DETERMINANT FACTORS FOR THE INTENSITY OF URBAN HEAT ISLAND IN RESIDENTIAL AREAS IN RURAL ENVIRONMENT.
ISSN 2278 – 859X (Online)
Asian Academic Research Journal of Social Sciences & Humanities
&
ISSN 2319-2801 (Online)
Asian Academic Research Journal of Multidisciplinary
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Asian Academic Research Journal of Multi-Disciplinary
Year 2015, Volume-2, Issue-1 (June 2015)
Online ISSN : 2319 – 2801
INDEX PAGE
SNO
ARTICLE TITLE
PAGE NO
1.
A COMPREHENSIVE STUDY ON ANTIMICROBIAL EFFECT OF SOME MEDICINAL
PLANTS
FARHINA PASHA
1–7
2.
STUDY OF THE PERFORMANCE OF A PARALLEL VERTICAL JUNCTION SILICON
SOLAR CELL UNDER THERMAL INFLUENCE
NFALLY DIEME
8 – 16
3.
TRANSITION CAPACITANCE OF SILICON PN JUNCTION UNDER THERMAL INFLUENCE
NFALLY DIEME
17 – 23
4.
CEREBROSPINAL FLUID ADENOSINE DEAMINASE ACTIVITY IN PATIENTS WITH
TUBERCULOUS AND NON TUBERCULOUS MENINGITIS
N.RAJYA LAKSHMI ; JYOTSNA.V ; N.V.UMA LAKSMI
24 – 39
5.
DETERMINANT FACTORS FOR THE INTENSITY OF URBAN HEAT ISLAND IN
RESIDENTIAL AREAS IN RURAL ENVIRONMENT
ZUBER ANGKASA ; FACHRURROZIE SJARKOWI ; DADANG H PURNAMA;
DWI SETYAWAN ; NGAKAN PUTU SUECA
40 – 64
6.
CONCEPTUAL AND HISTORICAL FRAMEWORK OF CONTINUOUS AND
COMPREHENSIVE EVALUATION
DR. NIMISHA BERI
65 – 78
7.
EVIDENCE OF DELTAIC SYSTEME, TYPE COLORADO, IN THE MIDDLE TERME OF THE
PROTEROZOÏC BASIN OF SEMBE-OUESSO: NORTHWEST PART OF REPUBLIC OF
CONGO
MIYOUNA T; MALOUGUILA-NGANGA D; ESSOULI O; KINGAMOUZEO; KAYA F.; SOW
E. H. ; BOUDZOUMOU F
79 – 94
8.
PERFORMANCE OF WEANER PIGS FED VARYING LEVELS OF BREWERS YEAST
FERMENTED RICE MILLING WASTE (FRMW)
OBIH T.K.O
95 – 103
9.
MEASUREMENT OF INFECTIOUS DOSE FOR AFTOSA VIRUS ISOLATED FROM IRAQI
SHEEP BY INOCULATED THE CHORIO – ALLANTOIC MEMBRANE OF THE CHICK
EMBRYO
SABAA HILAL HADI
104 – 111
10.
CAUSED BY IMPROPER INADVERTENT THERMAL BURN USE OF ENERGY SOURCE
USED FOR COAGULATION OF BLOOD VESSELS DURING SURGERY
SUNIT KUMAR SINGH; ALOK TRIPATHI; DEEPAK MISHR
112 – 117
11.
INDUCTION OF THE GENES EXPRESSION OF ONION ENCODING FLAVONOIDS AND
ANTHOCYANINE BIOSYNTHETIC ENZYMES AS DEFENSE REACTION AGAINST
BOTRYTIS ACLADA INFECTION.
AHMED H.M. RAMADAN ; EL SHOBAKY AHMED; M. A. ABDEL-GAYED; SANAA. A.
KABEIL; EMAD EL-DEIN H. WASFY; ELSAYED E. HAFEZ
118 – 132
12.
UNIVERSITY-INDUSTRY COLLABORATION IN NIGERIA: ISSUES AND STRATEGIES
PETER ONWUALU ; ITOHAN OJEAGA
133 – 151
13.
BEHAVIOUR OF SOFT SUBGRADE SOIL WHEN STABILIZED WITH ALKALINE
SOLUTION AND REINFORCED WITH SISAL FIBER
MANIKANTA K V; JYOTHI SWARUP D; DR.SURESH BABU T
152 - 160
Asian Academic Research Journal of Multidisciplinary
www.asianacademicresearch.org
14.
COMPARATIVE PHOSPHATE-PHOSPHORUS RELEASE FROM ROCK PHOSPHATE AND
SUGARCANE PRESS MUD AT VARIOUS TEMPERATURES AND BUFFER CONTROLLED
PH LEVELS
KASHIFA NAGHMA WAHEED ; ZEHRA KHATOON; SIKENDER HAYAT; ZAID
MEHMOOD ; SAJID ALI NAQVI
161 – 174
15.
REPLACEMENT VALUE OF ENZYME SUPPLEMENTED MAIZE OFFAL FOR MAIZE ON
THE PERFORMANCE OF FINISHER BROILERS
T. K. O. OBIH
175 – 182
16.
PRODUCTION OF RHAMNOLIPIDS BY PSEUDOMONAS AERUGINOSA PAO1 FROM
FOOD WASTES: PURIFICATION, DETECTION AND PROTEOMIC ANALYSIS
SANAA SA KABIL ; ABDELAAL SHAMSELDIN; AMIRA SA HASSAN; WAEL A SABRA ;
SORAYA A SABRY
183 – 205
17.
MOLECULAR, SEROLOGICAL AND PATHOLOGICAL STUDY ON BROILER BREEDERS
AFFECTED WITH INFECTIOUS BRONCHITIS VIRUS
ALAA ABDUL AZIZ ABED
206 – 221
18.
CURRICULUM ON MEDICAL PROFESSIONALISM FOR UNDERGRADUATES.
SUNEEL. I. MAJAGI; ARA TEKIAN
222 – 243
19.
PEDIATRIC PULMONARY HYPOPLASIA WITH HORSE SHOE KIDNEY – A CASE
REPORT.
B B SHARMA
244 – 250
20.
PET-CT IMAGING - HOW TO RESOLVE THE RADIATION PROTECTION ISSUE?
SHILPA SINGH ; MONU SARIN ; B B SHARMA ; SARITA JILOWA ; YASHVANT SINGH
251 – 256
21.
RADIOACTIVE NUCLIDES AND CONTAMINATION IN FOODSTUFFS
ASIA H. AL-MASHHADANI1; ABDUL-JABBAR ABBAS OUDH
257 – 270
22.
STUDY ON FUNCTIONAL PROPERTIES OF REGENERATED BAMBOO AND ERI SILK
BLENDED FABRIC
DR. SMITARANI SAIKIA ; DR. BINITA BAISHYA KALITA
271 – 277
23.
RHIZOTROGINI LARVAE OF NORTH AFRICA (COLEOPTERA SCRABAEIDAE,
MELOLONTHINAE). UTILIZATION OF SILKS ANAL ECUSSION FORM TO THE
DETERMINATION OF SPECIES AND COMPARISON BETWEEN MORPHOLOGICAL
PARAMETERS IN LARVAE’S STAGES IN THE REGION OF CONSTANTINE-ALGERIA
MADACI BRAHIM; CHENOUF FADILA, DERRADJ LOTFI
278 – 293
24.
SEQUENCING BATCH REACTOR FOR TREATING PHARMACEUTICAL EFFLUENTS
SAREDDY RAVI SANKARA REDDY ;VARA SARITHA ;BHAVYA KAVITHA
DWARAPUREDDI
294 – 304
25.
SHERAZ SCHOOL OF MINIATURE
MARIA ANSARI; FARJAD FAIZ; AMNA ANSARI
305 – 314
26.
