Green Space Factor In Modifying The
Microclimates In A Neighbourhood:
Theory And Guidelines
Ar.I.Chandramathy, M.Arch
[Department of Architecture, Thiagarajar College of Engineering, Madurai]
Dr.JinuLouishidhaKitchley, PhD
[Department of Architecture, Thiagarajar College of Engineering, Madurai]
ABSTRACT
Cities and rural environments differ substantially in their land surface temperature, which leads to
urban heat island effect (UHI). Cities have a dynamic relationship with the microclimate. Landscaping
is one of the most effective passive design strategy compared to other passive design strategies in
mitigating the UHI effect. The degree of 'greenery' or 'greenness' (Green space factor) is usually defined
and measured as the percentage of total urban area that is devoted to open green spaces. The higher the
percentage of green cover, the greener that particular city becomes. National forest policy, India states
that a 20% to 33% of green cover is considered to be fairly good. The green spaces help to alter the
temperature, reduce the urban heat island effect and improve the air quality. In most cities, concentrated
vegetation is seen only in parks or recreational spaces. This lowers temperatures on the microclimate of
the park but does not have any effect on the microclimate of the neighbouring built environments. By
placing vegetation within the built space of the urban fabric, the effect of UHI effect can be reduced
where people live, work and spend most of their lives. Such approaches have been investigated in the
fields of planning, urban design, landscape architecture, environmental engineering. Selection of right
plant in the right place can be based on many aspects such as its thermal performance. It further
depends on various plant typologies and their characteristics which will have significant role in urban
heat balances by reducing the land surface temperature and reduce energy consumptions in the dense
built up areas. It also helps to improve the microclimate performance in the built environment and also
create a visually appealing environment compared to other passive techniques. This paper describes the
importance of relationship between green space factor and microclimate and implementation of these
guidelines in a neighbourhood with various case examples from research papers, literature and theories.
The study has been carried out with on site observation and Envimet simulation methods.
Keywords: urban heat island, green space factor, green spaces, Envimet
1. INTRODUCTION
Climate, buildings, and green spaces have been explored worldwide by many researchers due to
their interesting interrelationships and significant impacts to the environment. In recent years, urban heat
island effects(UHI), induced by urban form, anthropogenic heat from buildings and Air conditioning
systems have been studied extensively in cities around the world (1). Since the mid twentieth century,
the global surface temperature has increased by 0.7±0.18°C during the 100 years ended in 2005. Thus
the increased temperature is connected with increase in UHI through expansion of built up areas and
populated area. The heat island during daytime increases rapidly and takes 3-5 hours to reach the
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1
maximum after sunset. These increased temperatures have implications on electricity, energy
consumption and use of resources which inturn affect the environment. The most sustainable solution to
these energy and environment problems is following more natural passive cooling techniques. Urban
green spaces can directly or indirectly affect local and regional air quality by modifying the urban
climates. Many studies have highlighted how landscape in urban des ign and planning can improve
microclimate and thermal comfort (2). Plant processes such as photosynthesis, Evapotranspiration helps
to reduce the Mean radiant temperature and anthropogenic heat generated from the buildings which leads
to urban heat island effect. This in turn reduces the cooling load of the buildings. The environmental
conditions of urban green space have significant impact on the comfort conditions experienced ins ide
them especially in seasons of stressful climate and the development of sustainability in cities(6). Many
researchers agreed that plants have an effect on the urban temperature and the cooling loads of building
(6, 8). For instance the air temperature distribution was closely related to the distribution of greenery in
the urban areas where for some large urban park, the ambient temperature was 2-3°C lower than
surrounding built-up areas and it shapes a pleasant urban environment (14). Furthermore, the effects of
plants density, plants species, plants distribution and large space of greenery give a large impact, where
greenery reduce the surface temperature and urban heat effect (11). Green interventions in terms of trees,
shrubs, ground covers, green roofs, bioswales or rain gardens, green walls, permeable pavement may be
adopted to achieve comfort and reduce UHI in urban areas. These green interventions are to be
quantified to achieve the specific green space factors. The main objective of this study is to find the
effect of the green space factors in modifying the microclimate.
2. GREEN SPACE FACTOR CONCEPT
In the literature reviewed, the primary metric used to mesure the percentage of green spaces under
the land cover based on the plant types such as lawns, turfs, shrubs and trees are their biological
parameters such as LAI – Leaf area intensity, LAD – Leaf area density. There are several benefits
associated by incorporating plants in the neighbourhood. There are remarkable efforts being made at
different scales for the different types of green space factors which are developed across the world.
