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 30th INTERNATIONAL PLEA CONFERENCE 16-18 December 2014, CEPT University, Ahmedabad 1
Transcript
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 signif icant 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
30th INTERNATIONAL PLEA CONFERENCE 16-18 December 2014, CEPT University, Ahmedabad
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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
quantif ied 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.
30th INTERNATIONAL PLEA CONFERENCE 16-18 December 2014, CEPT University, Ahmedabad
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Table 1. Green metrics and policies used around the world(15)
Category of
metric
Place Green metric Goals Description and
characteristics
Year
One
dimensional
Inventory of
plants
To increase the number
of plants
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
Two
dimensional
Berlin Biotope Area
factor
Retain high densities of development, whilst also developing the city’s green infrastructure
Attempts to account
for various types of
ecologically effective
or environmentally
friendly green systems
1994
Malmo Greenspace
Factor
Increase of green space per inhabitant from 33m
2 to 48m
2
in the urban area and increase the area of accessible green space in the countryside from 2% to 33%
Quantif ies the planting
area. Does not account
for vertical stacking.
2001
Portland Portland’s
Green
Building
Policy
All new City-owned facilities to include an eco-roof systems
Advancement in green roofs design to include an eco-roof with at least 70% coverage
2005
Vancouver Eco-Density Building green liveable and affordable Communities
Cities’ densification
systems
2006
California
California
Global
Warming
Solutions
Act
of 2006(5)
Reduce GHG emissions
in the state to1990
levels (25%) by 2020,
and 80%
below 1990 levels by
2050
2007
Seattle Green Factor New developments in commercially zoned areas must commit 30% of the parcel area to urban landscaping
Attempts to account
for various types of
ecologically effective
or environmentally
friendly green systems
2007
Three
dimensional
Singapore Green Plot
Ratio
Greening the buildings
in cities
Sum of total leaf area
developed in the s ite
divided by the s ite
area. Better correlation
with the
environmental
performance of
greenery
2003
30th INTERNATIONAL PLEA CONFERENCE 16-18 December 2014, CEPT University, Ahmedabad
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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%.
The impact of green spaces on the urban heat isalnd is more comprehensive if supported 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
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
30th INTERNATIONAL PLEA CONFERENCE 16-18 December 2014, CEPT University, Ahmedabad
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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
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 wind 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.
1
2
2
30th INTERNATIONAL PLEA CONFERENCE 16-18 December 2014, CEPT University, Ahmedabad
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X (m) 0 10 20 30 40
Y (
m)
0
10
20
30
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<Left foot> <Right foot>
case2 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 304.61 K
304.61 bis 304.80 K
304.80 bis 304.99 K
304.99 bis 305.17 K
305.17 bis 305.36 K
305.36 bis 305.55 K
305.55 bis 305.74 K
305.74 bis 305.93 K
305.93 bis 306.12 K
über 306.12 K
X (m) 0 10 20 30 40
Y (
m)
0
10
20
30
40
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basecase 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 305.13 K
