buildings
Article
Outdoor Thermal Comfort: Coupling Microclimatic
Parameters with Subjective Thermal Assessment to
Design Urban Performative Spaces
Mohamed H. Elnabawi 1,2, *
1
2
3
*
and Neveen Hamza 3
College of Engineering, Applied Science University (ASU), Bahrain, In Partnership with London South Bank
University, London SE1 0AA, UK
Faculty of Engineering, October University for Modern Sciences and Arts (MSA), Cairo 12566, Egypt
School of Architecture, Planning and Landscape, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;
neveen.hamza@ncl.ac.uk
Correspondence: melnabawi@aucegypt.edu
Received: 6 November 2020; Accepted: 7 December 2020; Published: 11 December 2020
Abstract: Thermal comfort plays a main role in encouraging people to use outdoor spaces, specifically
in hot arid and humid climates. The reconciliation of climatic aspects during the urban design
phase is limited in implementation, due to the need for multidisciplinary collaboration between
desperate scientific fields of climatology, urban planning, and urban environmental modelling.
This paper aims to create an integrated interface between the microclimate, outdoor thermal comfort,
and design guidelines. The investigation combines subjective and objective approaches, including
on-site field measurements, a structured questionnaire using the seven-point American Society
of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE 55) thermal sensation votes,
and a correlation study of these votes and the microclimatic parameters. Pedestrian thermal comfort
was then examined under six shading scenarios, addressing the form and opening of shading devices
using computational fluid dynamics. Modelling is based on four dependent variables: wind velocity,
ventilation flow rate, air temperature, and the physiological equivalent temperature (PET) index.
Findings indicate that the form and location of apertures of the shading devices were the dominant
factors in achieving thermal comfort on the urban scale, and led to a reduction in air temperature
and a physiological equivalent temperature of 2.3–2.4 ◦ C. Subjective votes indicate that people who
live in hot arid climates have a wider range of adaptation and tolerance to local climatic conditions
Accordingly, a psychometric chart, for the case study outdoor thermal comfort was developed.
Keywords: outdoor thermal comfort; physiological equivalent temperature (PET); thermal sensation
votes (TSV); computational fluid dynamics (CFD)
1. Introduction
The 2020 Global Risks Perception survey issued by the World Economic Forum classified climate
change and associated environmental matters as being in the top five global risks most likely to occur in
the next decade. This is in addition to a report by the Intergovernmental Panel on Climate Change [1]
and supported by public concerns, suggesting that global warming should be held at 1.5 ◦ C to avoid
significant damage to human wellbeing and the ecosystem. Climate change predictions suggest a high
probability of a further rise in global temperatures by 1.5 ◦ C between 2030 and 2052, if the present rate
of increase remains unmanaged. In regions with hot arid climates, increases in air temperature may
lead to increases in endangering life and mortality rates. Furthermore, human performance of mental
and physical tasks diminishes at uncomfortably high temperatures, while illness and death caused by
air pollution are likely to increase during extreme hot weather [2–4].
Buildings 2020, 10, 238; doi:10.3390/buildings10120238
www.mdpi.com/journal/buildings
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Even without the added thermal stress due to global warming, cities are at a higher risk than
rural areas during hot weather and clear sky conditions, experiencing the phenomenon of elevated air
temperatures than their surrounding urban environments—known as the urban heat island (UHI), [5–7].
UHI is attributed to rapid urbanisation and accelerated changes to outdoor surfaces [8,9]. Several
studies have reported that by adapting certain urban configurations within the urban canopy layer
(found in the air layer between the ground and the rooftops), improvements can be made to the local
microclimate. This can enhance urban thermal comfort for inhabitants, particularly during the summer,
such as by reducing heat-related illness [10–13]. Usually, urban thermal comfort design strategies
tackle day-time conditions, when severe heat is most likely to occur during longer summer daylight
hours [10]. An initial action to alleviate uncomfortable hot outdoor conditions in summer is to decrease
or intercept the amount of radiation received on surfaces and people.
Since the ability to control the air temperature in urban spaces is limited, it is fairly simple
to control exposure to direct sunlight by providing shade to improve urban occupants’ thermal
comfort [14,15]. Additionally, from a behavioural adjustment stance, seeking shade is considered
the predominant adaptive behaviour taken by outdoor users on hot summer days [16]. However,
the outdoor environment is heterogeneous and includes various types of urban forms, surface materials,
and landscapes. These parameters combine to create a local microclimate by altering direct solar
radiation and reflection patterns, wind speed, and direction.
Moreover, for a full assessment, subjective parameters, such as thermal perceptions and preferences,
must also be examined and combined, since residents of hot countries tolerate higher temperatures
than those in temperate ones [17–19]. Accordingly, thermal comfort indices or scales established in
temperate areas are inapplicable elsewhere, providing the opportunity to use local subjective comfort
data developed for the examined sites, and produce a better assessment of outdoor thermal comfort as
related to locals using the space regularly [16,19–23].
However, the integration of subjective and objective parameters within the planning and design
process is missing due to the poor interdisciplinary collaboration between climatologists, urban planners,
and urban simulation experts [24–28]. Therefore, this study aims to facilitate such integration within
the urban planning process into one framework, including microclimatic data and subjective thermal
sensations in hot arid climates. The main objectives of this study are:
1.
2.
3.
To evaluate pedestrians’ thermal comfort perceptions and preferences in outdoor urban spaces in
a hot arid climate;
To evaluate the cooling effect of different shading scenarios on air temperature, wind velocity,
and subsequent improvement to outdoor thermal comfort;
To predict the performance of various shading scenarios on urban thermal comfort by triangulating
the measured field data with the subjective outcomes from the social survey and the numerical
modelling tools.
Study Context and Justifications
According to Gehl (2008), local sun and shade conditions may have a significant influence
over people’s desire to leave or stay in an outdoor space [29]. Pearlmutter et al. (2007) stated
that shade is the predominant parameter steering the heat balance equation in an arid region [30].
Therefore, shading is considered key to improving urban thermal comfort in such a climate, mostly in
summer [30–35]. Recently the efficacy of shading systems as a traditional climatic solution has been
scrutinized [36–38]. Indeed, studies have reported a negative effect of shading systems with limited
openings that restrict night purge, and surfaces take longer to cool, causing high air temperature and
an increase in UHIs [38–41]. This indicates that the higher the sky view factor, the lower the UHI,
especially at night [37]. In this respect, any shading proposals should be assessed, not only during
the day when their positive effect is well known, but also at night when the positive effect might be
reversed. Accordingly, to fulfil the paper’s objectives, the study investigated an existing historical
commercial alley in mid Cairo, Egypt. The location climate is classified as hot arid (BWh) based on the
Buildings 2020, 10, 238
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Koppen-Geiger climate categorisation [42]. Al Khayamiya (Tentmakers) Alley has been preserved and
represents a climate-conscious design acquired by trial and error of building experience (Figure 1).