Table 1 shows the examples of such initiatives across the world; California’s attempt to reduce C02
emissions by 25% by 2020 (5); Vancouver’s Eco- Density initiative (7); Portland’s effort to reduce
stormwater runoff (1); from an urban landscaping viewpoint, Biotope Area Factor (3), Seattle’s Green
Factor (10) Green Plot Ratio (15), and Malmo Green Space Factor (9), which discusses the usefulness of
various Green Factor. These green rating systems are designed to examine the relationship between the
Green factors or the landscape elements and their performance in the built environment. The green factor
systems are designed to increase the quantity and quality of planted areas while allowing the flexibility
for developers and designers to meet development standards. These studies deal the metric of green
spaces at the scales from one dimension to three dimensions but there is no evidence whether these
metrics are climatically sound. This study analyses the performance of these green space factors.
3 METHODOLOGY
Methods to study the green space factor in modifying the microclimate include both numerical
modeling and empirical analys is, such as using on site measurements using instruments and weather data
obtained from nearest weather stations. With empirical data, the study can be more specific, but have
limitations on time and space. Thus, to have a theoretical understanding of performance of different
vegetation scenarios and their effects on the microclimate, numerical modeling with on site observations
is required. The simulation has been carried out with the help of Envi-met models and simulated
alongwith initial onsite observation which was conducted on 20.03.2014 for the climate monitoring and
the plant distribution was accounted. In this paper, different scenario such as (i) with existing base case,
(ii) nil vegetation, (iii) with turfs and (iv) with trees was selected to assess the air temperature. For the
selected sites, the green plot ratio (15) has been applied and evaluated for its performance on the
microclimate.
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Table 1. Green metrics and policies used around the world(15)
Category of Place
Green metric Goals
metric
One
Inventory of To increase the number
dimensional
plants
of plants
Two
dimensional
Berlin
Biotope Area
factor
Retain high densities
of development, whilst
also developing the
city’s green
infrastructure
Malmo
Greenspace
Factor
Portland
Portland’s
Green
Building
Policy
Increase of green
space per inhabitant
from 33m2 to 48m2
in the urban area and
increase the area of
accessible green space
in the countryside
from 2% to 33%
All new City-owned
facilities to
include an eco-roof
systems
Vancouver
Eco-Density
Building
green Cities’ densification
liveable and affordable systems
Communities
2006
California
California
Global
Warming
Solutions
Act
of 2006(5)
Green Factor
Reduce GHG emissions
in the state to1990
levels (25%) by 2020,
and 80%
below 1990 levels by
2050
2007
New developments in
commercially
zoned areas must
commit 30% of the
parcel area to urban
landscaping
Attempts to account 2007
for various types of
ecologically effective
or
environmentally
friendly green systems
Green
Ratio
Greening the buildings
in cities
Sum of total leaf area 2003
developed in the s ite
divided by the s ite
area. Better correlation
with
the
environmental
performance
of
greenery
Seattle
Three
dimensional
Description
and Year
characteristics
The number of plants
being managed in an
area. Simple to use for
homogenous
or
hetrogenous
plant
populations. Does not
provide information
on plant species
Attempts to account 1994
for various types of
ecologically effective
or
environmentally
friendly green systems
Singapore
Plot
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Quantifies the planting 2001
area. Does not account
for vertical stacking.
Advancement
in 2005
green roofs design to
include an eco-roof
with at least 70%
coverage
3
3.1 AREA OF STUDY
Madurai is the oldest inhabited city in the Indian peninsula and is referred as Kadambavanam
(forest filled with kadamba trees) at the banks of river vaigai. Madurai city has an area of 52 km², within
an urban area now extending over as much as130 km², and it is located from 9°56′N to 9.93°N Latitude
and from 78°07′E to 78.12°E Longitude. It has an average elevation of 101 meters above mean sea level.
The climate is hot and humid, with rains during October to December. Summer temperatures range
between 40 and 26.3 degrees Celsius. Winter temperatures range between 29.6 and 18 degrees Celsius.
The average rainfall is about 85 cm and the average humidity is 65%.