ADAPTIVE NETWORK DESIGN PARAMETERS FOR EFFICIENT SYSTEM DESIGN
L.SUDHA; DR.P.THANGARAJ
315 – 325
27.
WOMEN AND WORKING OF CONSTITUTIONAL PROVISIONS IN INDIA: A CRITICAL
LEGAL STUDY
HARI SHANKAR PANDA
326 – 342
28.
EVALUATION GEOTECHNICAL FOR SITE COMMERCIAL COMPLEX BUILDING
PROJECT IN AL-SAMAWA DISTRICT , AL- MUTHANNA GOVERNORATE
PROF. DR.MOHSEN ABD ALI ; DR. KADHIM NAIEF AL-TAAE ; MOHANAD MOUSA ALFARHAN ; PROF. DR.AMER ATYAH LAFTAH
343 – 368
29.
CLINICAL EVALUATION OF MASHABALADI KWATH IN THE MANAGEMENT OF ARDITA
(FACIAL PARALYSIS)
DR. SWAPNIL S. SINGHAI
369 – 379
Asian Academic Research Journal of Multidisciplinary
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A Peer Reviewed International Journal of Asian
Academic Research Associates
AARJMD
ASIAN ACADEMIC RESEARCH
JOURNAL OF MULTIDISCIPLINARY
DETERMINANT FACTORS FOR THE INTENSITY OF URBAN HEAT ISLAND IN
RESIDENTIAL AREAS IN RURAL ENVIRONMENT
ZUBER ANGKASA1; FACHRURROZIE SJARKOWI2; DADANG H PURNAMA3;
DWI SETYAWAN4; NGAKAN PUTU SUECA5
1
Ph.D Student of Environmental Science, Sriwijaya University, Indonesia
2
Department of Environmental Science, Sriwijaya University, Indonesia
3
Department of Environmental Science, Sriwijaya University, Indonesia
4
Department of Environmental Science, Sriwijaya University, Indonesia
5
Department of Architecture, Udayana University, Denpasar , Indonesia.
Abstract
There is a potential adverse impact of housing construction in rural areas that arise due to the
increased heat of the housing population trends to produce various kinds of heat. This article
focuses on the roughness of the roof, energy density, and house height home that
may contribute to the development of the Urban heat island (UHI). Researchers compared the
variation of UHI on 125 homes in a residential village in South Sumatra. By using multiple
linear regression, researchers found indications that the highest UHI intensity arise when the
house have slippery roofs, tall, small ratio of green to built area, and a large home electricity
consumption. Natural factors also become important determinants, especially the weather.
Based on these findings, a number of policies to prevent heat buildup in housing outside the
city recommended.
Keywords: energy density, green to built ratio, roof roughness, rural housing, urban heat
island
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1. Introduction
This article aims to analyze the factors that determine Urban Heat Island (UHI) in
housing in the area outside the city in a tropical country. Additional purpose, which is
essentially drawn from the research findings, is to providethe right policy that could allow
local governments to develop more environmentally friendly housing.
UHI is the most important features of urban climate (Blazejczyk et al., 2012). UHI and its effects - in urban areas has become an important issue, particularly in developing
countries after the issue of global warming. It
reduces the community ability to live
comfortably in the environment for their activities. UHI has the potential to generate
discomfort during activity that would require additional electrical energy for cooling the
room and increase the comfort (Valsson and Bharat, 2009), especially in the tropics (Fischer
and Schär, 2010). In addition, the increase in urban temperatures can cause human becoming
increasingly aggressive and temperamental. This triggers an increase in the crime of murder
and violence. Itis estimated that there were 4.58 cases per hundred thousand people for
increase of 1oF (United States Department of Energy, 2009; Anderson et al., 1997). The study
also found that the UHI accounted for 2-4% of global warming (Jacobson and Ten Hoeve,
2011). Because of the magnitude of the problem that it can caused, perhaps surprising that
few studies examine the factors that influence the UHI in urban areas in Indonesia
(Tursilowati, 2009; Hidayati, 1990; Karyono, 1995). Fewer still focuses exclusively on
residential areas as micro urban in the rural environment, or how rural housing increasing
temperature through its presence in the middle of rural area. This article contributes to the
literature by analyzing the determinants
of
UHI in
rural
residential
areas
in
Palembang. Analysis done by comparing the houses are close together and be part of the
same housing and measurable having the UHI effect is significantly higher than other
housing in the region. This situation results from the residential areas for long times and high
variation on the residents who live in it. The implications of the housing area in
the UHI context explored further in section 2. Section 3 of this article then concentrate on
theoretical concepts and review of the literature. The author developed a theoretical
framework, in particular from the pioneering work of Zhao et al (2014). The author
then review the literature to build a concrete hypothesis that will tested, with a focus on the
types of factors that explains the UHI. In section 4, the researchers present
a methodology to determine the factors that explain the UHI in the study sample, followed by
analysis
of
the
data
sets
obtained
from Taman Sari Kenten I,
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Data collected through field observations and interviews with residents in February 2013.
Field observations provide details on the volume of home, type of roof used, the current
weather measurement, and the ratio of green to built areas of the house. The interview
provides data on population size of the household.
It is quite difficult to create the perfect measurement of UHI with only measure the
temperature in the housing area. The dependent variable in this study is the increase of air
temperature becomes compared to the surrounding area (Unger et al., 2001). This definition
is essentially for a city. Even so, there is no obstacle when it to apply to the housing sphere in
rural areas. This is due to the housing has characteristics similar to the city. It structurally
organized, population density that is larger than the rural areas, and generally have a variety
of self-support system. UHI measurements done by creating a temperature difference
between the temperature measured in the house individually with the temperature in the
reference point. The temperature at the reference point outside the city are at Indonesian
Agency for Meteorological, Climatological and Geophysics station in Kenten within about 2
km from the housing. One disadvantage of this method is the need for an approach to match
data because the temperature of the reference point is only available in four periods of time
while the temperature at the measuring point is available in real-time resolution. However,
assuming no extreme weather changes, this approach has been able to reflect the UHI in the
area under study.
The results of the study presented in section 5 with the aim of helping to understand
the important aspects of theUHI in a residential area in the outer region of the city.
Researcher initially determining factors from five sources of heat (radiation, convection,
evaporation, storage, and anthropogenic) before examining this relationship in the model of
linear regression analysis. The findings suggest two factors strongly associated with UHI: the
surface roughness of the roof and the home population. There are also indications that the
volume of the house is also important in explaining the UHI in residential areas. The findings
are then described in section 6 of the policy implications of local government to
the UHI mitigation efforts, particularly in the context of developing residential areas outside
the city. Section 7 providing conclusion of the study.
2. Development of Settlements in the Outer City Area
Urban Heat Island in Indonesia is increasingly widespread. Tursilowati (2009) stated
that some of the major cities in Indonesia increased air temperature. Expansion of UHI (areas
with high temperature 30-35oC located in the built areas in the city center), in Bandung,
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around 12 606 ha or 4.47% per year, in Semarang 12 174 ha or 8.4% per year, in Surabaya
1,512 ha or 4.8% per year. In 1990, the air temperature Jakarta City detected 0,02-1oC higher
than the sub-urban areas, (Hidayati, 1990; Karyono, 1995).
UHI expansion due to urbanization accompanied with change in various native
landforms with urban land (Lo and Quattrochi, 2003), increasing the density structure (Wong
et al., 2010), and housing and infrastructure (Upchurch, 2005). The areas most vulnerable
to UHI is growing cities and suburban areas (urban fringe). According to Ernawi (2010),
cities suburban area in Indonesia spread and packed even beyond the boundaries of the
administrative area of the city. Villages quickly became the center of a new urban activities.
Land that was originally in the form of open land, swamp, or forest land converted into solid
building resulting in a change in land use, demographic, and ecological balance (Yoyok et al.,
1997).