Figure 1. Al Khayamiya Alley. Ancient town planners tried to avoid direct exposure to intense
solar radiation.
The topology and form of the structure was designed and oriented to provide shade by screening
solar radiation. The shading structure also has several different openings so that stratified air escapes
at night, but daylight can penetrate the space to reduce the need for artificial lighting.
2. Methods
Urban thermal comfort is an interdisciplinary study, incorporating multi-disciplinary areas,
such as meteorology, urban structure, psychology, and social behaviour. The local microclimate at
the canyon level is of the utmost importance for thermal comfort assessment, yet, any urban thermal
comfort assessment cannot only be based on objective parameters without including, subjective
responses to the surrounding environment and urban configuration of the location. Accordingly,
the proposed framework presented in Figure 2 is designed based on the interoperability of the
different levels of assessment, and contextualises both the quantitative and qualitative characteristics
of thermal sensations regarding a particular urban climate, including field measurements, structured
questionnaires, and site observation as phase one, followed by computational fluid dynamics (CFD)
parametric analysis as phase two, to overcome the lack of integrating the climatic analysis within
the urban design stage, and to create an effective decision-making framework for urban planners
and designers.
The objectives of the two phases are as follows:
1.
2.
3.
4.
Identify the major microclimate parameters for a hot arid climate;
Calculate the objective and subjective comfort index;
Calibrate the objective and subjective comfort index;
CFD parametric analysis based on objective and subjective parameters.
2.1. In Situ Field Measurements
In situ measurements were carried out for one week during the hot summer, and covered the
five main microclimate parameters responsible for outdoor thermal comfort, based on ASHRAE
Standard [43], of air temperature, wind velocity, relative humidity, and solar radiation, in addition
to globe temperature. These field measurements were taken simultaneously with the pedestrians’
subjective thermal perceptions survey, as described in the following section. Based on the preliminary
data collected from the weather files for the last 30 years, the experimental data were collected between
26 June and 2 July 2012, as representative of a week in the hot summer season [44].
Buildings 2020, 10, 238
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• Date and timing of the field
measurements
• Weather data and climate
files
• In-situ measurment
• Steady state thermal
Phase 1
Objective
assessment
Subjective
assessment
• Structured questionnaire
• Site observation
comfort index
• Actual thermal sensation
vote
• Correlation between
thermal responses votes
Objective and
subjective
assessment
• Urban modelling and
parametric analysis
Phase 2
and environmental factors
• Comparative analysis for
different design scenarios
Figure 2. Methodological framework.
2.2. Questionnaire Survey
A structured survey took place concurrently with the in situ field measurements, with the main
objective of rating the pedestrians’ thermal perception. The structured questionnaire was based on
the ASHRAE 7 point thermal sensation vote (TSV) scale (e.g., cold 3; cool 2; slightly cool 1; neutral 0;
slightly warm 1; warm 2; and hot 3), in addition to a three point scale where the interviewees could
identify their level of preference for wind and sun, such as “I want the wind/daylight to be weaker”,
“no change”, and “I want the wind/daylight to be stronger”. At the same time, observations were
made of clothing units and metabolic rate, and estimates were recorded based on ASHRAE standards
55–2009 [45]. Accordingly, one hundred and sixty people were interviewed, of which 35% were women
and 65% were men. The majority were between 25 and 34 years of age (35.8%), followed by the age
group between 35 and 44 (24.2%), then the age group between 16 and 24 (19.2%), then 45–54 years old
(11.7%), and over 55 (9.1%). Only participants who had direct and regular contact with the study area
were interviewed.
2.3. CFD Modelling and Parametric Analysis
Following the field measurements and the questionnaire, parametric modelling was conducted
using CFD, which is frequently used to assess urban microclimate based on the physiological equivalent
temperature (PET) and acceptable comfort range obtained from the previous phase. CFD can resolve
the transfer of heat and mass and their interaction with individual obstacles, such as buildings [46].
CFD can also be utilised for the analysis of the microclimate around individual buildings, which is
classified as the building scale with typical distances of less than 100 m. Therefore, the CFD Fluent code
13.0 was used to examine the impact of six different shading scenarios, as shown in Figure 3, on the
overall thermal comfort underneath, along with the existing or base case for validation purposes.
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Case 1
Case 2 (Base Case)
Case 3
Case 4
Case 5
Case 6
Figure 3. The examined case studies, including specific changes in the roof form and opening locations.
All scenarios had the same aspect ratio equal to 1.5, with the only difference between the cases
being the roof’s geometric form and the number and location of roof openings (Figure 3). The tested
parameters were wind velocity, ventilation flow rate, and mean air temperature, which were used to
analyse the impact of each case on the overall thermal comfort. The outcomes were compared with
the PET comfort index reported from the site questionnaire. Parametric environment performance
simulation analysis has proven to be a very efficient tool to assess different design proposals and their
impact on human well-being [47].
2.3.1. CFD Simulation Model, Settings, and Atmospheric Boundary Layer ABL
To perform a reliable CFD simulation, the user has to deal with several uncertainties, starting
with flow approximate equations, the level of detail in the geometric model, computational
domain size, computational grid resolution and type, boundary conditions, discretisation schemes,
initialisation data, and iterative convergence criteria. Any changes within these variables will
completely affect the outcomes [48,49]. Therefore, a well-established framework or guidelines must be
followed, the formation of which has been a major move regarding more reliable and accurate CFD
simulations. For this reason, all of the simulations were regulated in accordance with the best practice
guidelines (BPG) scenario for developing existing urban configurations [49], in which domain size,
computational grid, boundary conditions, discretisation schemes, algorithms for pressure interpolation,
and pressure-velocity coupling are all well prescribed, as stated in Table 1.
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Table 1. Requisites for a consistent computational fluid dynamics (CFD) simulation [46,49,50] as
applied on Figure 4.