Figure 1 Land use pattern
Figure 2 Land use pattern
Figure 3 LST in 1991TM image. Source - Author
Figure 4 LST in 2001 ETM + image. Source - Author
The impact of green spaces on the urban heat isalnd is more comprehensive if suppor ted by the
appropriate green space factors. The urbanization in Madurai has rapidly increased and has also
increased land surface temperature of Madurai since 1990 to 2001. To substantiate this, study has been
done with Land use patterns for 1991 and 2001 (Figure 1&2) and land surface temperature maps
(Figure3&4) were prepared by using 1990 TM image and 2001 ETM+ image. Land surface temperature
type indicates; “red and orange” with average temperature of 43.9°C and 41.9°C respectively for
urbanized area a with a greater density of buildings and paved surfaces that absorb and retain heat from
the sun, “yellow” with average temperature of 38.9°C for urbanized area a with a medium density of
buildings and paved surfaces that absorb and retain heat from the sun, “green” with average temperature
of 36.9°C for urban green areas, and “blue” with average temperature of 30.9°C for water bodies. This
map shows that urbanization has spread rapidly from year 2001 especially in the central business district
of Madurai. This rapid urbanization has contributed to the increase of urban temperature in Madurai.
This map also shows how extremely green spaces are replaced by the grey spaces without considering
the impact of huge loss of green space to the urban environment. Therefore, this study has examined the
potential of green space factor in modifying the microclimate. Urban neighbourhoods was selected with
dense (Study area 1 – Railway colony) and sparse vegetation (Study area 2 - Periyar) as shown in Figure
5 and compared with hypothetical conditions with mentioned scenarios and has been studied for their
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microclimatic performance is studied using micro scale model ENVIMET (4) due to its advanced
approach on plant atmosphere interactions in cities. The numerical model simulates the complex urban
structures with resolution between 0.5m and 10m according to the position of sun, urban geometry,
vegetation, and soil by solving thermodynamic and plant physiological equations
Figure 5 Study areas. Source: Google Images
1
2
3.2 Model and its validation
Envi-met Version 3.1 (Bruse 2013) has been employed to simulate the potential impact of urban
form and vegetation on the urban microclimate for March 20, 2014, during mid summer day when peak
temperature is experienced. Envi-met is a three dimensional computational fluid dynamics and energy
balance model that simulates plant air interactions in urban environments with a typical horizontal
resolution of 0.5m to 10m in space and 10 seconds in time for built environment from microclimate scale
to local climate scale at any location. Although Envi-met mainly uses a 3D prognostic model, it also uses
1D models to transfer all data input for w ind speed, wind direction, air temperature, relative humidity,
specific humidity and turbulence quantities (Bruse 2004). In order to conduct the simulation, basic data
about the location, cloud cover conditions, initial temperature, wind speed at 10m above ground level,
specific humidity at 2500m and relative humidity at 2m are required. In addition, the initial temperature,
soil temperature (at 0m-0.2m, 0.2m-0.5m, 0.5m-2m), heat transmission in walls and roofs of buildings
can also be defined in the mentioned model. The model gives a large number of output data that include
air temperature, surface temperature, wall temperature, long wave radiation, shortwave radiation, latent
and sensible heat fluxes, PMV, PPD, and MRT as the indicators of outdoor thermal comfort. In order to
achieve realistic results, a simulation of an existing urban area in Madurai was carried out and the results
were compared with on-site measurement temperature (1300LST on 20th March 2014) as shown in
Figure 6. Envi-met was carried out for a 24 hour period starting at 0600LST with model output for every
60 minutes, using the configuration parameters. The relationship of both results was found to be
correlated with an R-squared value equal to 0.876 (Figure 6). The verification process further
rationalizes the use of ENVI-met to study the microclimatic issues in Madurai with hot and humid
climatic conditions.
4 RES ULTS AND DISCUSS IONS
Envi-met s imulation was conducted for both the selected areas and the results reveal that the
green spaces play a major role in modifying the microclimate.
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Figure 6. The relationship between ENVI-met simulation temperature and onsite
measurement temperature (1 pm on 20th March 2014).
4.1 AVERAGE AIR TEMPERATUR E
Figure 7 shows the average air temperature for different scenarios such as (i) with existing base
case, (ii) nil vegetation, (iii) with turfs and (iv) with trees. Figure 8 shows the average air temperature for
case2 13:00:00 20.03.2014
basecase 13:00:00 20.03.2014
different scenarios such as (i) with existing base case, (ii) nil vegetation, (iii) with less number of trees
and (iv) with increased number of trees since providing turf or lawns is not possible in this case as it has
dense urban pattern with wall to wall construction.