From all the cities in the world, new housing on the outskirts of the city estimated to
20-30% (Robinson, 2005). Approximately 8-19% of the population major cities in Indonesia
live in housing (Hoek-Smit, 2005). Housing is an area which has its own environmental and
sociological characteristics, generally do not bring a sense of ownership and personal
identity, do not have clear boundaries of personal and public life, and have a weak
relationship with the open space (Woehr, 2007). It is increasingly seen in developing
countries, such as Indonesia, that the high-quality housing is quite expensive (Bloom and
Khanna, 2007). The size of housing in major cities getting bigger, but the distance between
house increasingly narrow. It resulted in a narrowing of the space and replacing natural
spaces into artificial space (Upchurch, 2005).
Palembang is a city that is growing rapidly from a swamp land. Febriana (2008) wrote
that Palembang which has an area of 400.610 km2 originally consisted of 54% of the swamp
area. In 1999, the swamp area becomes 30.35%. The swamp land area continued to decline
until in 2007 the only remaining 15.3%. Recent data in Palembang Urban Land Use Planning
2010-2020 show that wetlands in Palembang only remaining 5835.19 ha or about14.58%.
That is, in the period 1999-2010, Palembang has lost swamp of 5713.60 ha
or approximately 14.26%.
Although Palembang increasingly crowded with buildings and the land conversion,
studies to find the effects of the heat generated from such a situation has not been addressed
until now. This situation if left unchecked will have negative effects for the people of
Palembang which grew by 1.82% per year, especially when the economic growth sector of
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the building continued rising 7.12% in 2009, 8.03% in 2010, and 12.92% in 2011 (Juliantina,
2012). Therefore, this research is very important to give least effect for the people of
Palembang.
3. Theoretical Literature and Hypotheses
Theoretical foundation of this research is mainly related to the results of Zhao et al
(2014) study. Some reviews from other literature also added, with a focus on factors that
developed from Zhao et al (2014).
3.1.
Theoretical Concept and UHI Components
Core discussion about the factors that influence the UHI assumed that the green area
is the main factor that lowers the UHI (Taha, 1997; Peng et al., 2012). However, this
assumption questioned by Zhao et al (2014). Zhao et al (2014) conducted a study on a
number of cities in Canada and the United States which spread across three climatic regions:
cold, temperate, and dry climate. If the hypothesis holds true, UHI in cold and dry climate
will be very high because almost no trees in this area. However, their study actually found the
opposite. UHI in cold and dry climates are even lower than in temperate region.
The main explanation to this finding is actually not plants that reduces the UHI, but
the roughness factor. In the temperate region, rural areas are more rugged than urban areas.
As a result, UHI is higher. Conversely, in a dry and cool area, it is urban which more rugged
than rural. UHI in this area can even negative. Green land apparently lowering UHI because
trees and various types of plants have a high roughness. In the temperate region and tropical
region, rural areas have far more trees than the city. Consequently, the resulting effect is
high UHI in town that does not have a tree. But in the cold and dry climate, rural areas are
very smooth. There are no trees and there is only desert or snow. Area of the city, although
also devoid of trees, but have buildings that increase the surface roughness. As a result there
is a negative impact on the heat in urban areas in the climate.
UHI equation developed in the simulation by Zhao et al (2014) using the method of
Lee et al (2011) is a non-linear equation. The linear form of the equation is as follows:
(1)
Where T is the temperature, C is the contribution, R is the radiation, H is convection, LE is
evaporation, S is storage, AH is anthropogenic heat, and e is the error rate arising from the
nonlinear form transformation. Simply put, this equation states that the UHI is the result of
the amount of heat by radiation, convection, evaporation, heat savings, and anthropogenic
heat contribution. In his study, Zhao et al (2014) using a CLM (Oleson, 2010) found that the
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heat convection is higher than the other heat in contributing to the UHI, and convection heat
donated by the surface roughness. Green land itself contributes heat through evaporation and
its contribution is smaller than the heat storage and heat radiation. Anthropogenic heat give
smallest contribution.
3.2.
Contributors Variables for Each UHI Component
The first part in equation (1) is the radiation component. In the form of non-linear, the
radiation component of UHI expressed through the following equation:
(2)
Where λ0 is the local climate sensitivity, expressed through the equation
(3)
Where ε is the surface emissivity, σ is the Stefan-Boltzmann constant, and T is the surface
temperature.
f is the energy redistribution factor, expressed through the equation
(4)
Where ρ is air density, Cp is the specific heat of air at constant pressure, ra is the aerodynamic
resistance to heat diffusion, expressed through the equation
(5)
Where Ta is the temperature at the reference height and H is the sensible heat flux, and β is
the Bowen ratio, which is expressed through the equation
(6)
Where LE is the latent heat flux.
R*n is the apparent net radiation, expressed through the equation
(7)
Where
is the albedo,
is the incoming solar radiation, and
is a long-wave radiation
coming.
In the above equation, ra, β, and R*n are parameters that come from human socio-technical
nature. The main component of the equation (2) is R*n because this term gives the
characteristics of the interaction between the material and the coming radiation. This
term determined by the albedo and emissivity of the material. Albedo has long been
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considered essential for UHI mitigation (Akbari et al., 2009). Even so, it determined again by
the sum of the radiation coming from the sun. In turn, this radiation determined by
meteorological factors, especially weather (Rong, 2006). On cloudy days, the radiation
coming less and therefore reduce the surface heating. Hence, UHI typically higher under
clear skies (Gosling et al., 2014). Furthermore, the weather effect is stronger than the surface
properties in developing UHI (Rosenzweig et al., 2006). Weather therefore, determinehow
much radiation coming from the sun to then interact with the material and apparent net
radiation. Based on these considerations, it can formulated the following hypothesis:
Hypothesis 1: The weather is negatively related to UHI intensity.
The second term in equation (1) is the rate that describes heat convection. This heat is
rarely studied in the earlier UHI studies (Arnfield, 2003). In non-linear, the second term
expressed as follows:
(8)
Where QS is the heat generated by the storage, QAH is the heat generated by anthropogenic
source, and Δf1 is the difference in energy redistribution factor, which is expressed through
the equation:
(9)
Δf1 is an important term in this equation because it contains the aerodynamic resistance to
heat diffusion. This term is the sum of the characteristics of the surface layer of the
atmosphere and the characteristics of the material at the lower limit. It determines the
material's ability to inhibit heat diffusion. Smooth material less able to inhibit heat diffusion,
and unable to prevent heat buildup.
Although it has elements of atmospheric, aerodynamic resistance can vary more
because of the materials used. In earlier studies using MODIS, this factor was not considered
because of methodological difficulties (Marpaung, 2009). Tursilowati (2007) see more
aerodynamic resistance to meteorological element to declare it as wind speed function. Even
so, should it be viewed as a factor determined by the roughness of roofing materials
that determinethe behavior of the wind as an agent convection (Voogt and Grimmond, 2000).
In addition, studies Zhao et al (2014) found that the convection component is the
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largest component of UHI. Therefore, a more rational put roughness characteristics of
materials as a major factor in determining the UHI.
Hypothesis 2: Surface roughness of roof coverings negatively related to the intensity of the
UHI.
The third term of equation (1) is a heat rate of evaporation. It formulated by the
following non-linear:
(10)
Where
(11)
This term called evaporative heat rate for Bowen ratio (β) which reduced due to more
radiation energy channeled through the surface latent heat flux (Zhao et al., 2014), especially
during post-rain. Traditionally, this is the most powerful heat effect due to the denser
vegetation in wetlands will have a high evaporation rate compared to other materials.