Second Order Schemes or Above Should be Used
to Solve Algebraic Equations
Solution Method
In the range of 10−4 to 10−6
Multi-block structured mesh
Carrying out sensitivity analysis with three levels of
refinements where the ratio of cells for two
consecutive grids should be at least 3.4
Realisable k-Eturbulence model
Details of dimension equal to, or more than, 1 m to be
included
If H is the building height; lateral dimension = 2H +
building width
Flow direction dimension = 20H + building
dimension in flow direction
Vertical direction = 6H
While maintaining a blockage ratio below 3% (Franke
et al., 2007; Tominaga et al., 2008b)
Inflow: horizontally homogenous log law
Atmospheric Boundary Layer (ABL) velocity
profile–velocity inlet
Bottom: no-slip wall with standard wall functions
Top and side: symmetry
Outflow: pressure outlet
Residuals
Mesh
Turbulence model
Accuracy of studied buildings
Domain dimensions
Boundary conditions
Apart from these common guidelines, there is a necessity to avoid any occurrence of the
unintended streamwise gradients in the vertical profiles of the mean wind speed and turbulence
quantities (horizontal inhomogeneity) as the flow travels from the inlet of the computational domain
towards the modelled buildings. According to Blocken et al. [49], unplanned streamwise gradients
can significantly affect the reliability of the modelling outcomes. In order to create an equilibrium
atmospheric boundary layer (ABL), Richards and Hoxey [51] provided inlet profiles and wall boundary
conditions that are consistent with the standard K-epsilon (k-e) model. Therefore, the inlet boundary
condition was identified according to Equations (1)–(3), representing the velocity, turbulent kinetic
energy, and turbulent dissipation rate, respectively, as recommended by Richards and Hoxey [51]:
U=
u∗
z + z0
ln(
)
k
z0
(1)
2
u∗
K= √
Cu
(2)
3
ε=
u∗
k ( z + z0 )
(3)
(u∗ ) Friction velocity (m/s), (k) von Kármán constant (=0.40 or 0.42), (Cu ) turbulence model constant,
(z) height (m), and (z0 ) aerodynamic roughness length (m), which is 0.5 m or 1.0 m depending on the
wind direction.
While the ground boundary conditions were classified as rough and standard wall functions;
Blocken et al. [52] derived the specific relationships between the roughness height (ks ) and roughness
constant (Cs ) (Equation (4) in order to have an accurate flow near the bottom surface.
ks =
9.793 z0
Cs
(4)
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The roughness length was assigned equal 2.0, representing city centres with a mixture of low- and
high-rise building, according to Davenport roughness classification [53,54].
The settings of the applied boundary conditions were comprised of 3 m/s as inlet velocity
approaching from the north direction and 35 ◦ C as inlet air temperature, in addition to all
other requirements presented in Table 1. The simulation was− carried
out using Ansys fluent
−
code 13.0. The model employs the control volume technique and the Semi-Implicit Method for
Pressure-Linked Equations (SIMPLE) velocity–pressure coupling algorithm with the second order
upwind discretisation [49]. The turbulence kinetic energy (k) and its rate of dissipation (e) are
ɛ
determined by Equations (5) and (6).
"
#
!
µt ∂k
∂
∂
∂
(ρk) +
(ρku j ) =
µ+
+Gk + Gb − ρε − YM + SK
σk ∂x j
∂t
∂x j
∂x j
(5)
"
#
µt ∂ε
∂
∂
ε2
∂
ε
µ+
(ρε) +
(ρεu j ) =
+ρC1 Sε − ρC2
√ + C1ε C3ε Gb + Sε
σε ∂x j
k
∂t
∂x j
∂x j
k + vε
(6)
(Gk ) the generation of turbulent kinetic energy that arises due to mean velocity gradients, (Gb ) generation
of turbulent kinetic energy arising from buoyancy, (YM ) the fluctuating dilation in compressible
turbulence that contributes to the overall dissipation rate. (SE ) and (Sk ) source terms defined by the
user. (αk ) and (αE ) the turbulent Prandtl numbers for the turbulent kinetic energy and its dissipation.
Figure 4. The model was placed in a computational domain with dimensions 147 m × 85 m × 45 m as
X, Y, and Z (length, width, and height).
2.3.2. CFD Simulation Validation
Validation is essential to achieve accurate and reliable results for CFD studies on urban
microclimate [55–57], which is the process of determining the degree to which a model is an accurate
representation of the real world from the perspective of the intended uses of the model, as stated
by the American Institute of Aeronautics and Astronautics [58]. According to several CFD best
practice guidelines [56,59,60], on-site measurement validation represents the complex reality without
simplification, and is therefore true validation data for numerical models
[49]. Studies that fall into
(
the sub-category of “real urban areas—with validation” include validation based on one or more of
the simulation parameters with measurements. According to Toparlar et al. [46], for real urban areas,
the field measurement of air temperature is relatively straightforward and has been widely used,
especially in recent years. Therefore, the validation was typically performed by comparing the air
∗
temperature reported in the CFD with the stated(values
) from the field data. The measurement point
was in the middle of Al Khayamiya Alley. As shown in Figure 5, the model included all the important
physical phenomena likely to occur, and these were gradually refined until a constant solution was
∗
achieved [61].
The mesh spacing was coarsened with 0.48 m as minimum spacing. Recommended skewness is
below 0.98 as per the ANSYS FLUENT tutorial, and mesh spacing was refined for the areas around the
model, to reach 0.24 m.
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Figure 5. The meshing domain for the existing case, with the coarsened area 10 m above the model.
3. Results and Discussion
3.1. The Thermal Sensation Votes
Due to the variations in the microclimatic parameters between day and night, the inhabitants’
thermal perceptions were examined based on the interviewees’ thermal sensation votes in relation to the
calculated PET. According to Fanger’s theory [62], only votes of −1, 0, and +1 represent satisfaction with
the thermal environment. The interviews were conducted with respondents who were predominantly
local people living or working in the area, with an outdoor staying time of more than 20 min. As shown
in Figure 6, a high percentage of respondents who voted for the three central TSV categories occupying
−
Al Khayamiya Alley were satisfied with the thermal environment, where the thermal satisfaction
levels were higher during the daytime recording 61% of the total sample compared to 51% during
the night-time.
Figure 6. Subjective thermal sensation votes in daytime and night-time in the summer.
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This might be explained by the existing shading settings, which improve heat stress during the
day with a high level of shielding from direct solar radiation, but cause discomfort at night due to the
low sky view factor, which delay or obstruct night purge [34,38].
3.2. Thermal Perception and Neutral PET
As illustrated in Figure 7, people’s thermal sensations within the alley were further investigated
using the ordinal logistic regression model (OLRM), which is a common method of modelling the
relationship between thermal sensation and various factors.
Figure 7. Correlation between actual physiological equivalent temperature (PET) and thermal sensation
votes in Al Khayamiya Alley.
