduce energy use and improve air quality in
urban areas. Solar Energy 70 (3), 295-310.
261 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
[93] Shashua-Bar, L., Hoffman, M. E., 2000. Vegetation as a climatic
component in the design of an urban street: An empirical model
for predicting the cooling effect of urban green areas with trees.
Energy and Buildings 31 (3), 221-235.
[94] Eliasson, I., 1996. Urban nocturnal temperatures, street geometry
and land use. Atmospheric Environment 30 (3), 379-392.
[95] Ca, V. T., Asaeda, T., Abu, E. M., 1998. Reductions in air
conditioning energy caused by a nearby park. Energy and
Buildings 29 (1), 83-92.
[96] Cao, X., Onishi, A., Chen, J., Imura, H., 2010. Quantifying the
cool island intensity of urban parks using ASTER and IKONOS
data. Landscape and Urban Planning 96 (4), 224-231.
[97] Hoyano, A., 1988. Climatological uses of plants for solar
control and the effects on the thermal environment of a building.
Energy and Buildings 11 (1–3), 181-199.
[98] Parker, J. H., 1983. Effectiveness of vegetation on residential
cooling. Passive solar journal 2 (2), 123-132.
[99] Imran, H. M., Kala, J., Ng, A. W. M., Muthukumaran, S., 2019b.
Effectiveness of vegetated patches as Green Infrastructure in
mitigating Urban Heat Island effects during a heatwave event in
the city of Melbourne. Weather and Climate Extremes 25,
[100] Yan, H., Wu, F., Dong, L. (2018). Influence of a large urban park
on the local urban thermal environment. The Science of the
Total Environment, 622 (623), 882–891.
[101] Cheung, P. K., Jim, C. Y., Siu, C. T., 2021. Effects of urban park
design features on summer air temperature and humidity in
compact-city milieu. Applied Geography, p. 102439.
[102] Jacobs, S., Gallant, A., Tapper, N., Li, D., 2018. Use of Cool
Roofs and Vegetation to Mitigate Urban Heat and Improve
Human Thermal Stress in Melbourne, Australia. Journal of
Applied Meteorology and Climatology, 57 (8), 1747-1764.
[103] Bouyer, J., Inard, C., Musy, M., 2011. Microclimatic coupling as
a solution to improve building energy simulation in an urban
context. Energy and Buildings 43 (7), 1549-1559.
[104] Buyantuyev, A., Wu, J., 2012. Urbanization diversifies land
surface phenology in arid environments: Interactions among
vegetation, climatic variation, and land use pattern in the
Phoenix metropolitan region, USA. Landscape and Urban
Planning 105 (1–2), 149-159.
[105] Akbari, H., Kurn, D. M., Bretz, S. E., Hanford, J. W., 1997.
Peak power and cooling energy savings of shade trees. Energy
and Buildings 25 (2), 139-148.
[106] McPherson, E. G., Herrington, L. P., Heisler, G. M., 1988.
Impacts of vegetation on residential heating and cooling. Energy
and Buildings 12 (1), 41-51.
[107] Akbari, H., 2005b. Potentials of urban heat island mitigation. In
Proceedings of the International Conference on Passive and
Low Energy Cooling for the Built Environment, Santorini,
Greece (pp. 19-21).
[108] Chang, C.-R., Li, M.-H., Chang, S.-D., 2007. A preliminary
study on the cool-island intensity of Taipei city parks.
Landscape and Urban Planning 80, 386-395.
[109] Dan, M. K., Sarah, E. B., Benson, H., Hashem, A., 1994. The
potential for reducing urban air temperatures and energy
consumption through vegetative cooling. No. LBL-35320.
Lawrence Berkeley Lab., CA (United States).
[110] David, J. S., Leo, I. R., Hashem, A., 1992. Measured impact of
neighborhood tree cover on microclimate, 1992 ACEEE
Summer Study on Energy Efficiency in Buildings. pp. 149-158.
[111] Gill, S., Handley, J. F., Ennos, R., Pauleit, S., 2007. Adapting
Cities for Climate Change: The Role of the Green Infrastructure.
Built Environment 33, 115-133.
[112] Jo, J. H., Golden, J. S., Shin, S. W., 2009. Incorporating built
environment factors into climate change mitigation strategies
for Seoul, South Korea: A sustainable urban systems framework.