Evaporation can come from soil or cover material from the blue land such as ponds and pools
(Golden, 2004). However, the main factor is the vegetation (Brazel et al., 2009). In addition
to providing an increased rate of evaporation, the vegetation is also useful to prevent
radiation coming to the surface so it is also relevant to the radiation rate (Valsson and Bharat,
2009). However, this research carried out in the context of the housing with the rest of the
land is very limited. In contrast to urban areas which can grow large trees, mainly for
sidewalks shade and parks, housing does not have this ability. As a result, the green land area
will be very little and negligible to the UHI. Because the author conducted a study on
housing, the author suspect the absencerelationship between the ratio of green areas to the
built areas.
Hypothesis 3: The ratio of green to built areas not related to the intensity of the UHI
The fourth term is the rate of heat storage equation. Non-linear equation for storage
rate is as follows:
(12)
Heat storage material associated with the ability to absorb energy. In urban areas, the
storage of heat coming from the broad opaque area and narrow view of the sky (Jahan, 2013).
This heat stored in buildings and the roof and walls (Hamdi and Schayes, 2008). In addition,
the soil surface such as asphalt and cement contributes to the heat storage (Connors, Galletti,
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and Chow, 2013). Of a number of factors, roads and buildings is the strongest (Dodman,
2009). This study conducted in a residential neighborhood where the main factor is
the building. Building factor in this case is high because of the housing tends to have the
same size in length x width dimensions but not the height dimension. The higher the building,
the greater the heat storage ability of the building. Therefore, the author suspect that a large
building will show greater intensity of the UHI.
Hypothesis 4: House height is positively related to the intensity of the UHI
Last term associated with anthropogenic heat. The underlying equation is:
(13)
The equation shows that in general the contribution of anthropogenic heat to the UHI is
linear, straight from the magnitude of the anthropogenic heat.
Anthropogenic heat comes from human activities such as energy use, Air
Conditioning, and respiratory (van Ooststroom, 2011). In addition, motorized vehicle can
also be viewed as a source of anthropogenic heat (Chow and Roth, 2006). Nelson (2011) uses
the energy density, which defined as the ratio of energy consumption to the building area. In
three dimensions, it can expressed as the ratio of the energy consumption to the building
volume. This makes sense because the energy density reflects the amount of energy used in a
home by humans. This makes the researcher suggest that there is a relationship between
energy density and intensity of the UHI.
Hypothesis 5: Energy density is positively related to the intensity of the UHI
4. Methodology
This analysis based on survey results in Taman Sari Kenten I, outside Palembang. The
coordinates of the location is
2°54'4.46"S and 104°46'4.95"E. This settlement chosen
because it reflects high UHI profile than the two other housing included in the first survey:
Talang Kelapo (2°56'31.27"S; 104°41'17.38"E) and Taman Ogan Permai Jakabaring (3°
1'36.98"S; 104°46'42.05"E). The residential site are also closer to rural areas than two other
housing.
Information gathered through field measurements, observations, and interviews with
residents. Samples taken covering the house in Taman Sari Kenten include 231 homes. By
removing the house that has not been occupied and not allow to collect the data, the total
house successfully surveyed include 125 homes.
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Researcher first peforming temperature measurements at three meters from the front
door of the house and a height of 2 meters above ground level. This would characterie the
CLUHI (Canopy-Layer UHI) (Martin et al., 2014). After the measurement, the researcher
collect the dimensions of the house, the weather measurement, identify the type of roof
covering, and the area of green areas and the house. Researcher asked residents about the
number of occupants in the house and monthly energy consumption on average.
The total sample of 125 homes have enough adequacy for regression using the effect
size of 0.20; alpha 0.05; power 0.95; and the number of predictors 5 using G * Power 3.0.10
software.
Table 1 presents the descriptive statistics related to the study variables.
Majority homes (78.4%) have only one level. This measure reflects the average house height
in similar housing. Looking at this size, the ratio of green to built quite surprising: half
(55.2%) of the house has a ratio of 0 which means it has no green space at all.
Type of roof covering measured in this study reflects asbestos cover. Actually there
are four types of roofing inTaman Sari Kenten: asbestos, tile, multiroof, and zinc. However,
observations show that asbestos have different roughness of the other three types of roof
coverings. Asbestos, even if thin, has a rougher surface than tile,multiroof, and zinc. It shown
from moss inability to cover the asbestos roof. Tile, multiroof, and zinc, have a waving
pattern and the same shape. All three wave-shaped and has a high surface smoothness. On the
old tile roof, moss can stick because basically the raw material of this roof is ground and this
adds to the smoothness of the surface. At the time of rain, dirt in the tile, multiroof, and
zinc can be immediately washed and updated surface becomes even more slippery than
before. This does not apply to asbestos which has more micro roughness, although that also
have a wavy pattern. Therefore, the researcher decided asbestos as the most rugged roof.
25.4% of homes have asbestos cover.
Most of the houses occupied by 5 people (32.8%). There are 3 houses inhabited by
seven people and six houses inhabited by only two people. Energy units in this study used
rupiahs because respondents more easily stated their energy use based on the average
monthly payment for their electricity consumption. In the total population, the average energy
density is Rp 2.3889 /m2.level.
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Table 1. Descriptive Statistics
Mean
Std. Deviation Number of Positive
Dependent Variable
UHI Intensity
4.1257 2.03903
Independent Variables
House height
20(14.9%)
Asbestos Roof
34 (25.4%)
Weather
4.1.
Clear
121 (90,3%)
Partially Clouded
1 (0,7%)
Heavily Clouded
3 (2,2%)
Green to Built ratio
0.1414 0.37336
Energy Density
2.3889 1.60430
Operationalization of Variables
The main strength of this study is perform a direct measurement of the
study location and carried out by providing clear and micro boundaries. The dependent
variable is the UHI intensity expressed as the difference between the temperature of the
measurement site with a temperature at the reference point in rural area provided by
Indonesian Agency for Meteorological, Climatological and Geophysics Station.
Table 2 describes the key variables tested and the sign hypothesized about their
relationship with the UHIintensity (analysis results are also summarized in this table);
relationships tested using multiple linear regression model. Correlation analysis showed that
there is no multicollinearity between the independent variables of the study. The independent
variables consist of five groups. The first is the weather. The weather coded 1 for sunny
weather, 2 to cloudy, and 3 to overcast. The weather is known by observation simultaneously
with temperature measurement. The second is the roof surface roughness. Asbestos
surface coded 1 and non-asbestos coded 0. This roof surface roughness indicator is known by
observation when the temperature measurement. Third is the ratio of green space to
built space. The ratio calculated by dividing the land area covered by extensive plant and the
built environment such as house, roads, and ceramics or cement covered veranda. Fourth is
house height. Building with one level codenamed 1 and two-story building coded 2. These
variables are known directly through observation in the time of measurement. Fifth is the
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energy density. Energy density measured by dividing the monthly electricity consumption by
the volume of the house (length x width x height).
Table 2. Variables Description and Summary of the Research
Variables
Type
Description
Relatio
Sign
n
Dependent
C*
UHI Intensity
D**
1 = sunny; 2 =
Significanc
e
Variable
Weather (H1)
-
Confirmed
cloudy; 3 =
Not
Confirmed
overcast
Roof roughness
D
(H2)
Green to Built
1 = asbestos; 0 =
-
Confirmed
tile, multiroof, zinc
C
Ratio (H3)
The ratio of the
Not
Confirmed
-
Confirmed
green area to the
Not
Confirmed
built area
House Height
D
(H4)
Energy Density
(H5)
1= one story; 2 =
+
Confirmed
Confirmed
+
Confirmed
Confirmed
two story
C
The amount of
energy consumed
per one m2.story
Note: * = continuous variable; ** = discrete variable
5. Results, Findings, and Discussion
The results of the four models to explain UHI intensity presented in Table 3.