A simple linear regression was used for the mean TSV for each 1 ◦ C degree PET interval.
This produced Equation (7), regarding the summer season and describing the correlation between the
mean thermal sensation votes (MTSVs) and PET.
MTSV (summer shaded) = 0.1611 (PET) − 4.816
R2 = 0.9156
(7)
Neutrality was derived by solving the MSTV of zero. The neutral PET is the thermal point at
which people feel thermally neutral (neither cool nor warm). Neutral PET was derived by solving the
MSTV of zero in the previous equations (Table 2).
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Table 2. PET at different levels of thermal sensation (only votes of −1, 0, and +1 represent satisfaction
with the thermal environments [62].
MTSV
Thermal Sensation
PET (◦ C) Summer
−3
−2
−1
0
1
2
3
Cold
Cool
Slightly cool
Neutral
Slightly warm
Warm
Hot
<17.6
17.6
19
30.1
36
42
>42
The linear regression revealed a strong relationship between perceived comfort and PET for all
cases, where the R2 is 0.92. The neutral PET is 30.1 ◦ C under the shade. According to a previous study
in a similar context, neutral PET was 29 ◦ C in summer for exposed areas [16]. By referring to Fanger’s
theory [62], it can be assumed that subjects who only votes of −1, 0, and +1 represent satisfaction
range with the thermal environment, which, in this case, varies between the PET value of 19 to 36 ◦ C.
This can be explained by people’s tendency to accept a higher temperature under shaded locations
more willingly than in sun exposed ones [28]. In another study, it was found that people with a higher
degree of control over their source of discomfort tolerate wide variations, and negative emotional
responses are greatly reduced [63]. This may be true of the current study, as the occupants could avoid
intense solar radiation by seeking available shade.
3.3. Correlation between Thermal Response Votes and Microclimatic Parameters
The correlation between the thermal sensation votes and the other environmental parameters of air
temperature, wind velocity, and solar exposure were all recorded and analysed using Spearman’s Rank
Order Correlation Coefficient. This quantifies the strength and direction of association between two
variables measured on at least an ordinal scale [64], to identify the most significant impact regarding
outdoor thermal comfort. According to Table 3, sun exposure was the predominant stimulus on
people’s TSV with a correlation coefficient of 0.680, followed by air temperature (0.51).
Table 3. Correlation analysis of thermal response votes.
TSV
TSV
Correlation
coefficient significant (2-tailed)
N
a
Air Temperature
1
100
a
0.51
0.000
100
Wind Speed
−0.179
0.000
100
a
Sun Exposure
0.680 a
0.000
100
correlation is significant at the 0.01 level (2-tailed).
Wind velocity had less effect on TSV, counting for a small correlation coefficient of −0.179.
The findings indicate that people’s TSVs seemed to increase with an increase in the sensation of sun
exposure, followed by air temperature, and a decrease, with an increase in wind speed.
3.4. CFD Simulations: Validation and Comparative Results
3.4.1. The CFD Model Validation Results
The simulated CFD air temperature was validated against reported data from the field
measurements, and the full set of data, in addition to the wall surface temperature, as presented in
Figure 5. The data used as input in the CFD simulation, in addition to the simulated and the measured
air temperature in the middle of the alley, are presented in Table 4, and the validation results at
three-hour intervals were plotted for better illustration, as shown in Figure 8.
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Table 4. Validation of air temperature taken at the middle of the alley in addition to model input of
surface temperatures.
Time Intervals
Hours
For Validation Purposes
Measured
Air temperature
3.00
6.00
9.00
12.00
15.00
18.00
21.00
24.00
Simulation Inputs
Simulated Outcomes
28.0
27.8
35.1
36.0
36.0
35.0
31.0
28.0
Wall 1
Wall 2
(◦ C)
Wall 3
Wall 4
Surface temperature
27.1
27.0
33.8
35.1
35.0
34.2
29.9
26.8
26.8
26.3
28.0
30.6
33.0
32.8
29.5
28.5
27.5
27.6
30.3
31.7
32.6
31.8
30.0
29.1
Roof
(◦ C)
27.4
26.8
28.6
31.6
39.0
35.7
30.8
29.4
27.0
27.7
35.9
32.9
33.9
32.4
29.9
28.8
31.1
30.2
33.2
37.7
39.2
36.7
34.1
32.5
Air temperature (oC)
39
37
35
33
31
29
27
25
0
3
6
9
12
15
18
21
24
Daily hours
Air temperature measured
Air temperature simulated
Figure 8. Actual measured air temperature under the shaded roof against CFD simulation output at
pedestrian level for validation purposes.
The CFD outcomes displayed a consistent trend against the field measurements, with small
differences varying between 0.8 ◦ C–1.2 ◦ C. This indicates the ability of the designed model to address
the existing pattern of air temperature in the current study, and, in turn, provides some confidence in
applying the same settings to examine the iterations for the suggested shading scenarios.
3.4.2. Comparative Results
As per the BPG, the same settings used for the validations were applied to the test scenarios,
and then a cross-comparison of the impact of each scenario was observed, including the air temperature
distribution, natural ventilation performance, and effect on outdoor thermal conditions. The six tested
scenarios, as presented in Figure 3, were all directed on the same track as the prevailing wind direction,
and each scenario was based on a certain geometric change regarding the roof shape and opening
positions. The boundary conditions were adjusted based on the same date as the validation to represent
a typical summer day, and simulation was adjusted to night-time. As stated, the high level of shading
elevates thermal comfort during summer days, but at night, reduces the long wave radiation loss rate
on surfaces, causing a higher temperature compared to unshaded sites [30,38,65].
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3.4.3. Comparison of the Vertical Profiles of Mean Wind Velocity
To investigate wind speed effect at the pedestrian level, a vertical profile representing the mean
wind velocity was located in the middle of the alley. The profile is 7 m tall from ground level to the top
of the roof, with a wind speed reference of 3 m/s at 10 m height, as shown in Figure 9.
Figure 9. Vertical profiles located in the centre of the alley facing the prevailing wind route.
According to the measurements taken along the vertical profiles (Figure 10 and Table 5), all cases
share similar profiles at lower levels before they are very close to the opening levels, and then the
profiles’ contour lines start to vary.
8
7
Height (m)
6
case1
5
case2
4
case3
3
case4
2
case5
1
0
case6
0
0.5
1
1.5
2
2.5
Wind Velocity (m/s)
Figure 10. Simulated mean wind velocities located at the 7 m height vertical line centre alley.