Habitat International 33 (3), 267-275.
[113] Piringer, M., Grimmond, C. S. B., Joffre, S. M., Mestayer, P.,
Middleton, D. R., Rotach, M. W., Baklanov, A., De Ridder, K.,
Ferreira, J., Guilloteau, E., Karppinen, A., Martilli, A., Masson,
V., Tombrou, M., 2002. Investigating the Surface Energy
Balance in Urban Areas – Recent Advances and Future Needs.
Water, Air and Soil Pollution: Focus 2 (5), 1-16.
[114] Taha, H., Douglas, S., Haney, J., 1997. Mesoscale
meteorological and air quality impacts of increased urban
albedo and vegetation. Energy and Buildings 25 (2), 169-177.
[115] Takebayashi, H., Moriyama, M., 2007. Surface heat budget on
green roof and high reflection roof for mitigation of urban heat
island. Building and Environment 42 (8), 2971-2979.
[116] Coutts, A. M., White, E. C., Tapper, N. J., Beringer, J., Livesley,
S. J., 2016. Temperature and human thermal comfort effects of
street trees across three contrasting street canyon environments.
Theoretical and applied climatology, 124 (1-2), pp. 55-68.
[117] Cheung, P. K., Jim, C. Y., 2018. Comparing the cooling effects
of a tree and a concrete shelter using PET and UTCI. Building
and Environment, 130, pp. 49-61.
[118] Fung, C. K., Jim, C. Y., 2019. Microclimatic resilience of
subtropical woodlands and urban-forest benefits. Urban
Forestry & Urban Greening, 42, pp. 100-112.
[119] Cheung, P. K., Fung, C. K., Jim, C. Y., 2020. Seasonal and
meteorological effects on the cooling magnitude of trees in
subtropical climate. Building and Environment, 177, p. 106911.
[120] MoL, 2008. Living Roofs and Walls, Technical Report:
Supporting London Plan Policy. Greater London Authority.
[121] USEPA, 2008b. Reducing Urban Heat Islands: Compendium of
Strategies: Green Roofs. Available from:
[122] Oberndorfer, E., Lundholm, J., Bass, B., Coffman, R. R., Doshi,
H., Dunnett, N., Gaffin, S., KÖHler, M., Liu, K. K. Y., Rowe, B.,
2007. Green Roofs as Urban Ecosystems: Ecological Structures,
Functions, and Services. BioScience 57 (10), 823-833.
[123] Barrio, E. P. D., 1998. Analysis of the green roofs cooling
potential in buildings. Energy and Buildings 27 (2), 179-193.
[124] Eumorfopoulou, E., Aravantinos, D., 1998. The contribution of
a planted roof to the thermal protection of buildings in Greece.
Energy and Buildings 27 (1), 29-36.
[125] Niachou, A., Papakonstantinou, K., Santamouris, M.,
Tsangrassoulis, A., Mihalakakou, G., 2001. Analysis of the
green roof thermal properties and investigation of its energy
performance. Energy and Buildings 33 (7), 719-729.
Earth Sciences 2021; 10(6): 244-264 262
[126] Onmura, S., Matsumoto, M., Hokoi, S., 2001. Study on
evaporative cooling effect of roof lawn gardens. Energy and
Buildings 33 (7), 653-666.
[127] Banting, D., Doshi, H., Li, J., Missios, P., Au, A., Currie, B.,
Verrati, M., 2005. Report on the Environmental Benefits and
Costs of Green Roof Technology for the City of Toronto.
Available from:
[128] Wong, N. H., Chen, Y., Ong, C. L., Sia, A., 2003. Investigation
of thermal benefits of rooftop garden in the tropical environment.
Building and Environment 38 (2), 261-270.
[129] Köhler, M., Kaiser, D., 2019. Evidence of the climate mitigation
effect of green roofs—A 20-year weather study on an extensive
green roof (EGR) in Northeast Germany. Buildings, 9 (7), p.
[130] Smith, K., Roebber, P., 2011. Green Roof Mitigation Potential
for a Proxy Future Climate Scenario in Chicago, Illinois. Journal
of applied meteorology and climatology, 50 (3), 507-522.
[131] Imran, H. M., Kala, J., Ng, A. W. M., Muthukumaran, S., 2018a.