305.13 bis 305.40 K
305.40 bis 305.66 K
305.66 bis 305.93 K
305.93 bis 306.20 K
306.20 bis 306.46 K
306.46 bis 306.73 K
306.73 bis 307.00 K
307.00 bis 307.27 K
über 307.27 K
X (m) 0 10 20 30 40
Y (
m)
0
10
20
30
40
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case1 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 304.96 K
304.96 bis 305.22 K
305.22 bis 305.49 K
305.49 bis 305.76 K
305.76 bis 306.02 K
306.02 bis 306.29 K
306.29 bis 306.55 K
306.55 bis 306.82 K
306.82 bis 307.09 K
über 307.09 K
X (m) 0 10 20 30 40
Y (
m)
0
10
20
30
40
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case5 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 304.75 K
304.75 bis 304.98 K
304.98 bis 305.21 K
305.21 bis 305.43 K
305.43 bis 305.66 K
305.66 bis 305.89 K
305.89 bis 306.11 K
306.11 bis 306.34 K
306.34 bis 306.57 K
über 306.57 K
X (m) 0 10 20 30 40
Y (
m)
0
10
20
30
40
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case2 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 304.61 K
304.61 bis 304.80 K
304.80 bis 304.99 K
304.99 bis 305.17 K
305.17 bis 305.36 K
305.36 bis 305.55 K
305.55 bis 305.74 K
305.74 bis 305.93 K
305.93 bis 306.12 K
über 306.12 K
X (m) 0 10 20 30 40
Y (
m)
0
10
20
30
40
<Left foot> <Right foot>
basecase 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 305.13 K
305.13 bis 305.40 K
305.40 bis 305.66 K
305.66 bis 305.93 K
305.93 bis 306.20 K
306.20 bis 306.46 K
306.46 bis 306.73 K
306.73 bis 307.00 K
307.00 bis 307.27 K
über 307.27 K
X (m) 0 10 20 30 40
Y (
m)
0
10
20
30
40
<Left foot> <Right foot>
case1 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 304.96 K
304.96 bis 305.22 K
305.22 bis 305.49 K
305.49 bis 305.76 K
305.76 bis 306.02 K
306.02 bis 306.29 K
306.29 bis 306.55 K
306.55 bis 306.82 K
306.82 bis 307.09 K
über 307.09 K
X (m) 0 10 20 30 40
Y (
m)
0
10
20
30
40
<Left foot> <Right foot>
case5 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 304.75 K
304.75 bis 304.98 K
304.98 bis 305.21 K
305.21 bis 305.43 K
305.43 bis 305.66 K
305.66 bis 305.89 K
305.89 bis 306.11 K
306.11 bis 306.34 K
306.34 bis 306.57 K
über 306.57 K
4.1 AVERAGE AIR TEMPERATURE
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
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.
Existing base case
Scenario 1
Scenario 2
Scenario 3
Figure 7 Air temperature for different scenarios for study area 1
Figure 6. The relationship between ENVI-met simulation temperature and onsite
measurement temperature (1 pm on 20th March 2014).
30th INTERNATIONAL PLEA CONFERENCE 16-18 December 2014, CEPT University, Ahmedabad
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X (m) 0 10 20 30 40 50
Y (
m)
0
10
20
30
40
50
<Left foot> <Right foot>
case1h3g3 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 308.25 K
308.25 bis 308.55 K
308.55 bis 308.85 K
308.85 bis 309.14 K
309.14 bis 309.44 K
309.44 bis 309.74 K
309.74 bis 310.03 K
310.03 bis 310.33 K
310.33 bis 310.63 K
über 310.63 K
X (m) 0 10 20 30 40 50
Y (
m)
0
10
20
30
40
50
<Left foot> <Right foot>
case1h3g3 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 308.25 K
308.25 bis 308.55 K
308.55 bis 308.85 K
308.85 bis 309.14 K
309.14 bis 309.44 K
309.44 bis 309.74 K
309.74 bis 310.03 K
310.03 bis 310.33 K
310.33 bis 310.63 K
über 310.63 K
X (m) 0 10 20 30 40 50
Y (
m)
0
10
20
30
40
50
<Left foot> <Right foot>
case1h3 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 308.39 K
308.39 bis 308.68 K
308.68 bis 308.96 K
308.96 bis 309.25 K
309.25 bis 309.54 K
309.54 bis 309.82 K
309.82 bis 310.11 K
310.11 bis 310.40 K
310.40 bis 310.68 K
über 310.68 K
X (m) 0 10 20 30 40 50
Y (
m)
0
10
20
30
40
50
<Left foot> <Right foot>