In the first two cases, they have almost the same values and vertical contours for wind speed, until
1 m under roof level. Case 1′ s wind velocity went down to 1.1 m/s, from 1.9 m/s, where the rooftop is
solid, with no openings, before it reaches, again, 2 m/s above the roof. Case 2 has a centred rooftop
opening that released the wind slightly slower, at about 0.05 m/s. However, in cases 3 and 4, the side
openings caused a greater reduction in wind velocity by enabling its release from both sides. Both cases
have two side openings, but with different locations, as shown in Figure 3, and this appeared to have
a negligible effect on wind speed or the profile underneath, as both cases reported an equal maximum
wind velocity of 2.1 m/s, and almost equal rates at roof level of 0.70 m/s and 0.75 m/s, respectively.
For cases 5 and 6, which have one more opening in the centre of the roof than the previous two cases,
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their greater number of openings and locations assisted wind release, while maintaining wind speed.
Both cases had the same vertical contours regarding average wind velocity along the whole profile
and, as a result, the number and location of the openings in the shading surface is crucial to shaping
the wind speed and profile underneath.
Table 5. Simulated mean wind velocity outcomes for each case along the 7 m vertical profile.
Height
Case 1
Case 2
m
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
Case 3
Case 4
Case 5
Case 6
0
0.78
1.43
1.49
1.53
1.56
1.625
1.68
1.75
1.82
1.9
1.98
2.08
2.12
2.15
0
0.68
1.2
1.54
1.6
1.65
1.74
1.8
1.85
1.92
1.98
2.05
2.1
2.1
2.04
Velocity (m/s)
0
0.7
1.23
1.38
1.45
1.5
1.58
1.65
1.7
1.78
1.82
1.9
1.3
1.1
2.05
0
0.65
1.2
1.4
1.48
1.55
1.6
1.65
1.7
1.73
1.78
1.825
1.8
1.86
1.975
0
0.68
1.19
1.4
1.47
1.55
1.6
1.68
1.725
1.8
1.88
1.95
2.1
1.7
0.7
0
1
1.41
1.44
1.52
1.58
1.65
1.7
1.78
1.83
1.95
2.02
2.1
1.2
0.75
These findings match well with a preceding study stating that urban geometry is mainly responsible
for wind behaviour at pedestrian level, and not building height, which significantly influences the
wind environment on a larger scale [66] (Ng et al., 2011). A study by Ng et al. [67] on achieving urban
thermal comfort with wind speed threshold values at pedestrian level in hot regions, for a typical
summer’s day, a wind speed range of 0.6–1.3 m/s was required to achieve neutral thermal sensation
(neutral PET: 28.1 ◦ C). Another study for the same climate conditions found that 1.6 m/s wind speed
was required to achieve outdoor thermal comfort [68]. Yuan and Ng [69] proposed five different
categories for pedestrian-level wind speed in a street canyon, as presented in Table 6.
Table 6. Classification of pedestrian-level natural ventilation (u) in street canyons [69].
Class 1
Class 2
Class 3
Class 4
Class 5
U < 0.3 m/s
0.6 m/s > u ≥ 0.3 m/s
1.0 m/s > u ≥ 0.6 m/s
1.3 m/s > u ≥ 1.0 m/s
U ≥ 1.3 m/s
Stagnant
Poor
Low
Satisfactory
Good
Based on these results, all of the examined cases in the current study fall within the fifth class
of wind speed ≥1.3 m/s, achieving thermal comfort by natural ventilation. Case 6 represents ideal
conditions by recording 1.6 m/sec, according to the recommendations of Cheng et al. [68]. However,
the outdoor environment is heterogeneous, and relying alone on wind velocity for describing thermal
comfort is not enough, therefore, the study used the wind velocity outcomes for each case to calculate
the PET as the main comfort index in the later section.
3.4.4. The Ventilation Flow Rate
According to Hang et al. [70], the effect of semi-open street roofs on the natural ventilation
underneath is less understood as it may produce various flow patterns and ventilation capacities.
Therefore, it was very important to quantify the effects of the roof opening on the ventilation by
quantifying the flow rate. Table 7 presents the normalised flow rates (Q*) for the examined case studies,
Buildings 2020, 10, 238
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where the positive values represent the incoming air and the negative values denote the existing air,
while Figure 11, shows the overall performance of the flow rates for each case.
Table 7. Volumetric flow rate (Q) (m3 /s) through the alley’s openings. Positive Q values refer to air
entering (inlet) and negative values denote air leaving (outlet).
Case 1
Front opening
Back opening
+26.77 (inlet)
−26.77
Case 4
Front opening
Back opening
Upper left
opening
Upper right
opening
+47.88 (inlet)
−36.50
−5.63
−5.73
Case 2 (Base Case)
Front opening
Back opening
Roof opening
+29.48 (inlet)
−22.53
−6.94
Case 5
Front opening
Back opening
Upper right
opening
Upper left
opening
Roof opening
+48.66 (inlet)
−35.38
−4.86
−4.79
−3.61
Case 3
Front opening
Back opening
Upper left
opening
Lower right
opening
+41.99 (inlet)
−29.84
−6.04
−6.10
Case 6
Front opening
Back opening
Upper left
opening
Upper right
opening
Roof opening
+49.99 (inlet)
36.69
−4.28
−4.09
−4.92
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60
50
m3/s
40
30
20
10
0
case 1
case 2
case 3
case 4
case 5
case 6
Figure 11. Total volume flow rate (m3 /s).
Although cases 1 and 2 have an equal space volume of 464 m3 , it was found that case 2 had 10%
more flow rate than case 1 due to the roof opening accounting for 23.5% of the entire flow rate.
Regarding cases 3 and 4, the latter recorded a higher flow rate due to its slightly greater volume
space compared to the former, but the two-sided roof opening in these cases both accounted for 30%
of the total ventilation flow rate. Again, by assessing cases 4 and 5, which shared the same volume,
case number 5—with an extra opening in the centre of the roof—accounted for only a 2% higher
volume rate than case 4. In cases 5 and 6, both have the same top opening numbers and locations,
but a different roof shape (case 5 has a flat roof and 6 a vaulted shape). Both cases recorded the same
amount of total volume rate of 27%, and yet the top centre opening in case 6 was responsible for 10%
of the total volume flow rate, compared to 7.4% for case 5. This improved the volume flow by 2.8%
for case 6 against case 5, possibly because of the roof shape. It has been suggested that vault-shaped
roofs increase the inflow rate and redistribute the internal airflow by directing some of the air to exit
through the roof openings instead of the wall openings [71].