Effectiveness of green and cool roofs in mitigating urban heat
island effects during a heatwave event in the city of Melbourne
in southeast Australia. Journal of Cleaner Production 197,
[132] Manso, M., Teotónio, I., Silva, C. M., Cruz, C. O., 2021. Green
roof and green wall benefits and costs: A review of the
quantitative evidence. Renewable and Sustainable Energy
Reviews, 135, p. 110111.
[133] Shafique, M., Kim, R., Rafiq, M., 2018. Green roof benefits,
opportunities and challenges–A review. Renewable and
Sustainable Energy Reviews, 90, pp. 757-773.
[134] Kingsbury, N., Dunnett, N., 2008. Planting green roofs and
living walls. 2nd Edn., Timber Press, 336 p.
[135] Wong, N. H., Tan, A. Y. K., Tan, P. Y., Wong, N. C., 2009.
Energy simulation of vertical greenery systems. Energy and
Buildings 41 (12), 1401-1408.
[136] Wong, N. H., Tan, A., Tan, P., Sia, A., Wong, N., 2010.
Perception Studies of Vertical Greenery Systems in Singapore.
Journal of Urban Planning and Development, 136 (4),
[137] Imran, H. M., Kala, J., Ng, A. W. M., Muthukumaran, S., 2019.
Impacts of future urban expansion on urban heat island effects
during heatwave events in the city of Melbourne in southeast
Australia. Quarterly Journal of the Royal Meteorological
Society 145 (723), 2586-2602.
[138] Gartland, L., 2008. Heat islands. London: Sterling VA,, 215 p.
[139] Berdahl, P., Bretz, S. E., 1997. Preliminary survey of the solar
reflectance of cool roofing materials. Energy and Buildings, 25
(2), 149-158.
[140] Synnefa, A., Santamouris, M., Livada, I., 2006. A Study of the
Thermal Performance of Reflective Coatings for the Urban
Environment. Solar Energy 80, 968-981.
[141] Synnefa, A., Santamouris, M., Apostolakis, K., 2007. On the
development, optical properties and thermal performance of
cool colored coatings for the urban environment. Solar Energy
81 (4), 488-497.
[142] Akbari, H., Berdahl, P., Levinson, R., Miller, S. W. W.,
Desjarlais, A., 2006. Cool color roofing materials. California
Energy Commission, Berkeley, CA., 73 p.
[143] Levinson, R., Berdahl, P., Asefaw Berhe, A., Akbari, H., 2005.
Effects of soiling and cleaning on the reflectance and solar heat
gain of a light-colored roofing membrane. Atmospheric
Environment 39 (40), 7807-7824.
[144] Watkins, R., Palmer, J., Kolokotroni, M., 2007. Increased
Temperature and Intensification of the Urban Heat Island:
Implications for Human Comfort and Urban Design. Built
Environment (1978-) 33 (1), 85-96.
[145] Akbari, H., Konopacki, S., 2004. Energy effects of heat-island
reduction strategies in Toronto, Canada. Energy 29 (2), 191-210.
[146] Berdahl, P., Akbari, H., Rose, L. S., 2002. Aging of reflective
roofs: soot deposition. Applied optics 41 (12), 2355-2360.
[147] Liébard, A., DeHerde, A., 2005, Treatise on architecture and
urbanism bioclimatic, Design, build and develop with
sustainable development, Paris: The Monitor.
[148] Susca, T., 2012. Enhancement of life cycle assessment (LCA)
methodology to include the effect of surface albedo on climate
change: Comparing black and white roofs. Environmental
Pollution 163 (0), 48-54.
[149] Taha, H., 2008. Urban Surface Modification as a Potential
Ozone Air-quality Improvement Strategy in California: A
Mesoscale Modelling Study. Boundary-Layer Meteorol 127 (2),
[150] Synnefa, A., Karlessi, T., Gaitani, N., Santamouris, M.,
Assimakopoulos, D. N., Papakatsikas, C., 2011. Experimental
testing of cool colored thin layer asphalt and estimation of its
potential to improve the urban microclimate. Building and
Environment 46 (1), 38-44.
[151] Grimmond, S. U. E., 2007. Urbanization and global
environmental change: local effects of urban warming.