40
x/y cut at z= 3
x/y cut at z= 3
30
40
40
case2 13:00:00 20.03.2014
basecase 13:00:00 20.03.2014
x/y cut at z= 3
x/y cut at z= 3
Pot. Temperature
Pot. Temperature
unter 305.13 K
unter 304.61 K
30
30
304.61 bis 304.80 K
305.13 bis 305.40 K
305.40 bis 305.66 K
Y (m)
304.80 bis 304.99 K
Pot. Temperature
304.99
20
Pot. Temperature
305.66 bis
bis 305.17 K
305.93 bis 306.20 K
305.17 bis 305.36 K
305.13 bis 305.40 K
304.61 bis 304.80 K
306.20 bis 306.46 K
305.40 bis 305.66 K
305.36 bis 305.55 K
Y (m)
Y (m)
304.80 bis 304.99 K
304.99 bis 305.17 K
20
305.93 K
unter 305.13 K
unter 304.61 K
305.55 bis 305.74 K
305.17 bis 305.36 K
305.66 bis 305.93 K
20
305.93 bisbis
306.20
K
306.46
306.73
K
306.20 bis 306.46 K
305.36 bis 305.55 K
306.73
307.00
K
306.46 bisbis
306.73
K
305.74 bis 305.93 K
305.55 bis 305.74 K
306.73 bis 307.00 K
305.74 bis 305.93 K
307.00 bis 307.27 K
305.93 bis 306.12 K
case1 13:00:00 20.03.2014
307.00 bis 307.27 K
305.93 bis 306.12 K
über 307.27 K
über
307.27 K
über 306.12 K
x/y cut at z= 3
über 306.12 K
10
10
40
10
case5 13:00:00 20.03.2014
x/y cut at z= 3
N
N
0
0
0
10
20
30
X (m)
0
40
Existing base case
<Left foot>
<Right foot>
30
20
N
X (m)
<Right foot>
N
case5 13:00:00 20.03.2014
x/y cut at z= 3
20
30
X (m)
304.96 bis 305.22 K
40
Pot. Temperature
305.22 bis 305.49 K
<Left foot>
305.49 bis 305.76 K
30
<Right foot>
unter 304.75 K
30
Pot. Temperature
306.29
Pot. Temperature
305.21 bis 305.43 K
bis 306.55 K
unter 304.75 K
305.43 bis 305.66 K
304.75 bis 304.98 K
unter 304.96 K
306.55 bis 306.82 K
305.22 bis 305.49 K
306.82 bis 307.09 K
305.49 bis 305.76 K
20
304.98 bis 305.21 K
Y (m)
Y (m)
304.98 bis 305.21 K
306.02 bis 306.29 K
20
<Right foot>
304.75 bis 304.98 K
305.76 bis 306.02 K
304.96 bis 305.22 K
Y (m)
40
40
x/y cut at z= 3
10unter
304.96 K
40
30
Scenario 1
case1 13:00:00 20.03.2014
0
30
20
X (m)
<Left foot>
Pot. Temperature
0
40
10
10
305.66 bis 305.89 K
305.21 bis 305.43 K
20
305.89 bis 306.11 K
305.43 bis 305.66 K
305.66 bis 305.89 K
305.76 bis 306.02 K
über 307.09 K
306.02 bis 306.29 K
306.11 bis 306.34 K
305.89 bis 306.11 K
306.11 bis 306.34 K
306.34 bis 306.57 K
306.29 bis 306.55 K
306.34 bis 306.57 K
306.55 bis 306.82 K
über 306.57 K
über 306.57 K
306.82 bis 307.09 K
über 307.09 K
10
10
10
N
0
N
0
0
0
10
20
X (m)
30
40
Scenario 2
Scenario 3
Figure 7 Air temperature for different scenarios for study area 1
X (m)
<Left foot>
10
10
20
N
20
X (m)
30
40
<Left foot>
<Right foot>
<Right foot>
N
0
30
40
0
<Left foot>
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10
20
X (m)
<Right foot>
30
40
<Right foot>
6
40
50
case1h3g3 13:00:00 20.03.2014
50
x/y cut at z= 3
case1h3 13:00:00 20.03.2014
Pot. Temperature
Pot. Temperature
30
x/y cut at z= 3
unter 308.39 K
unter 308.25 K
40
308.39 bis 308.68 K
40
308.25 bis 308.55 K
308.68 bis 308.96 K
Y (m)
308.55 bis 308.85 K
308.96 bis 309.25 K
308.85 bis 309.14 K
Pot. Temperature
Pot. Temperature
309.14
bis