3.4.5. Comparison of Air Temperature Distribution
The same vertical profile settings used to examine the mean wind velocity were applied to the
analysis of the mean air temperature, with 35 ◦ C being the reference air temperature at the inlet as
reported in the in situ field measurements. Table 8 and Figure 12 represent the mean air temperature
profiles for the six cases.
Each simulated mean air temperature profile was about 7 m high, starting from ground level to
the top of the roof. All of the examined cases had mean air temperatures varying between 32.4 ◦ C
and 33.4 ◦ C at the ground level (Table 8), with only a 1 ◦ C difference between all cases. These values
were lower than the inlet air temperature, possibly due to the ventilation flow rate and shading effect.
The situation changed as the mean air temperature difference started to increase. Case 1 had the highest
temperature, followed by cases 2 and 3, with a slight difference of less than 0.2 ◦ C. Although these
cases have similar vertical profile contours, close to the roof level, case 1 had a slight increase in air
temperature, while in case 3—with two side openings—the air temperature had the same increase as
case 1, before falling again at the side opening level. When comparing the profile trend of cases 4 and
5, both had very similar air temperature distribution by profile, but case 5 had an extra opening mid
roof, which gave the indication that the centre location of the opening may have had less influence on
air temperature distribution than wind velocity. Although case 6 had the same number and location of
Buildings 2020, 10, 238
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openings as case 5, it was noted that it had the lowest air temperature along the vertical line compared
to other cases. In this case, the air temperature suddenly increased by 1.5 ◦ C once it reached the side
opening level, to reach the inlet temperature of 35 ◦ C. This may refer to the mode of heat transfer
from the semi-closed space to the outside air temperature [72]. The air temperature dropped again by
the same amount (1.5 ◦ C) right under the roof top. This variation in air temperature between cases 5
and 6 may be due to the vaulted shape in case 6, as the top roof centre opening seemed to have no
effect on the air temperature underneath, as mentioned previously. Hence, case 6 may be the best of
the examined scenarios for having the lowest air temperature, especially at pedestrian level, as the
difference in air temperature was 1 ◦ C compared to cases 4 and 5, and 1.6 ◦ C compared to case 2.
This may be explained by Hadavand and Yaghoubi [73], as in their study the vaulted shape received
less solar heat per unit area due to the curved shape, which in turn lowered surface temperatures,
and assisted heat reradiation after sunset. In addition, heat transmission by vaulted or curved roofs
to the interior is reduced compared to flat ones [74]. Overall, air temperature in the current study
was directly proportional to the height of the profile, and there was a direct proportional relationship
between air temperature and profile level; that is, the higher the level, the higher the air temperature,
until the air reached the side openings, when the air temperature reached its peak due to the direct
interaction with the outside air temperature. This warm air migration to the top of the semi-enclosure
offers possible solutions for more comfortable adjustment, resulting in the availability of cooler air at
the ground level in the pedestrian zone. In addition, the hot air reservoir at higher levels may have
been discharged through the upper level opening and, thus, generated a cooling airflow at lower levels,
steered by the stack effect [72].
Table 8. Simulated mean air temperature (◦ C) outcomes for each case along the 7 m vertical profile.
Height
Case 1
Case 2
m
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.2
6.5
7
Case 4
Case 4
Air Temperature
32.5
33.6
34.5
34.6
34.7
34.7
34.7
34.8
34.8
34.8
34.8
34.9
34.9
34.9
34.9
35.0
33.4
33.7
34.7
34.8
34.8
34.8
34.8
34.9
34.9
34.9
34.9
34.9
34.9
34.9
34.9
35.0
33.3
33.5
34.5
34.6
34.7
34.7
34.7
34.8
34.8
34.8
34.8
34.9
34.9
34.9
34.9
34.8
Case 5
Case 6
32.8
33.4
33.9
34.0
34.1
34.2
34.2
34.3
34.3
34.4
34.5
34.5
34.7
34.8
34.5
34.0
32.4
32.7
32.9
33.1
33.1
33.1
33.2
33.2
33.3
33.3
33.3
33.4
34.4
34.9
34.2
33.5
(◦ C)
32.5
33.5
33.9
34.1
34.1
34.2
34.2
34.3
34.3
34.4
34.5
34.5
34.7
34.8
34.6
34.0
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8
7
Height (m)
6
5
case1
case2
4
case3
3
case4
case5
2
case6
1
0
32
32.5
33
33.5
34
34.5
Air temperature
(oC)
35
35.5
35oC
Air
temperature at the
Figure 12. Simulated mean air temperature at the 7 m height vertical line in the alley centre.
Moreover, by examining the relationship between the mean air temperature and the previous
analysis regarding wind velocity, a negative correlation was found, where an increase in wind speed
would be equal to a reduction in the air temperature and vice versa (Figure 13).
Air Temperature (oC)
35.5
35
34.5
34
33.5
33
32.5
1.25
1.3
1.35
1.4
1.45
1.5
1.55
Velocity (m/s)
Figure 13. Relationship between mean air temperature and wind velocity at pedestrian level.
Similar outcomes were found in previous studies [67–69,75,76].
3.4.6. Mean Radiant Temperature and PET
Relying on wind velocity and air temperature alone to evaluate overall thermal comfort is
insufficient, and it is essential to apply a thermal comfort index using the main microclimate parameters
as described in the ASHRAE Standard [43]. Therefore, as a comfort index, the physiologically
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equivalent temperature (PET) [77,78] was calculated for each case using the Rayman model [79,80] and
the simulation output for the dominant microclimatic parameters. The exception to this was mean
radiant temperature, which was calculated for each case based on the following equation ASHRAE [45],
Equation (8):
h
4 1.1 × 108 Va0.6
1
Tmrt = T g + 273.15 +
(8)
× (T g − Ta )] 4 − 273.15
0.4
εg D
(T g ) is the globe temperature (◦ C), (Va ) is air velocity (m/s), (Ta ) is the air temperature (◦ C), D (mm) is
the globe diameter (=25 mm), and (ε g ) is the emissivity of the sphere (=0.95 for a black globe).
All of the parameters for Equation (8) were available except for the globe temperature (Tg),
which had to be recalculated using the simulation outcomes for air temperature and wind velocity in
each case, by applying the following equation [81,82], Equation (9):
Tg =
B + CTa + 7680000
C + 256000
(9)
where, (Ta ) is air temperature and B and C are defined as, Equation (10):
!
fdb
1.2
f
B=S
+ (εa )Ta4
+
σ di f
4 σ cos(z)
C=
(10)
hv0.58
(5.3865 × 10−8 )
(11)
where, (Ta ) is air temperature (◦ C), (σ) the Stefan–Boltzmann constant = 5.67 × 10−8 , (S) solar irradiance,
( fdb ) direct beam radiation from the sun, ( fdi f ) diffuse radiation from the sun, (z) solar angle to zenith,
(s) solar irradiance (W/m2 ), (h) convective heat transfer coefficient, (v) wind velocity (mph) and (εa ).