Geographical Journal 173 (1), 83-88.
[152] Akbari, H., 2005a. Energy Saving Potentials and Air Quality
Benefits of Urban Heat Island Mitigation. (Working Paper);
Lawrence Berkeley National Laboratory: Berkeley, CA, USA,
[153] Asaeda, T., Ca, V. T., Wake, A., 1996. Heat storage of pavement
and its effect on the lower atmosphere. Atmospheric
Environment 30 (3), 413-427.
[154] Doulos, L., Santamouris, M., Livada, I., 2004. Passive cooling
of outdoor urban spaces. The role of materials. Solar Energy 77
(2), 231-249.
[155] Ferguson, B., Fisher, K., Golden, J., Hair, L., Haselbach, L.,
Hitchcock, D., Kaloush, K., Pomerantz, M., Tran, N., Waye, D.,
2008. Reducing urban heat islands: compendium of
strategies-cool pavements.
[156] Pomerantz, M., 2000. The Effect of Pavements' Temperatures
On Air Temperatures in Large Cities. Lawrence Berkeley
National Laboratory Report No. LBNL-43442, Berkeley, CA.
[157] Rosenfeld, A., Romm, J., Akbari, H., Lloyd, A., 1997. Painting
the town white and green. Technology Review, 100 (2), 52-59.
[158] Rosenfeld, A. H., Akbari, H., Romm, J. J., Pomerantz, M., 1998.
Cool communities: strategies for heat island mitigation and
smog reduction. Energy and Buildings 28 (1), 51-62.
263 H. M. Imran et al.: Causes, Modeling and Mitigation of Urban Heat Island: A Review
[159] Li, H., Harvey, J., Jones, D., 2013a. Cooling effect of permeable
asphalt pavement under both dry and wet conditions. Transport
Res Record: J Transport Res Board 3 (2372), 97-107.
[160] Li, H., Harvey, J., Kendall, A., 2013b. Field measurement of
albedo for different land cover materials and effects on thermal
performance. Building and Environment 59 (0), 536-546.
[161] Li, H., Harvey, J. T., Holland, T. J., Kayhanian, M., 2013c. The
use of reflective and permeable pavements as a potential
practice for heat island mitigation and stormwater management.
Environ Res Lett 8 (1), 14pp.
[162] Chan, H. Y., Riffat, S. B. and Zhu, J. 2010. Review of passive
solar heating and cooling technologies. Renewable and
Sustainable Energy Reviews, 14 (2), 781-789.
[163] Oliva, J. P., Courgey, S., 2006. Bioclimatic design: efficient homes
and comfortable in new and rehabilitation. Living Earth, 240p.
[164] Imran, H. M., Kala, J., Ng, A., Muthukumaran, S., 2019a.
Effectiveness of vegetated patches as Green Infrastructure in
mitigating Urban Heat Island effects during a heatwave event in
the city of Melbourne. Weather and Climate Extremes 25,
[165] Taha, H., Konopacki, S., Gabersek, S., 1999. Impacts of
large-scale surface modifications on meteorological conditions
and energy use: a 10-region modeling study. Theor. Appl.
Climatol. 62 (3-4).
[166] Kikegawa, Y., Genchi, Y., Kondo, H., Hanaki, K., 2006. Impacts
of city-block-scale countermeasures against urban heat-island
phenomena upon a building’s energy-consumption for
air-conditioning. Applied Energy 83 (6), 649-668.
[167] Žuvela-Aloise, M., Koch, R., Buchholz, S., Früh, B., 2016.
Modelling the potential of green and blue infrastructure to
reduce urban heat load in the city of Vienna. Climatic Change
135 (3), 425-438.
[168] Morris, K. I., Chan, A., Ooi, M. C., Oozeer, M. Y., Abakr, Y. A.,
Morris, K. J. K., 2016. Effect of vegetation and waterbody on
the garden city concept: An evaluation study using a newly
developed city, Putrajaya, Malaysia. Computers, Environment
and Urban Systems 58, 39-51.