Researchers using stepwise selection method. Variables entered in line with the findings
of Zhao et al (2014) on the level of influence of the factors of the UHI. The regression model
showed statistical evidence of a relationship between UHI intensity and the array of factors
that presented before by the theory. Only the first model significant at the level of p
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Asian Academic Research Journal of Multi-Disciplinary
Year 2015, Volume-2, Issue-1 (June 2015)
Online ISSN : 2319 – 2801
INDEX PAGE
SNO
ARTICLE TITLE
PAGE NO
1.
A COMPREHENSIVE STUDY ON ANTIMICROBIAL EFFECT OF SOME MEDICINAL
PLANTS
FARHINA PASHA
1–7
2.
STUDY OF THE PERFORMANCE OF A PARALLEL VERTICAL JUNCTION SILICON
SOLAR CELL UNDER THERMAL INFLUENCE
NFALLY DIEME
8 – 16
3.
TRANSITION CAPACITANCE OF SILICON PN JUNCTION UNDER THERMAL INFLUENCE
NFALLY DIEME
17 – 23
4.
CEREBROSPINAL FLUID ADENOSINE DEAMINASE ACTIVITY IN PATIENTS WITH
TUBERCULOUS AND NON TUBERCULOUS MENINGITIS
N.RAJYA LAKSHMI ; JYOTSNA.V ; N.V.UMA LAKSMI
24 – 39
5.
DETERMINANT FACTORS FOR THE INTENSITY OF URBAN HEAT ISLAND IN
RESIDENTIAL AREAS IN RURAL ENVIRONMENT
ZUBER ANGKASA ; FACHRURROZIE SJARKOWI ; DADANG H PURNAMA;
DWI SETYAWAN ; NGAKAN PUTU SUECA
40 – 64
6.
CONCEPTUAL AND HISTORICAL FRAMEWORK OF CONTINUOUS AND
COMPREHENSIVE EVALUATION
DR. NIMISHA BERI
65 – 78
7.
EVIDENCE OF DELTAIC SYSTEME, TYPE COLORADO, IN THE MIDDLE TERME OF THE
PROTEROZOÏC BASIN OF SEMBE-OUESSO: NORTHWEST PART OF REPUBLIC OF
CONGO
MIYOUNA T; MALOUGUILA-NGANGA D; ESSOULI O; KINGAMOUZEO; KAYA F.; SOW
E. H. ; BOUDZOUMOU F
79 – 94
8.
PERFORMANCE OF WEANER PIGS FED VARYING LEVELS OF BREWERS YEAST
FERMENTED RICE MILLING WASTE (FRMW)
OBIH T.K.O
95 – 103
9.
MEASUREMENT OF INFECTIOUS DOSE FOR AFTOSA VIRUS ISOLATED FROM IRAQI
SHEEP BY INOCULATED THE CHORIO – ALLANTOIC MEMBRANE OF THE CHICK
EMBRYO
SABAA HILAL HADI
104 – 111
10.
CAUSED BY IMPROPER INADVERTENT THERMAL BURN USE OF ENERGY SOURCE
USED FOR COAGULATION OF BLOOD VESSELS DURING SURGERY
SUNIT KUMAR SINGH; ALOK TRIPATHI; DEEPAK MISHR
112 – 117
11.
INDUCTION OF THE GENES EXPRESSION OF ONION ENCODING FLAVONOIDS AND
ANTHOCYANINE BIOSYNTHETIC ENZYMES AS DEFENSE REACTION AGAINST
BOTRYTIS ACLADA INFECTION.
AHMED H.M. RAMADAN ; EL SHOBAKY AHMED; M. A. ABDEL-GAYED; SANAA. A.
KABEIL; EMAD EL-DEIN H. WASFY; ELSAYED E. HAFEZ
118 – 132
12.
UNIVERSITY-INDUSTRY COLLABORATION IN NIGERIA: ISSUES AND STRATEGIES
PETER ONWUALU ; ITOHAN OJEAGA
133 – 151
13.
BEHAVIOUR OF SOFT SUBGRADE SOIL WHEN STABILIZED WITH ALKALINE
SOLUTION AND REINFORCED WITH SISAL FIBER
MANIKANTA K V; JYOTHI SWARUP D; DR.SURESH BABU T
152 - 160
Asian Academic Research Journal of Multidisciplinary
www.asianacademicresearch.org
14.
COMPARATIVE PHOSPHATE-PHOSPHORUS RELEASE FROM ROCK PHOSPHATE AND
SUGARCANE PRESS MUD AT VARIOUS TEMPERATURES AND BUFFER CONTROLLED
PH LEVELS
KASHIFA NAGHMA WAHEED ; ZEHRA KHATOON; SIKENDER HAYAT; ZAID
MEHMOOD ; SAJID ALI NAQVI
161 – 174
15.
REPLACEMENT VALUE OF ENZYME SUPPLEMENTED MAIZE OFFAL FOR MAIZE ON
THE PERFORMANCE OF FINISHER BROILERS
T. K. O. OBIH
175 – 182
16.
PRODUCTION OF RHAMNOLIPIDS BY PSEUDOMONAS AERUGINOSA PAO1 FROM
FOOD WASTES: PURIFICATION, DETECTION AND PROTEOMIC ANALYSIS
SANAA SA KABIL ; ABDELAAL SHAMSELDIN; AMIRA SA HASSAN; WAEL A SABRA ;
SORAYA A SABRY
183 – 205
17.
MOLECULAR, SEROLOGICAL AND PATHOLOGICAL STUDY ON BROILER BREEDERS
AFFECTED WITH INFECTIOUS BRONCHITIS VIRUS
ALAA ABDUL AZIZ ABED
206 – 221
18.
CURRICULUM ON MEDICAL PROFESSIONALISM FOR UNDERGRADUATES.
SUNEEL. I. MAJAGI; ARA TEKIAN
222 – 243
19.
PEDIATRIC PULMONARY HYPOPLASIA WITH HORSE SHOE KIDNEY – A CASE
REPORT.
B B SHARMA
244 – 250
20.
PET-CT IMAGING - HOW TO RESOLVE THE RADIATION PROTECTION ISSUE?
SHILPA SINGH ; MONU SARIN ; B B SHARMA ; SARITA JILOWA ; YASHVANT SINGH
251 – 256
21.
RADIOACTIVE NUCLIDES AND CONTAMINATION IN FOODSTUFFS
ASIA H. AL-MASHHADANI1; ABDUL-JABBAR ABBAS OUDH
257 – 270
22.
STUDY ON FUNCTIONAL PROPERTIES OF REGENERATED BAMBOO AND ERI SILK
BLENDED FABRIC
DR. SMITARANI SAIKIA ; DR. BINITA BAISHYA KALITA
271 – 277
23.
RHIZOTROGINI LARVAE OF NORTH AFRICA (COLEOPTERA SCRABAEIDAE,
MELOLONTHINAE). UTILIZATION OF SILKS ANAL ECUSSION FORM TO THE
DETERMINATION OF SPECIES AND COMPARISON BETWEEN MORPHOLOGICAL
PARAMETERS IN LARVAE’S STAGES IN THE REGION OF CONSTANTINE-ALGERIA
MADACI BRAHIM; CHENOUF FADILA, DERRADJ LOTFI
278 – 293
24.
SEQUENCING BATCH REACTOR FOR TREATING PHARMACEUTICAL EFFLUENTS
SAREDDY RAVI SANKARA REDDY ;VARA SARITHA ;BHAVYA KAVITHA
DWARAPUREDDI
294 – 304
25.
SHERAZ SCHOOL OF MINIATURE
MARIA ANSARI; FARJAD FAIZ; AMNA ANSARI
305 – 314
26.
ADAPTIVE NETWORK DESIGN PARAMETERS FOR EFFICIENT SYSTEM DESIGN
L.SUDHA; DR.P.THANGARAJ
315 – 325
27.