According to Hunter and Minyard [83], thermal emissivity can be calculated using the following,
Equation (12):
1
εa = 0.575ea( 7 )
(12)
where, (ea) is atmospheric vapour pressure.
Equation (2) calculates globe temperature, according to an experiment by Dimiceli et al. [81,82],
in which output variations vary within 0.27 ◦ C between measured and estimated temperatures.
All of the data used in the equation, in addition to the globe, mean radiant temperature, and PET,
are presented in Table 9.
Table 9. Values used to calculate globe temperature (Tg), mean radiant temperature (Tmrt) and PET, in
addition to the constant values used in Equations (9)–(12).
Parameters
Case 1
Case 2
Case 3
Case 4
Case 5
Case 6
v (m/s)
Ta
B
C
Tg
Tmrt
PET
1.38
34.67
2,668,708.03
783,414,319
34.67
34.75
35.2
1.4
34.78
2,702,738.3
789,979,636
34.78
34.84
35.2
1.4
34.68
2,671,788.3
789,979,636
34.68
34.79
35.1
1.45
34.05
2482871
806,222,801
34.05
34.16
34.2
1.49
34.04
2,479,955.6
819,048,598
34.04
34.15
34.2
1.54
33.05
2,203,796.82
834,879,223
33.05
33.06
32.9
Constant Values Used in Equations (9)–(12)
z
90.22
s
0 (at night)
fdb
0
ea
22.48619
h
0.127660528
fdif
100
Some variables are the same for all cases, including the vapour pressure, solar angle to zenith,
and solar irradiance, as the examined cases share the same conditions and location. Based on the
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calculated PET and the acceptable range calculated previously from the survey, none of the six scenarios
successfully reached the acceptable range. However, case six achieved a reduction in PET of 2.3 ◦ C
compared to the base case, followed by cases 4 and 5 with 1 ◦ C differences. These differences in the
comfort range were designated slightly warm, as per the PET classification, and this only came from the
shading parameter. Nevertheless, by adding different parameters, such as vegetation [84,85], type of
materials, and albedo [86–88], a further reduction and better comfort range is expected.
3.4.7. Outputs Reflection on Psychometric Chart
To predict the performance of various shading scenarios on urban thermal comfort, analysis
of measured field data is triangulated with subjective survey responses and computational Fluid
Dynamics (CFD-ANSYS) modelling. A summary of the results of iterations of the shading scenarios
to the base case are presented in Table 10. Table 10 shows changes to air temperature, air velocity,
and PET.
Table 10. The final environmental outcomes and PET comfort index for all the examined case studies.
Cases
1
2 Base Case
3
4
5
6 Best Case
Volume Flow Rate
Air Velocity
Air Exchange Rate
Air Temperature
PET
(m3 /s)
m/s
m3 /h
(◦ C)
(◦ C)
26.77
29.48
41.99
47.88
48.66
49.99
1.38
1.4
1.4
1.45
1.49
1.54
207.7
228.7
277.8
295
299
300
34.67
34.78
34.68
34.05
34.04
33.05
35.2
35.2
35.1
34.2
34.2
32.9
Results indicate the mutual relationship between changing the roof shape and its openings on
environmental parameters affecting thermal comfort and the cooling effect for the pedestrians at street
level. Results indicate the best reductions of 2.3 ◦ C in air temperature and 32.9 ◦ C as PET can be
achieved when the roof configuration is vaulted with central and side openings (Case 6), compared to
air temperature of 34.8 ◦ C and PET 35 ◦ C recorded when the roof configuration is flat and with one
centre opening (Case 2). Applying the simulation results on a psychometric chart will make it easier to
link between the best and base case, in accordance to the neutral PET and comfort range developed
from the in situ field measurements and questionnaire survey on phase one.
Firstly, Figure 14 presents the thermal comfort acceptable range and neutral PET according to the
estimates from the in situ field measurements and the subjective questionnaire, in addition to the PET
obtained from the CFD modelled cases.
Plotting results of this study from the field measurements and survey on the (ISO 7730:2005) [89]
for thermal comfort psychometric chart, considering relative humidity in Cairo ranges between 40 and
60% year-round, it can be concluded that people of the examined site express a wider range of comfort,
compared to the ISO 7730 scale (Figure 15).
This confirms that people who live in hot arid climates have a wider range of adaptation and
tolerance to local climatic conditions year-round, compared to the range suggested by the standard for
moderate climate conditions. Similar studies have reported the same conclusion, such as, Lin [90],
who examined the outdoor thermal comfort in a hot and humid subtropical climate, where the PET
acceptance range was 21.3–28.5 ◦ C, higher when compared to a European scale of 18–23 ◦ C PET. Cohen
et al. [22] also reported the same regarding the Mediterranean climate, as the PET values were higher by
3 ◦ C than the European scale. Moreover, Elnabawi et al. [16] reported a thermal comfort range of 23–32
◦ C PET for a hot arid climate, which was found to be higher than both temperate and humid subtropical
climate acceptance range. This supports the claim of many scholars that thermal comfort indices
established in temperate areas are unsuitable to be applied elsewhere. Local subjective comfort data
acquired for the examined site incorporated in predictive comfort studies provides a better assessment
of outdoor thermal comfort representing the locals who are in regular contact with the space [16,22,28].
Buildings 2020, 10, 238
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Figure 14. Case study psychrometric chart for outdoor thermal comfort acceptable range and neutral
PET according to the estimates from the in situ field measurements and the subjective questionnaire,
in addition to the PET obtained from the CFD modelled cases.
Figure 15. Psychometric chart summarises the study outputs where the comfort range (the blue dotted
lines) compared against the ISO 7730:2005 [89] comfort range. The best case no. 6 (32.9 ◦ C PET) and the
base case no 2 (35.2 ◦ C PET) are within the acceptable range however when compared to the ISO range;
only the best case can be considered within the comfort range (slightly warm).
Secondly, although the maximum results of thermal comfort improvement due to changing the
roof configuration did not reach the neutral PET (which is the thermal point at which people feel
thermally neutral), it still predicted a PET reduction of 2.3 ◦ C compared to base case conditions (35.1 ◦ C).
This reduction moves comfort sensation predictions according to estimated ones, and the ISO 7730 [89]
comfort range (slightly warm), from warm zone for the base case to slightly warm conditions for the
best case, which falls within the acceptable comfort range.