309.44 K
unter 308.25
K
50
30
unter 308.39 K
30
309.25 bis 309.54 K
308.39 bis 308.68 K
case1h3g1 13:00:00 20.03.2014
308.68 bis 308.96 K
308.25 bis 308.55 K
Y (m)
Y (m)
309.44
bis 309.74
K
308.55 bis 308.85
K
308.85 bis 309.14 K
309.25 bis 309.54 K
309.14 bis 309.44
K
309.74
bis 310.03
K
309.54 bis 309.82 K
309.82 bis 310.11 K
case1h3g2
13:00:00
310.03
bis 310.33 20.03.2014
K
309.44 bis 309.74 K
20
309.54 bis 309.82 K
x/y cut at z= 3
309.82 bis 310.11 K
308.96 bis 309.25 K
310.11 bis 310.40 K
20
309.74 bis 310.03 K
310.40 bis 310.68 K
310.11 bis 310.40 K
x/y cut at z= 3
310.03 bis 310.33 K
20
über 310.68 K
310.33 bis 310.63
K
310.33
bis 310.63
K
310.40 bis 310.68 K
über 310.63 K
über 310.63 K
über 310.68 K
10
10
Existing
base case
40
N
<Left foot>
0
10
20
X (m)
30
40
N
0
0
10
20
X (m)
30
40
50
<Left foot>
0
10
Scenario 1
<Right foot>
50
50
50
<Right foot>
case1h3g2 13:00:00 20.03.2014
Scenario 2
case1h3g1 13:00:00 20.03.2014
x/y cut at z= 3
x/y cut at z= 3
Pot. Temperature
Pot. Temperature
30
unter 308.01 K
unter 308.12 K
40
40
308.12 bis 308.40 K
308.01 bis 308.30 K
N
308.30 bis 308.58 K
X (m)
0
30
40
308.58
N bis 308.87 K
Pot. Temperature
Pot. Temperature
308.97 bis 309.25 K
unter 308.12 K
0
10
unter 308.01 K
308.87
bis 309.15 K
30
308.12 bis 308.40
309.25
bis K309.54 K
20
30
308.01 bis 308.30 K
40
308.40 bis 308.68 K
X (m)
308.68 bis 308.97
309.54
bis K309.82 K
308.97 bis 309.25 K
309.82
bis K310.10 K
309.25 bis 309.54
20
20
<Left foot>
308.68 bis 308.97 K
50
Y (m)
30
Y (m)
20
Y (m)
308.40 bis 308.68 K
50
308.30 bis 308.58 K
309.15
bis 309.43 K
308.58 bis 308.87 K
<Right foot>
308.87 bis 309.15 K
309.43
bis 309.72 K
309.15 bis 309.43 K
309.43 bis 309.72 K
309.72
bis 310.00 K
309.72 bis 310.00 K
309.54 bis 309.82 K
310.10
bis 310.39 K
309.82 bis 310.10 K
20
310.00 bis 310.29 K
<Right foot>
310.00
über 310.29 Kbis 310.29 K
310.10 bis 310.39 K
über
310.39 K
über 310.39 K
über 310.29 K
Scenario 3
10
10
N
N
10
0
0
10
20
X (m)
30
40
0
0
50
10
20
X (m)
30
40
50
<Left foot>
<Right foot>
<Right foot>
Figure 8 Air temperature for different scenarios for study area 2
<Left foot>
N
20
4.2 APPLICATION OF GREEN SPACE FACTORS
X (m)
30
40
50
N
Green
plot ratio (GnPR) by Ong (2003) was known as an effective green assessment method to
0
0
10
20
30
40
50
determine the ratio of green space distribution.
X (m) According to Ong (2003), the GnPR has been defined as
the
average leaf area index (LAI) of the greenery on the site and also can be equivalently defined as the
<Left foot>
ratio of the total single-side leaf area of the planted landscape to the plot or site area. GnPR can be define
as the area-weighted average LAI of a site, which account for unequal amount of area occupied by
different plants in a landscape. Leaf area index can be defined as one-sided area of leaf tissue per unit
ground surface area where the green plot ratio is the only green assessment method that relies on LAI.