WOMEN AND WORKING OF CONSTITUTIONAL PROVISIONS IN INDIA: A CRITICAL
LEGAL STUDY
HARI SHANKAR PANDA
326 – 342
28.
EVALUATION GEOTECHNICAL FOR SITE COMMERCIAL COMPLEX BUILDING
PROJECT IN AL-SAMAWA DISTRICT , AL- MUTHANNA GOVERNORATE
PROF. DR.MOHSEN ABD ALI ; DR. KADHIM NAIEF AL-TAAE ; MOHANAD MOUSA ALFARHAN ; PROF. DR.AMER ATYAH LAFTAH
343 – 368
29.
CLINICAL EVALUATION OF MASHABALADI KWATH IN THE MANAGEMENT OF ARDITA
(FACIAL PARALYSIS)
DR. SWAPNIL S. SINGHAI
369 – 379
Asian Academic Research Journal of Multidisciplinary
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AARJMD
VOLUME 2
ISSUE 1
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A Peer Reviewed International Journal of Asian
Academic Research Associates
AARJMD
ASIAN ACADEMIC RESEARCH
JOURNAL OF MULTIDISCIPLINARY
DETERMINANT FACTORS FOR THE INTENSITY OF URBAN HEAT ISLAND IN
RESIDENTIAL AREAS IN RURAL ENVIRONMENT
ZUBER ANGKASA1; FACHRURROZIE SJARKOWI2; DADANG H PURNAMA3;
DWI SETYAWAN4; NGAKAN PUTU SUECA5
1
Ph.D Student of Environmental Science, Sriwijaya University, Indonesia
2
Department of Environmental Science, Sriwijaya University, Indonesia
3
Department of Environmental Science, Sriwijaya University, Indonesia
4
Department of Environmental Science, Sriwijaya University, Indonesia
5
Department of Architecture, Udayana University, Denpasar , Indonesia.
Abstract
There is a potential adverse impact of housing construction in rural areas that arise due to the
increased heat of the housing population trends to produce various kinds of heat. This article
focuses on the roughness of the roof, energy density, and house height home that
may contribute to the development of the Urban heat island (UHI). Researchers compared the
variation of UHI on 125 homes in a residential village in South Sumatra. By using multiple
linear regression, researchers found indications that the highest UHI intensity arise when the
house have slippery roofs, tall, small ratio of green to built area, and a large home electricity
consumption. Natural factors also become important determinants, especially the weather.
Based on these findings, a number of policies to prevent heat buildup in housing outside the
city recommended.
Keywords: energy density, green to built ratio, roof roughness, rural housing, urban heat
island
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1. Introduction
This article aims to analyze the factors that determine Urban Heat Island (UHI) in
housing in the area outside the city in a tropical country. Additional purpose, which is
essentially drawn from the research findings, is to providethe right policy that could allow
local governments to develop more environmentally friendly housing.
UHI is the most important features of urban climate (Blazejczyk et al., 2012). UHI and its effects - in urban areas has become an important issue, particularly in developing
countries after the issue of global warming. It
reduces the community ability to live
comfortably in the environment for their activities. UHI has the potential to generate
discomfort during activity that would require additional electrical energy for cooling the
room and increase the comfort (Valsson and Bharat, 2009), especially in the tropics (Fischer
and Schär, 2010). In addition, the increase in urban temperatures can cause human becoming
increasingly aggressive and temperamental. This triggers an increase in the crime of murder
and violence. Itis estimated that there were 4.58 cases per hundred thousand people for
increase of 1oF (United States Department of Energy, 2009; Anderson et al., 1997). The study
also found that the UHI accounted for 2-4% of global warming (Jacobson and Ten Hoeve,
2011). Because of the magnitude of the problem that it can caused, perhaps surprising that
few studies examine the factors that influence the UHI in urban areas in Indonesia
(Tursilowati, 2009; Hidayati, 1990; Karyono, 1995). Fewer still focuses exclusively on
residential areas as micro urban in the rural environment, or how rural housing increasing
temperature through its presence in the middle of rural area. This article contributes to the
literature by analyzing the determinants
of
UHI in
rural
residential
areas
in
Palembang. Analysis done by comparing the houses are close together and be part of the
same housing and measurable having the UHI effect is significantly higher than other
housing in the region. This situation results from the residential areas for long times and high
variation on the residents who live in it. The implications of the housing area in
the UHI context explored further in section 2. Section 3 of this article then concentrate on
theoretical concepts and review of the literature. The author developed a theoretical
framework, in particular from the pioneering work of Zhao et al (2014). The author
then review the literature to build a concrete hypothesis that will tested, with a focus on the
types of factors that explains the UHI. In section 4, the researchers present
a methodology to determine the factors that explain the UHI in the study sample, followed by
analysis
of
the
data
sets
obtained
from Taman Sari Kenten I,
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Data collected through field observations and interviews with residents in February 2013.
Field observations provide details on the volume of home, type of roof used, the current
weather measurement, and the ratio of green to built areas of the house. The interview
provides data on population size of the household.
It is quite difficult to create the perfect measurement of UHI with only measure the
temperature in the housing area. The dependent variable in this study is the increase of air
temperature becomes compared to the surrounding area (Unger et al., 2001). This definition
is essentially for a city. Even so, there is no obstacle when it to apply to the housing sphere in
rural areas. This is due to the housing has characteristics similar to the city. It structurally
organized, population density that is larger than the rural areas, and generally have a variety
of self-support system. UHI measurements done by creating a temperature difference
between the temperature measured in the house individually with the temperature in the
reference point. The temperature at the reference point outside the city are at Indonesian
Agency for Meteorological, Climatological and Geophysics station in Kenten within about 2
km from the housing. One disadvantage of this method is the need for an approach to match
data because the temperature of the reference point is only available in four periods of time
while the temperature at the measuring point is available in real-time resolution. However,
assuming no extreme weather changes, this approach has been able to reflect the UHI in the
area under study.
The results of the study presented in section 5 with the aim of helping to understand
the important aspects of theUHI in a residential area in the outer region of the city.
Researcher initially determining factors from five sources of heat (radiation, convection,
evaporation, storage, and anthropogenic) before examining this relationship in the model of
linear regression analysis. The findings suggest two factors strongly associated with UHI: the
surface roughness of the roof and the home population. There are also indications that the
volume of the house is also important in explaining the UHI in residential areas. The findings
are then described in section 6 of the policy implications of local government to
the UHI mitigation efforts, particularly in the context of developing residential areas outside
the city. Section 7 providing conclusion of the study.
2. Development of Settlements in the Outer City Area
Urban Heat Island in Indonesia is increasingly widespread. Tursilowati (2009) stated
that some of the major cities in Indonesia increased air temperature. Expansion of UHI (areas
with high temperature 30-35oC located in the built areas in the city center), in Bandung,
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around 12 606 ha or 4.47% per year, in Semarang 12 174 ha or 8.4% per year, in Surabaya
1,512 ha or 4.8% per year. In 1990, the air temperature Jakarta City detected 0,02-1oC higher
than the sub-urban areas, (Hidayati, 1990; Karyono, 1995).
UHI expansion due to urbanization accompanied with change in various native
landforms with urban land (Lo and Quattrochi, 2003), increasing the density structure (Wong
et al., 2010), and housing and infrastructure (Upchurch, 2005). The areas most vulnerable
to UHI is growing cities and suburban areas (urban fringe). According to Ernawi (2010),
cities suburban area in Indonesia spread and packed even beyond the boundaries of the
administrative area of the city. Villages quickly became the center of a new urban activities.
Land that was originally in the form of open land, swamp, or forest land converted into solid
building resulting in a change in land use, demographic, and ecological balance (Yoyok et al.,
1997).