4. Conclusions
Human thermal comfort in outdoor spaces in hot arid regions may depend as much on the
radiant load to which a pedestrian is exposed as to air temperature. Accordingly, scholars have
found that shade plays the main role in tackling outdoor discomfort in hot arid climates, especially in
summer [12,28,30–33]. Applying any type of shading form or arrangement within an existing urban
site influences other microclimatic parameters, and most likely affects thermal perception and comfort.
Buildings 2020, 10, 238
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Few studies have provided an in-depth analysis that considers multiple types of shading strategies [35].
Therefore, the comprehensive framework considered in this study, which integrates quantitative and
qualitative parameters, and links these to the microclimatic environment with a subjective thermal
assessment, adds to the field. This framework works on two levels; the first includes the on-site field
measurements accompanied by the field survey, for which the following findings were recorded:
•
•
•
The percentage of people satisfied with the thermal environment during the day (61%) was more
than those satisfied with the thermal conditions during the night (51%). This may be explained
by the performance of the shading elements, which assist in reducing the amount of direct solar
radiation. This results in reduced heat released from the surrounding surfaces, reducing air
temperature during the day; however, at night, these shading elements obstruct the accumulated
heat underneath the shading system from release, causing a delay in heat release and boosting the
UHI effect [65];
Direct solar radiation is the dominant microclimatic parameter in shaping people’s thermal
perceptions, with a correlation coefficient of 0.680 (Table 2);
Neutral PET was 30.1 ◦ C under the shade on summer days.
Based on outcomes, such as the microclimatic parameters from the field measurements, and the
actual TSV and PET from the field survey, the framework incorporates numerical modelling and
parametric analysis as a subsequent level. The study examined six different shading scenarios using
CFD, addressing the type of shape and number of openings and their locations within the shading
elements. In so doing, the study examined the overall thermal performance underneath, based on
dependent variables of air temperature and wind velocity distributions, ventilation flow rate, and the
PET. The findings at this level are summarised as follows:
•
•
•
•
•
•
•
The shading form and opening location proved to influence the wind vertical profiles underneath.
The analysis showed that the wind profiles for the examined scenarios tended to follow similar
patterns, starting from the ground level until they became close to the opening levels, at which
point each profile started to have its own shape and speed based on the opening location or the
roof shape;
The shading device shapes and opening locations were dominant features in causing a reduction
in air temperature within the urban scale. This alteration in the shading form led to a reduction of
2.3 ◦ C in air temperature for the best case, case 6;
A positive relationship was found between the air temperature vertical distribution and the profile
levels, where the higher the level, the higher the air temperature. This migration of the stratified
hot air to the roof top and the shading devices may be a possible solution for more comfortable
adjustments, resulting in the availability of cooler air at the ground level in the pedestrian zone.
In addition, the hot air reservoir at higher levels may be discharged through the upper level
opening and, thus, generate a cooling airflow at lower levels, steered by the stack effect [72];
A negative correlation was found between the wind velocity and the air temperature underneath,
since there is a reduction in the air temperature when the wind velocity increases, and vice versa.
This relationship was expected as it has been reported previously in several studies [68,69,75,76];
In terms of ventilation flow rate, both the number and location of openings was found to be
the key to better performance. The cases with side and roof openings showed an increase in
ventilation volume rate of 23–30% compared to those with roof or side openings;
Again, in terms of ventilation, the use of a vaulted roof increases the inflow rate (10%) compared
to a flat roof (7.4%). According to Asfour and Gadi [71], the vaulted roof can be used to improve
the natural ventilation underneath as it redistributes the internal air flow by attracting some air to
leave through the top opening in the roof;
The vaulted shape proved to receive less solar heat per unit area due to the curved shape, which in
turn led to lower surface temperatures; it thus assists reradiation after sunset [73], and additionally
Buildings 2020, 10, 238
•
•
22 of 26
the heat transmission of a vaulted or curved roof to the interior is reduced compared to flat
ones [74];
All of these factors led to a reduction in overall thermal comfort (PET), as the best case with the
vaulted roof (case 6) recorded 32.9 ◦ C compared to 35.2 ◦ C for the Base Case (case 2), which is
2.9 ◦ C from neutral or accepted PET and within three central votes of thermal satisfaction [62];
The reduction in air temperature was due to the vaulted shape of the roof with three openings, as it
causes a higher air velocity and higher air exchange rate underneath, which has a positive effect
in decreasing the air temperature. Moreover, the vault shaped roof with its curved surface area is
considerably larger than the base case roof with no openings, and so receives less solar heat per
unit area; thus, lowering surface temperatures and facilitating reradiation after sunset. The vaulted
configuration Continuously, this process improved the thermal comfort of the pedestrian area,
as the PET for the best case 6 was about 32.9 ◦ C against 35 ◦ C for the base case, which was only
0.9 ◦ C, close to the thermal acceptable range on the hottest day of the year.
In conclusion, the paper proposes a comprehensive interdisciplinary framework that can correlate
between the different levels of assessment, including objective assessments, such as in situ field
measurements and subjective ones, including questionnaires and site observations, which proved to be
very reliable methods in tackling the thermal comfort, defined as state of mind that expresses satisfaction
with the surrounding environment, indicating that comfort is a physiological and psychological
condition. Accordingly, thermal comfort has been examined solely, from either a physical or
psychological point of view, without any serious attempt to correlate and impose these results
into urban design proposals.
Therefore, based on the calculated and calibrated comfort index obtained from the site
measurements and social survey. The first three objectives in Section 2 (methodology) have been
achieved. However, when this comfort index was taken to the next phase in an attempt to correlate
between the social and psychological level of assessment obtained from phase one, with the design
level of phase two, by acquiring the parametric modelling for different shading scenarios based on
the previously calculated PET and acceptable range, none of the scenarios successfully reached the
acceptable range. However, the outcomes were very promising, where one of the scenarios achieved
a 2.3 ◦ C reduction of the PET compared to the base case, which definitely opens the door for more
studies, for testing more shading scenarios, or adding more parameters, such as vegetation [84,85],
type of materials, albedo [86–88], and some active techniques, where a further reduction and better
comfort range is expected, as well as testing more frameworks, including different levels of assessment
in relation to the design.
Author Contributions: Conceptualization, M.H.E. and N.H.; Formal analysis, M.H.E.; Investigation, M.H.E.;
Methodology, M.H.E. and N.H.; Supervision, N.H.; Writing—review and editing, M.H.E. and N.H. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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