[169] Kolokotroni, M., Giannitsaris, I., Watkins, R., 2006. The effect
of the London urban heat island on building summer cooling
demand and night ventilation strategies. Solar Energy 80 (4),
[170] Genchi, Y., Ishisaki, M., Ohashi, Y., Takahashi, H., Inaba, A.,
2003. Impacts of large-scale photovoltaic panel installation on
the heat island effect in Tokyo. Fifth Conference on the Urban
[171] Akbari, H., 2003. Measured energy savings from the application
of reflective roofs in 2 small non-residential buildings. Energy,
28 (9), 953-967.
[172] Saiz, S., Kennedy, C., Bass, B., Pressnail, K., 2006.
Comparative life cycle assessment of standard and green roofs.
Environmental science & technology 40 (13), 4312-4316.
[173] Andric, I., Kamal, A., Al-Ghamdi, S. G., 2020. Efficiency of
green roofs and green walls as climate change mitigation
measures in extremely hot and dry climate: Case study of Qatar.
Energy Reports, 6, pp. 2476-2489.
[174] Ihara, T., Kikegawa, Y., Asahi, K., Genchi, Y., Kondo, H., 2008.
Changes in year-round air temperature and annual energy
consumption in office building areas by urban heat-island
countermeasures and energy-saving measures. Applied Energy
85 (1), 12-25.
[175] Golden, J. S., Carlson, J., Kaloush, K. E., Phelan, P., 2007. A
comparative study of the thermal and radiative impacts of
photovoltaic canopies on pavement surface temperatures. Solar
Energy 81 (7), 872-883.
[176] Ferrante, A., Cascella, M. T., 2011. Zero energy balance and zero
on-site CO
emission housing development in the Mediterranean
climate. Energy and Buildings 43 (8), 2002-2010.
[177] Tsangrassoulis, A., Santamouris, M., Geros, V., Wilson, M.,
Asimakopoulos, D., 1999. A method to investigate the potential
of south-oriented vertical surfaces for reflecting daylight onto
oppositely facing vertical surfaces under sunny conditions.
Solar Energy 66 (6), 439-446.
[178] Nabil, A., Mardaljevic, J., 2006. Useful daylight illuminances:
A replacement for daylight factors. Energy and Buildings 38 (7),
[179] Mesa, N. A., Corica, L., Pattini, A., 2011. Evaluation of the
potential of natural light to illuminate buildings in dense urban
environment. A study in Mendoza, Argentina. Renewable
Energy 36 (9), 2414-2423.
[180] Leveratto, M. J., 2002. Urban planning instruments to improve
winter solar access in open public spaces. Environmental
Management and Health 13 (4), 366-372.
[181] Compagnon, R., 2004. Solar and daylight availability in the
urban fabric. Energy and Buildings 36 (4), 321-328.
[182] Cheng, V., Steemers, K., Montavon, M., Compagnon, R., 2006.
Urbanform, densityand solar potential. PLEA 2006—The 23rd
Conference on passive and low energy architecture, Geneva,
Switzerland, 6–8 September.
[183] Kristl, Ž., Krainer, A., 2001. Energy evaluation of urban
structure and dimensioning of building site using iso-shadow
method. Solar Energy 70 (1), 23-34.
[184] Georgakis, C., Santamouris, M., 2006. Experimental
investigation of air flow and temperature distribution in deep
urban canyons for natural ventilation purposes. Energy and
Buildings 38 (4), 367-376.
[185] Geros, V., Santamouris, M., Karatasou, S., Tsangrassoulis, A.,
Papanikolaou, N., 2005. On the cooling potential of night
ventilation techniques in the urban environment. Energy and
Buildings 37 (3), 243-257.
[186] Ali-Toudert, F., Mayer, H., 2006. Numerical study on the effects
of aspect ratio and orientation of an urban street canyon on
outdoor thermal comfort in hot and dry climate. Building and
Environment 41 (2), 94-108.
[187] Nakamura, Y., Oke, T. R., 1988. Wind, temperature and stability
conditions in an east-west oriented urban canyon. Atmospheric
Environment (1967) 22 (12), 2691-2700.
[188] Déoux, S., Déoux, P., 2004. Guide to Habitat Sain. Medieco
Editions, 537 p.
[189] Giguere, M., 2009. Urban Heat Island Mitigation Strategies.
Literature Review of The Institute national de sante´ publique,
Canada, Pp 1-72.
Earth Sciences 2021; 10(6): 244-264 264
[190] Manning, M. M., Elmahdy, A. H., Swinton, M. C., Parekh, A.,
2008. Selecting low-E glazing for optimum energy performance.
Home Builder 21 (2), 1-4.