From all the cities in the world, new housing on the outskirts of the city estimated to
20-30% (Robinson, 2005). Approximately 8-19% of the population major cities in Indonesia
live in housing (Hoek-Smit, 2005). Housing is an area which has its own environmental and
sociological characteristics, generally do not bring a sense of ownership and personal
identity, do not have clear boundaries of personal and public life, and have a weak
relationship with the open space (Woehr, 2007). It is increasingly seen in developing
countries, such as Indonesia, that the high-quality housing is quite expensive (Bloom and
Khanna, 2007). The size of housing in major cities getting bigger, but the distance between
house increasingly narrow. It resulted in a narrowing of the space and replacing natural
spaces into artificial space (Upchurch, 2005).
Palembang is a city that is growing rapidly from a swamp land. Febriana (2008) wrote
that Palembang which has an area of 400.610 km2 originally consisted of 54% of the swamp
area. In 1999, the swamp area becomes 30.35%. The swamp land area continued to decline
until in 2007 the only remaining 15.3%. Recent data in Palembang Urban Land Use Planning
2010-2020 show that wetlands in Palembang only remaining 5835.19 ha or about14.58%.
That is, in the period 1999-2010, Palembang has lost swamp of 5713.60 ha
or approximately 14.26%.
Although Palembang increasingly crowded with buildings and the land conversion,
studies to find the effects of the heat generated from such a situation has not been addressed
until now. This situation if left unchecked will have negative effects for the people of
Palembang which grew by 1.82% per year, especially when the economic growth sector of
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the building continued rising 7.12% in 2009, 8.03% in 2010, and 12.92% in 2011 (Juliantina,
2012). Therefore, this research is very important to give least effect for the people of
Palembang.
3. Theoretical Literature and Hypotheses
Theoretical foundation of this research is mainly related to the results of Zhao et al
(2014) study. Some reviews from other literature also added, with a focus on factors that
developed from Zhao et al (2014).
3.1.
Theoretical Concept and UHI Components
Core discussion about the factors that influence the UHI assumed that the green area
is the main factor that lowers the UHI (Taha, 1997; Peng et al., 2012). However, this
assumption questioned by Zhao et al (2014). Zhao et al (2014) conducted a study on a
number of cities in Canada and the United States which spread across three climatic regions:
cold, temperate, and dry climate. If the hypothesis holds true, UHI in cold and dry climate
will be very high because almost no trees in this area. However, their study actually found the
opposite. UHI in cold and dry climates are even lower than in temperate region.
The main explanation to this finding is actually not plants that reduces the UHI, but
the roughness factor. In the temperate region, rural areas are more rugged than urban areas.
As a result, UHI is higher. Conversely, in a dry and cool area, it is urban which more rugged
than rural. UHI in this area can even negative. Green land apparently lowering UHI because
trees and various types of plants have a high roughness. In the temperate region and tropical
region, rural areas have far more trees than the city. Consequently, the resulting effect is
high UHI in town that does not have a tree. But in the cold and dry climate, rural areas are
very smooth. There are no trees and there is only desert or snow. Area of the city, although
also devoid of trees, but have buildings that increase the surface roughness. As a result there
is a negative impact on the heat in urban areas in the climate.
UHI equation developed in the simulation by Zhao et al (2014) using the method of
Lee et al (2011) is a non-linear equation. The linear form of the equation is as follows:
(1)
Where T is the temperature, C is the contribution, R is the radiation, H is convection, LE is
evaporation, S is storage, AH is anthropogenic heat, and e is the error rate arising from the
nonlinear form transformation. Simply put, this equation states that the UHI is the result of
the amount of heat by radiation, convection, evaporation, heat savings, and anthropogenic
heat contribution. In his study, Zhao et al (2014) using a CLM (Oleson, 2010) found that the
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heat convection is higher than the other heat in contributing to the UHI, and convection heat
donated by the surface roughness. Green land itself contributes heat through evaporation and
its contribution is smaller than the heat storage and heat radiation. Anthropogenic heat give
smallest contribution.
3.2.
Contributors Variables for Each UHI Component
The first part in equation (1) is the radiation component. In the form of non-linear, the
radiation component of UHI expressed through the following equation:
(2)
Where λ0 is the local climate sensitivity, expressed through the equation
(3)
Where ε is the surface emissivity, σ is the Stefan-Boltzmann constant, and T is the surface
temperature.
f is the energy redistribution factor, expressed through the equation
(4)
Where ρ is air density, Cp is the specific heat of air at constant pressure, ra is the aerodynamic
resistance to heat diffusion, expressed through the equation
(5)
Where Ta is the temperature at the reference height and H is the sensible heat flux, and β is
the Bowen ratio, which is expressed through the equation
(6)
Where LE is the latent heat flux.
R*n is the apparent net radiation, expressed through the equation
(7)
Where
is the albedo,
is the incoming solar radiation, and
is a long-wave radiation
coming.
In the above equation, ra, β, and R*n are parameters that come from human socio-technical
nature. The main component of the equation (2) is R*n because this term gives the
characteristics of the interaction between the material and the coming radiation. This
term determined by the albedo and emissivity of the material. Albedo has long been
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considered essential for UHI mitigation (Akbari et al., 2009). Even so, it determined again by
the sum of the radiation coming from the sun. In turn, this radiation determined by
meteorological factors, especially weather (Rong, 2006). On cloudy days, the radiation
coming less and therefore reduce the surface heating. Hence, UHI typically higher under
clear skies (Gosling et al., 2014). Furthermore, the weather effect is stronger than the surface
properties in developing UHI (Rosenzweig et al., 2006). Weather therefore, determinehow
much radiation coming from the sun to then interact with the material and apparent net
radiation. Based on these considerations, it can formulated the following hypothesis:
Hypothesis 1: The weather is negatively related to UHI intensity.
The second term in equation (1) is the rate that describes heat convection. This heat is
rarely studied in the earlier UHI studies (Arnfield, 2003). In non-linear, the second term
expressed as follows:
(8)
Where QS is the heat generated by the storage, QAH is the heat generated by anthropogenic
source, and Δf1 is the difference in energy redistribution factor, which is expressed through
the equation:
(9)
Δf1 is an important term in this equation because it contains the aerodynamic resistance to
heat diffusion. This term is the sum of the characteristics of the surface layer of the
atmosphere and the characteristics of the material at the lower limit. It determines the
material's ability to inhibit heat diffusion. Smooth material less able to inhibit heat diffusion,
and unable to prevent heat buildup.
Although it has elements of atmospheric, aerodynamic resistance can vary more
because of the materials used. In earlier studies using MODIS, this factor was not considered
because of methodological difficulties (Marpaung, 2009). Tursilowati (2007) see more
aerodynamic resistance to meteorological element to declare it as wind speed function. Even
so, should it be viewed as a factor determined by the roughness of roofing materials
that determinethe behavior of the wind as an agent convection (Voogt and Grimmond, 2000).
In addition, studies Zhao et al (2014) found that the convection component is the
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largest component of UHI. Therefore, a more rational put roughness characteristics of
materials as a major factor in determining the UHI.
Hypothesis 2: Surface roughness of roof coverings negatively related to the intensity of the
UHI.
The third term of equation (1) is a heat rate of evaporation. It formulated by the
following non-linear:
(10)
Where
(11)
This term called evaporative heat rate for Bowen ratio (β) which reduced due to more
radiation energy channeled through the surface latent heat flux (Zhao et al., 2014), especially
during post-rain. Traditionally, this is the most powerful heat effect due to the denser
vegetation in wetlands will have a high evaporation rate compared to other materials.