For the selected sites, the green plot ratio (15) has been applied and s imulated alongwith initial
onsite observation which was conducted on 20.03.2014 for the climate monitoring and the plant
distribution was accounted. This on site observation was only focused on the study areas, Madurai and
during the observation the temperature found to be 31°C with clear sky conditions.
In this study the green plot ratio have been considered for all the scenarios as shown in equation 1.
The green plot ratio have been carried out as following; existing base case, scenario (i) with no
vegetation of 0 green area factor, scenario (ii) with lawn of green factor 0.304 and scenario (iii) only
trees of green area factor 0.304 had impact on the reduction of temperatures. The calculations,
assumptions, and results were given in Table 2 and 3.
Based on the understanding of the parametric study and Green space factor, the following key
observations were found as given in Figure 7 and 8 which can be useful for urban planners.
First, greening is benefic ial in cooling the urban environment and creating better urban
microclimatic conditions for human activities at the ground level.
Second, tree planting is more beneficial than turfs or lawns (where trees provide shade for the
lawns, to other vegetations and buildings) as evident from the study.
It also suggests that the green factor of above 0.45 is essential to reduce a maximum of 2°C in the
built environment. Whereas in the study area 2 the temperature is reduced a maximum of only 1°C.
<Right foot>
30th INTERNATIONAL PLEA CONFERENCE
16-18 December 2014, CEPT University, Ahmedabad
<Right foot>
7
This also shows that the green factor of maximum 0.34 is not sufficient as it reduces about only
1°C and green factor of above 0.45 is as essential as to reduce a maximum of 2°C in the built
environment.
Table 2.Green plot ratio conditions and ENVImet parametric results for study area 1
Existing
Base Existing
Scenario 1
Scenario 2
Case Recorded Base Case
with
outdoor Simulated
weather
with
monitoring
Envimet
station.
Total site area
12800
12800
12800
12800
Total
landscape 6000
6000
NIL
1950
area
Trees
40
40
NIL
NIL
Palms
NIL
NIL
NIL
NIL
Shrubs
NIL
NIL
NIL
NIL
Turfs
NIL
NIL
NIL
1950
Green Plot Ratio
0.468
0.468
0
0.304
Average
32.67°C
32.87°C
34.02°C
33.84°C
Temperature
found byENVImet
Table 3.Green plot ratio conditions and ENVImet parametric results study area 2
Existing
Base Existing
Scenario 1
Scenario 2
Case Recorded Base Case
with
outdoor Simulated
weather
with
monitoring
Envimet
station.
Total site area
10000
10000
10000
10000
Total
landscape 1440
1440
NIL
2640
area
Trees
6
6
NIL
11
Palms
NIL
NIL
NIL
NIL
Shrubs
NIL
NIL
NIL
NIL
Turfs
NIL
NIL
NIL
NIL
Green Plot Ratio
0.144
0.144
0
0.264
Average
37.8°C
37.98°C
38.1°C
37.54°C
Temperature
found byENVImet
Scenario 3
12800
3900
25
NIL
NIL
NIL
0.304
33.32°C
Scenario 3
10000
3600
15
NIL
NIL
NIL
0.36
37.24°C
5 CONCLUS ION
Design strategies for open spaces and landscape in a site development not only require to
accommodate their density in the s ite development standards, but also it plays an important role in
modifying the microclimate and create thermal comfort since it controls the access of sun, light and
wind. The microclimatic effect of the Green area factors were done with Envimet Numerical model. This
has been done considering only the lawns and the trees. The results shows that trees perform better then
the turfs or lawns. This lead to further investigate and develop green space factors for the thermal
comfort conditions outdoors, based on the empirical data and numerical modeling. This Green space
factor when included and applied in Madurai will help the Urban Designers, Planners and Landscape
architects to decide on the percentage of green space to be included in the city which can create
comfortable environment even in the urban neighbourhoods which is now available in the large urban
open spaces such as parks alone. This further helps to reduce the UHI in the city which in reduce the
energy consumption in the buildings.
30th INTERNATIONAL PLEA CONFERENCE
16-18 December 2014, CEPT University, Ahmedabad
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6 REFERENCES
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10. Greenspace Factor, 2001, Malmö, Sweden. http://www.map21ltd.com/scan-green/bo01.htm
11. Green Factor, 2007, Seattle, US. http://www.seattle.gov/dpd/Permits/GreenFactor/
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in the Calculation of Green Plot Ratio, National Parks Board.
30th INTERNATIONAL PLEA CONFERENCE
16-18 December 2014, CEPT University, Ahmedabad
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