[191] Imran, H. M., Akib, S., Karim, M. R., 2013. Permeable
pavement and stormwater management systems: a review.
Environmental Technology 34 (18), 2649-2656.
[192] Vivre en ville, 2004. Towards sustainable communities. Vivre
en ville Editions, Quebec, 637 p.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
The main goal of this study is to assess the potential of green roofs and walls as a mitigation measure for the climate-change-driven growth of building energy consumption in extremely hot climates. A comprehensive, interdisciplinary methodology was developed that bridged climate change and building modeling. The residential building stock of Qatar was considered, with a two-story residential villa selected as a representative of the stock and consequently a case study. Weather scenarios were created for the years 2020, 2050, and 2080, and four building renovation scenarios were developed. The findings suggested that without any mitigation measures, residential building energy consumption in Qatar could increase by up to 9%, 17%, and 30% in 2020, 2050, and 2080, respectively. The addition of 5-cm expanded polystyrene and the installation of energy-efficient windows proved to be far more efficient than the addition of green walls and roofs under the climate conditions (30% reduction in energy consumption vs. 3%). Additionally, the environmental impact of green wall and roof maintenance, specific to Qatar, should be considered. However, in the final judgment, other positive effects of a green infrastructure (such as the effect on air quality, heat island effect, and health of the inhabitants) should be considered.
Full-text available
Street trees are more and more regarded as an effective measure to reduce excessive heat in urban areas. However, the vast majority of mesoscale urban climate models do not represent street trees in an explicit manner and, for example, do not take the important effect of shading by trees into account. In addition, urban canopy models that take interactions of trees and urban fabrics directly into account are usually limited to the street or neighbourhood scale and hence cannot be used to analyse the citywide effect of urban greening. In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the Building Effect Parameterisation with Trees (BEP-Tree) vegetated urban canopy model and the Consortium for Small-scale Modeling (COSMO) mesoscale weather and climate model. The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June–July 2015. Overall, the model compared well with measurements of individual components of the surface energy balance and with air and surface temperatures obtained from a flux tower, surface stations and satellites. Deficiencies were identified for nighttime air temperature and humidity, which can mainly be traced back to limitations in the simulation of the nighttime stable boundary layer in COSMO. The representation of street trees in the coupled model generally improved the agreement with observations. Street trees produced large changes in simulated sensible and latent heat flux, and wind speed. Within the canopy layer, the presence of street trees resulted in a slight reduction in daytime air temperature and a very minor increase in nighttime air temperature. The model was found to realistically respond to changes in the parameters defining the street trees: leaf area density and stomatal conductance. Overall, COSMO-BEP-Tree demonstrated the potential of (a) enabling city-wide studies on the cooling potential of street trees and (b) further enhancing the modelling capabilities and performance in urban climate modelling studies.
Full-text available
Approximately 10 km2 of new green roofs are built in Germany every year. About 85% of these are Extensive Green Roofs (EGR). An EGR with several research features was installed on new buildings belonging to the University of Applied Sciences Neubrandenburg in 1999. The results of the almost 20-year permanent survey of the climate effects of the green roof in contrast to gravel roofs are presented here. High-quality sensors, similar to those used by official weather stations, are in use, and data is collected every 10 s and aggregated to hourly values which enable comparisons to official measurements made by the DWD in Neubrandenburg and Berlin. The results show the typical urban heat island effect (UHI) and the mitigation effect of EGR. Whilst the temperature increased over the years due to the urban heat island effect, the temperature within the growing media in the green roof remained constant. The EGR has a stabilization effect of 1.5 K. This is good news for all those seeking a UHI mitigation solution for city centers. In a best-case scenario, the green roof potential of cities in Germany is between 3 and 8%. A value of 50% can be achieved for all buildings; roofs represent about ¼ of urban surfaces, and the cooling effect of 1.5 K in 20 years is a reasonable contribution to cooling cities and achieving environmental goals by greening urban surfaces.