Evaporation can come from soil or cover material from the blue land such as ponds and pools
(Golden, 2004). However, the main factor is the vegetation (Brazel et al., 2009). In addition
to providing an increased rate of evaporation, the vegetation is also useful to prevent
radiation coming to the surface so it is also relevant to the radiation rate (Valsson and Bharat,
2009). However, this research carried out in the context of the housing with the rest of the
land is very limited. In contrast to urban areas which can grow large trees, mainly for
sidewalks shade and parks, housing does not have this ability. As a result, the green land area
will be very little and negligible to the UHI. Because the author conducted a study on
housing, the author suspect the absencerelationship between the ratio of green areas to the
built areas.
Hypothesis 3: The ratio of green to built areas not related to the intensity of the UHI
The fourth term is the rate of heat storage equation. Non-linear equation for storage
rate is as follows:
(12)
Heat storage material associated with the ability to absorb energy. In urban areas, the
storage of heat coming from the broad opaque area and narrow view of the sky (Jahan, 2013).
This heat stored in buildings and the roof and walls (Hamdi and Schayes, 2008). In addition,
the soil surface such as asphalt and cement contributes to the heat storage (Connors, Galletti,
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and Chow, 2013). Of a number of factors, roads and buildings is the strongest (Dodman,
2009). This study conducted in a residential neighborhood where the main factor is
the building. Building factor in this case is high because of the housing tends to have the
same size in length x width dimensions but not the height dimension. The higher the building,
the greater the heat storage ability of the building. Therefore, the author suspect that a large
building will show greater intensity of the UHI.
Hypothesis 4: House height is positively related to the intensity of the UHI
Last term associated with anthropogenic heat. The underlying equation is:
(13)
The equation shows that in general the contribution of anthropogenic heat to the UHI is
linear, straight from the magnitude of the anthropogenic heat.
Anthropogenic heat comes from human activities such as energy use, Air
Conditioning, and respiratory (van Ooststroom, 2011). In addition, motorized vehicle can
also be viewed as a source of anthropogenic heat (Chow and Roth, 2006). Nelson (2011) uses
the energy density, which defined as the ratio of energy consumption to the building area. In
three dimensions, it can expressed as the ratio of the energy consumption to the building
volume. This makes sense because the energy density reflects the amount of energy used in a
home by humans. This makes the researcher suggest that there is a relationship between
energy density and intensity of the UHI.
Hypothesis 5: Energy density is positively related to the intensity of the UHI
4. Methodology
This analysis based on survey results in Taman Sari Kenten I, outside Palembang. The
coordinates of the location is
2°54'4.46"S and 104°46'4.95"E. This settlement chosen
because it reflects high UHI profile than the two other housing included in the first survey:
Talang Kelapo (2°56'31.27"S; 104°41'17.38"E) and Taman Ogan Permai Jakabaring (3°
1'36.98"S; 104°46'42.05"E). The residential site are also closer to rural areas than two other
housing.
Information gathered through field measurements, observations, and interviews with
residents. Samples taken covering the house in Taman Sari Kenten include 231 homes. By
removing the house that has not been occupied and not allow to collect the data, the total
house successfully surveyed include 125 homes.
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Researcher first peforming temperature measurements at three meters from the front
door of the house and a height of 2 meters above ground level. This would characterie the
CLUHI (Canopy-Layer UHI) (Martin et al., 2014). After the measurement, the researcher
collect the dimensions of the house, the weather measurement, identify the type of roof
covering, and the area of green areas and the house. Researcher asked residents about the
number of occupants in the house and monthly energy consumption on average.
The total sample of 125 homes have enough adequacy for regression using the effect
size of 0.20; alpha 0.05; power 0.95; and the number of predictors 5 using G * Power 3.0.10
software.
Table 1 presents the descriptive statistics related to the study variables.
Majority homes (78.4%) have only one level. This measure reflects the average house height
in similar housing. Looking at this size, the ratio of green to built quite surprising: half
(55.2%) of the house has a ratio of 0 which means it has no green space at all.
Type of roof covering measured in this study reflects asbestos cover. Actually there
are four types of roofing inTaman Sari Kenten: asbestos, tile, multiroof, and zinc. However,
observations show that asbestos have different roughness of the other three types of roof
coverings. Asbestos, even if thin, has a rougher surface than tile,multiroof, and zinc. It shown
from moss inability to cover the asbestos roof. Tile, multiroof, and zinc, have a waving
pattern and the same shape. All three wave-shaped and has a high surface smoothness. On the
old tile roof, moss can stick because basically the raw material of this roof is ground and this
adds to the smoothness of the surface. At the time of rain, dirt in the tile, multiroof, and
zinc can be immediately washed and updated surface becomes even more slippery than
before. This does not apply to asbestos which has more micro roughness, although that also
have a wavy pattern. Therefore, the researcher decided asbestos as the most rugged roof.
25.4% of homes have asbestos cover.
Most of the houses occupied by 5 people (32.8%). There are 3 houses inhabited by
seven people and six houses inhabited by only two people. Energy units in this study used
rupiahs because respondents more easily stated their energy use based on the average
monthly payment for their electricity consumption. In the total population, the average energy
density is Rp 2.3889 /m2.level.
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Table 1. Descriptive Statistics
Mean
Std. Deviation Number of Positive
Dependent Variable
UHI Intensity
4.1257 2.03903
Independent Variables
House height
20(14.9%)
Asbestos Roof
34 (25.4%)
Weather
4.1.
Clear
121 (90,3%)
Partially Clouded
1 (0,7%)
Heavily Clouded
3 (2,2%)
Green to Built ratio
0.1414 0.37336
Energy Density
2.3889 1.60430
Operationalization of Variables
The main strength of this study is perform a direct measurement of the
study location and carried out by providing clear and micro boundaries. The dependent
variable is the UHI intensity expressed as the difference between the temperature of the
measurement site with a temperature at the reference point in rural area provided by
Indonesian Agency for Meteorological, Climatological and Geophysics Station.
Table 2 describes the key variables tested and the sign hypothesized about their
relationship with the UHIintensity (analysis results are also summarized in this table);
relationships tested using multiple linear regression model. Correlation analysis showed that
there is no multicollinearity between the independent variables of the study. The independent
variables consist of five groups. The first is the weather. The weather coded 1 for sunny
weather, 2 to cloudy, and 3 to overcast. The weather is known by observation simultaneously
with temperature measurement. The second is the roof surface roughness. Asbestos
surface coded 1 and non-asbestos coded 0. This roof surface roughness indicator is known by
observation when the temperature measurement. Third is the ratio of green space to
built space. The ratio calculated by dividing the land area covered by extensive plant and the
built environment such as house, roads, and ceramics or cement covered veranda. Fourth is
house height. Building with one level codenamed 1 and two-story building coded 2. These
variables are known directly through observation in the time of measurement. Fifth is the
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energy density. Energy density measured by dividing the monthly electricity consumption by
the volume of the house (length x width x height).
Table 2. Variables Description and Summary of the Research
Variables
Type
Description
Relatio
Sign
n
Dependent
C*
UHI Intensity
D**
1 = sunny; 2 =
Significanc
e
Variable
Weather (H1)
-
Confirmed
cloudy; 3 =
Not
Confirmed
overcast
Roof roughness
D
(H2)
Green to Built
1 = asbestos; 0 =
-
Confirmed
tile, multiroof, zinc
C
Ratio (H3)
The ratio of the
Not
Confirmed
-
Confirmed
green area to the
Not
Confirmed
built area
House Height
D
(H4)
Energy Density
(H5)
1= one story; 2 =
+
Confirmed
Confirmed
+
Confirmed
Confirmed
two story
C
The amount of
energy consumed
per one m2.story
Note: * = continuous variable; ** = discrete variable
5. Results, Findings, and Discussion
The results of the four models to explain UHI intensity presented in Table 3.
Researchers using stepwise selection method. Variables entered in line with the findings
of Zhao et al (2014) on the level of influence of the factors of the UHI. The regression model
showed statistical evidence of a relationship between UHI intensity and the array of factors
that presented before by the theory. Only the first model significant at the level of p