case1h3 13:00:00 20.03.2014x/y cut at z= 3
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Pot. Temperature
unter 308.39 K
308.39 bis 308.68 K
308.68 bis 308.96 K
308.96 bis 309.25 K
309.25 bis 309.54 K
309.54 bis 309.82 K
309.82 bis 310.11 K
310.11 bis 310.40 K
310.40 bis 310.68 K
über 310.68 K
X (m) 0 10 20 30 40 50
Y (
m)
0
10
20
30
40
50
<Left foot> <Right foot>
case1h3g2 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 308.12 K
308.12 bis 308.40 K
308.40 bis 308.68 K
308.68 bis 308.97 K
308.97 bis 309.25 K
309.25 bis 309.54 K
309.54 bis 309.82 K
309.82 bis 310.10 K
310.10 bis 310.39 K
über 310.39 K
X (m) 0 10 20 30 40 50
Y (
m)
0
10
20
30
40
50
<Left foot> <Right foot>
case1h3g2 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 308.12 K
308.12 bis 308.40 K
308.40 bis 308.68 K
308.68 bis 308.97 K
308.97 bis 309.25 K
309.25 bis 309.54 K
309.54 bis 309.82 K
309.82 bis 310.10 K
310.10 bis 310.39 K
über 310.39 K
X (m) 0 10 20 30 40 50
Y (
m)
0
10
20
30
40
50
<Left foot> <Right foot>
case1h3g1 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 308.01 K
308.01 bis 308.30 K
308.30 bis 308.58 K
308.58 bis 308.87 K
308.87 bis 309.15 K
309.15 bis 309.43 K
309.43 bis 309.72 K
309.72 bis 310.00 K
310.00 bis 310.29 K
über 310.29 K
X (m) 0 10 20 30 40 50
Y (
m)
0
10
20
30
40
50
<Left foot> <Right foot>
case1h3g1 13:00:00 20.03.2014x/y cut at z= 3
N
Pot. Temperature
unter 308.01 K
308.01 bis 308.30 K
308.30 bis 308.58 K
308.58 bis 308.87 K
308.87 bis 309.15 K
309.15 bis 309.43 K
309.43 bis 309.72 K
309.72 bis 310.00 K
310.00 bis 310.29 K
über 310.29 K
Existing
base case
Scenario 1
Scenario 2
Scenario 3
Figure 8 Air temperature for different scenarios for study area 2
4.2 APPLICATION OF GREEN SPACE FACTORS
Green plot ratio (GnPR) by Ong (2003) was known as an effective green assessment method to
determine the ratio of green space distribution. 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
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.
30th INTERNATIONAL PLEA CONFERENCE 16-18 December 2014, CEPT University, Ahmedabad
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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
Table 3.Green plot ratio conditions and ENVImet parametric results study area 2
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.
Existing Base
Case Recorded
with outdoor
weather
monitoring
station.
Existing
Base Case
Simulated
with
Envimet
Scenario 1 Scenario 2 Scenario 3
Total site area 12800 12800 12800 12800 12800
Total landscape
area
6000 6000 NIL 1950 3900
Trees 40 40 NIL NIL 25
Palms NIL NIL NIL NIL NIL
Shrubs NIL NIL NIL NIL NIL
Turfs NIL NIL NIL 1950 NIL
Green Plot Ratio 0.468 0.468 0 0.304 0.304
Average
Temperature
found byENVImet
32.67°C
32.87°C 34.02°C 33.84°C 33.32°C
Existing Base
Case Recorded
with outdoor
weather
monitoring
station.
Existing
Base Case
Simulated
with
Envimet
Scenario 1 Scenario 2 Scenario 3
Total site area 10000 10000 10000 10000 10000
Total landscape
area
1440 1440 NIL 2640 3600
Trees 6 6 NIL 11 15
Palms NIL NIL NIL NIL NIL
Shrubs NIL NIL NIL NIL NIL
Turfs NIL NIL NIL NIL NIL
Green Plot Ratio 0.144 0.144 0 0.264 0.36
Average
Temperature
found byENVImet
37.8°C
37.98°C 38.1°C 37.54°C 37.24°C
30th INTERNATIONAL PLEA CONFERENCE 16-18 December 2014, CEPT University, Ahmedabad
8
6 REFERENCES
1. 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 Journalof Climatology,
23(1), 1–26. http://dx.doi.org/10.1002/joc.859
2. Ali-Toudert F, Mayer H. 2007. Effects of asymmetry, galleries, overhanging façades and
vegetation on thermal comfort in urban street canyons. Solar Energy. 81:742-54.
3. Biotope Area Factor (BAF), 1994, Berlin, Germany.