Full-text available
The city of Melbourne in southeast Australia experiences frequent heatwaves and their frequency, intensity and duration are expected to increase in the future. In addition, Melbourne is the fastest growing city in Australia and experiencing rapid urban expansion. Heatwaves and urbanization contribute in intensifying the Urban Heat Island (UHI) effect, i.e., higher temperatures in urban areas as compared to surrounding rural areas. The combined effects of UHI and heatwaves have substantial impacts on the urban environment, meteorology and human health, and there is, therefore, a pressing need to investigate the effectiveness of different mitigation options. This study evaluates the effectiveness of urban vegetation patches such as mixed forest (MF), combination of mixed forest and grasslands (MFAG), and combination of mixed shrublands and grasslands (MSAG) in reducing UHI effects in the city of Melbourne during one of the most severe heatwave events. Simulations are carried out by using the Weather Research and Forecasting (WRF) model coupled with the Single Layer Urban Canopy Model (SLUCM). The fractions of vegetated patches per grid cell are increased by 20%, 30%, 40% and 50% using the mosaic method of the WRF model. Results show that by increasing fractions from 20 to 50%, MF reduces near surface (2 m) UHI (UHI2) by 0.6–3.4 °C, MSAG by 0.4–3.0 °C, and MFAG by 0.6–3.7 °C during the night, but there was no cooling effect for near surface temperature during the hottest part of the day. The night-time cooling was driven by a reduction in storage heat. Vegetated patches partitioned more net radiation into latent heat flux via evapotranspiration, with little to no change in sensible heat flux, but rather, a reduction in the storage heat flux during the day. Since the UHI is driven by the release of stored heat during the night, the reduced storage heat flux results in reductions in the UHI. The reductions of the UHI2 varied non-linearly with the increasing vegetated fractions, with lager fractions of up to 50% resulting in substantially larger reductions. MF and MFAG were more effective in reducing UHI2 as compared to MSAG. Vegetated patches were not effective in improving HTC during the day, but a substantial improvement of HTC was obtained between the evening and early morning particularly at 2100 local time, when the thermal stress changes from strong to moderate. Although limited to a single heatwave event and city, this study highlights the maximum potential benefits of using vegetated patches in mitigating the UHI during heatwaves and the overall principles are applicable elsewhere. Keywords: UHI mitigation, Urban vegetation, Green Infrastructure, Heatwave, WRF-SLUCM
Urban park patronage is intimately linked to the thermal comfort level perceived by visitors. This study quantified the effects of nine park-design and urban-landscape parameters on air temperature and relative humidity in summer by deploying 100 sensors in 14 urban parks in Hong Kong. The field data were compared with a reference weather station and analysed with multiple regression. The sampled parks were on average 0.2 °C warmer and 1.7% less humid than the reference. A notable temperature reduction (mean = 0.6 °C and max. = 4.9 °C) was only observed in the largest park in Hong Kong. The daily variations in cooling and humidifying magnitudes were strongly dependent on background temperature. The distance from sea, shrub cover, tree cover and sky view factor were significant parameters that controlled temperature and relative humidity. Mean temperature could be predicted to cool by 0.6 °C if the park was 1 km closer to the sea. For every 10% increase in shrub and tree covers, mean temperature would drop by 0.07 and 0.04 °C respectively. Larger parks with good coverage of woody vegetation should be developed to improve urban microclimate within parks and mediate the warming effect of nearby built-up areas.
Greening the urban environment can be an important strategy to tackle the problems of urban densification and meet the United Nations Sustainable Development Goals. Green infrastructures, like green roofs and green walls, have multiple associated environmental, social and economic benefits that improve buildings performance and the urban environment. Yet, the implementation of green roofs and green walls is still limited, as these systems often have additional costs when compared to conventional solutions. Recent studies have been comparing these greening systems to other solutions, balancing the long-term benefits and costs. Also, there is significant research on green roofs and green walls benefits. Although, green roofs and green walls economic analyses don't include all benefits due to measuring difficulties. The associated uncertainty regarding the quantification of the benefit makes it difficult to compare the research outcomes. This paper aims to provide a research review of existing benefits and costs of different types of green roofs and green walls. These were divided between building scale benefits, urban scale benefits and life cycle costs, focusing on the identification of results variability and assessment of their average quantification. The analysis shows that in general, there are few data regarding intangible benefits, as the promotion of quality of life and well-being. Also, there are still few studies quantifying green walls benefits and costs. High variability in data is mostly related to the different characteristics of systems, buildings envelope, surrounding environment and local weather conditions.
The cooling effect of trees is one of the most important ecosystem services offered by natural components in cities. However, the large variations in cooling magnitudes reported in different studies call for deeper understanding of the underlying mechanisms. This study investigated the seasonal and meteorological effects on the cooling magnitude of trees in the humid subtropical climate. The meteorological conditions at four peri-urban woodland sites and a rooftop control site were continuously monitored for one year. The annual mean (±SD) cooling magnitudes were 0.9 ± 0.5, 2.5 ± 1.4 and 1.6 ± 0.8 °C in air temperature (Ta), physiological equivalent temperature (PET) and universal thermal climate index (UTCI) respectively with notable seasonal and diurnal variations. The daily total incoming shortwave radiation (S_in) explained 24.7, 39.2 and 35.7% of the variability in cooling magnitudes in Ta, PET and UTCI respectively. For every 1 MJ/m² increase in S_in, the cooling magnitude increased by 0.03, 0.16 and 0.08 °C in Ta, PET and UTCI respectively. The cooling magnitude measured in Ta could increase by 0.05 °C for every 1 °C rise in background Ta. The monthly mean interception of S_in was generally over 80% with an annual mean of 82.3%, which allowed the cooling benefit of trees to extend to the transitional season. Future studies are suggested to conduct a continuous measurement for at least 24 h under sunny and cloudy conditions in both hot and cold seasons with a ground-level control site to capture a more complete picture of the fluctuations in cooling magnitude.
In this study, we examined the effects of key urban (road cover, building volume ratio, and proximity to sea) and landscape (water body, tree cover, shrub cover, turf cover, park area, and sky view factor) parameters on air temperature, and the impacts of weather conditions on landscape-temperature relationship. One hundred temperature sensors were installed in fourteen urban parks in Hong Kong during summer season to collect continuous air temperature data. Linear mixed-effect models showed that the effects of weather (cloud amount, solar radiation and wind speed) on landscape-temperature relationships were minor (<0.2 °C). Therefore, the landscape effects were further investigated using the entire dataset regardless of weather conditions. In a circular buffer zone with a 20-m radius, a 10% increase in road density caused a 0.059 °C rise in daytime mean air temperature while the same increase in tree cover and shrub cover led to a 0.052 and 0.041 °C drop in temperature, respectively. A 0.849 °C rise could be expected when sky view factor increased from 0 to 1. The proximity to the sea also had a significant daytime cooling effect (0.784 °C/1000 m). The night-time landscape effects were similar to the daytime except that the strengths of the effects on air temperature were weaker. The obtained results can be used by landscape designers and urban planners for modifying the landscape to bring cooling effects and tackle heat-island and climate-change impacts.
The city of Melbourne in southeast Australia is planning to substantially expand urban areas by the year 2050 and this expansion has the potential to alter the Urban Heat Island (UHI), i.e., higher temperatures in urban areas as compared to surrounding rural areas. Moreover, Melbourne has been experiencing more frequent heatwaves for last two decades, and the intensity and duration of heatwaves is expected to increase in the future, which could exacerbate the UHI. This study evaluates the potential impacts of future urban expansion on the urban meteorology in southeast Australia during four of the most severe heatwave events during the period of 2000 to 2009. Urban expansion is implemented as high density urban with a high urban fraction of 0.9 to investigate the maximum possible impacts. Simulations are carried out using the Weather Research and Forecasting model coupled with the Single Layer Urban Canopy Model with current land use and future urban expansion scenarios. Urban expansion increases the near‐surface (2 m) UHI (UHI2) by 0.75 to 2.80 °C and the skin‐surface UHI (UHIsk) by 1.9 to 5.4 °C over the expanded urban areas during the night, with no changes in existing urban areas. No substantial changes in the UHI2 and UHIsk occur during the day over both existing and expanded urban areas. This is largely driven by changes in the storage heat flux, with an increase in storage heat at night, and a decrease during the day, i.e., excess storage heat accumulated during the day is released at night, which causes slower decrease of near surface temperature and increase in the UHI. Urban expansion did not affect human thermal comfort (HTC) in existing urban areas and there were no marked differences in HTC between existing and expanded urban areas. This article is protected by copyright. All rights reserved.