DETERMINANT FACTORS FOR THE INTENSITY OF URBAN HEAT ISLAND IN RESIDENTIAL AREAS IN RURAL ENVIRONMENT.

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Asian Academic Research Journal of Social Sciences & Humanities

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ISSN 2319-2801 (Online)

Asian Academic Research Journal of Multidisciplinary

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DEPARTMENT OF STUDIES AND RESEARCH IN ECONOMICS, MANGALORE UNIVERSITY, MANAGALAGANGOTHRI,

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Asian Academic Research Journal of Multidisciplinary

www.asianacademicresearch.org 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

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www.asianacademicresearch.org

14. 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 REPORT. – A CASE 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 TAAE ; MOHANAD MOUSA AL-FARHAN ; PROF. DR.AMER ATYAH LAFTAH

343 – 368

29.

CLINICAL EVALUATION OF MASHABALADI KWATH IN THE MANAGEMENT OF ARDITA (FACIAL PARALYSIS)

DR. SWAPNIL S. SINGHAI

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www.asianacademicresearch.org

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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 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

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41 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

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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

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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

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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

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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

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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).

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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

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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:

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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,

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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:

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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

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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

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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

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

4.1. 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

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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

n

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 <0.10.

Three other models are at are not significant.

Regression 1 test hypothesis that roof roughness and house height influence

the UHI intensity. Only height showing significant effect. However, the two variables show

the expected sign. In the second step, the weathervariables included in the model, but found

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regression 3 did not show significance of green to built radio, but did show the expected sign.

Finally, regression 4 examines the relationship between energy density with the dependent

variable. Energy density is found to relate positively and significantly (p <0.10) to

the UHI intensity.

Table 3. Linear Regression Model

Research Variables Reg. 1 Reg. 2 Reg. 3 Reg. 4

Roof cover surface roughness -0.064 -0.026 -0.049 0.047

Building height 0.329** 0.329** 0.292* 0.387**

Weather -0.099 -0.101 -0.119

Green to built ratio -0.151 -0.199

Energy density 0.295*

Model statistics

Adj. R. Sq 0.071 0.054 0.05 0.1

F-Value 2.463* 1.724 1.501 1.842

Notes: * significant at p<0.10; ** significant at p<0.05

Against the second hypothesis, the researcher investigated the relationship of

roughness roof coverings with UHI intensity. All regression showed no significant correlation

and regression 4 even showing signs change. This result is important, because it shows that

the heat convection is not a major factor contributing to the UHI in the housing area. These

characteristics distinguish between residential areas with urban areas dominated by

convection.

High house provide large heat storage because it doubled the size of the home and the

ability to accommodate heat. Hypothesis 4 confirmed; the higher the house, the

greater the UHI intensity. These results are consistent with earlier studies in the urban context

(Ng, 2011; Alcoforado and Andrade, 2006; Chan et al., 2007). High-building is an important

factor in explaining the UHI intensity, especially in providing storage UHI.

Accordance the results of earlier studies (Chow and Roth, 2006 and Elsayed, 2012)

and the first hypothesis, the researcher found a negative relationship between weather

with UHI intensity. However, this relationship was not significant. These results indicate that

heat radiation does not contribute strongly to the UHI on housing.

Researchers did not find a significant association between green to built ratio

with UHI intensity; although the sign of the corresponding correlation confirmed; and hence

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diversity on these ratios. 55.2% have a ratio of 0 which indicates the house did not have a

green land. Therefore, there is a strong bias in the house that has a green land. Therefore, it

may not deserve to tested statistically.

Hypothesis 5 confirmed, in line with the results of earlier studies. The research found

a significant relationship between energy density and intensity of the UHI.

6. Policy Implications

Overall, two variables was found in this study are significant effect on UHI intensity:

building height and energy density. However, we must understand the weaknesses of research

due to the small sample size and the tentative nature of these policy discussions. The

argument can put forward to focus on key areas that must addressed and further studied both

by policy makers as well as by researchers.

The first significant variable is the height of the building. High building reflects heat

savings in the urban context is actually just became the second strongest contributor

to the UHI intensity, weaker than convection heat. Housing characteristics with narrow

distance and functions as residence make it a perfect place for large heat storage.

Contributions of heat stored by this house is greater than heat convection due to high roof

coverings in two-storey houses. The second significant variable is the energy density. This

variable is anthropogenic and, in the urban context, is a negligible factor in causing the UHI.

However, because housing is a residence and thus become the center of human activity on an

ongoing basis, the anthropogenic heat becomes very meaningful. Combined with tall houses

and large energy contained in it, houses in a residential area is a large heat source in rural

areas. We don't even take into account the heat from the heat caused by the highway.

A number of policy implications for local government can drawn from the above

findings, especially since the growth rate of housing in the area outside the city continues to

grow in regions outside Java.

6.1. Implications on Building Height

Most homes in residential areas outside the city built standardized with a permanent

roof. However, homeowners have the freedom to add the roof. This indicated by grow houses

type, built by temporary cement roof to increase the level. Options to increase the home are

even more likely when there is no way to extend the house to the side due to the distance in

the adjoining residential houses. This study found that by increasing the level of the house,

the house became a source of heat because it absorbs more heat from the environment. A

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and it absorb heat without hindrance. This is probably because in general the house built after

the soil covered and it means no more trees covering the ground, while the tree serves to

prevent heat exposed to the level of the house. Therefore, researcher suggest that:

1. Local governments need to build local laws that prohibit home in a residential area

to add height to increase the level. If you want to increase the level, then there

must exist a heat convection solvers such as roof with rough surface or protrusion

in many levels, planting greenery on the outskirts of the level, or not to use a level

to the source of electrical energy which is greater than the original level of the

building.

2. Housing developers must provide a large tree that can prevent the heat stored in

the building level. This tree needs to installed on the roadside so that the heat from

the sun blocked to enter the second level.

3. Residents should plan carefully when adding the level of their home. This plan is

not only economical but calculating environmental impact that can provided

by new level addition, both for residents and for the community.

6.2. Implications for Energy Density

As a place to live, houses in residential areas provide great

anthropogenic heat contribution to UHI formation. This leads to the houses in a residential

area as a large heat storage box where heat comes not only from the outside, but from the

activity in the building itself. This heat leak out and enhancing the temperature in housing

area, adding that the existing heat from the sun. Therefore, researcher suggest:

1. Local governments should implement energy policies by providing electricity

consumption limit allowed for a certain volume of the house. This will encourage

residents to increase the level if greater energy consumption inevitable. To that

end, the policy of the addition rate must also be given.

2. Housing developers can consider the power restrictions for the size of a particular

house not to exceed the limits that can improve the overall ambient temperature.

3. Residents need to consider more about power consumption, especially that

generate heat such as Air Conditioning.

7. Conclusion

UHI phenomenon that occurs in a residential area outside the city is still far from the

attention in the academic literature. This is due to the region seen as micro in size and

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heat island compared to the surrounding rural areas where they located. This paper examines

this aspect further using housing profiles with significant UHI intensity in the tropics.

One of the strengths of this analysis is the uniqueness of the data source that collected

from residential areas. Previous studies, especially Zhao et al (2014) found that based on the

strength of its contribution, a source of heat for the urban area is the convection, heat storage,

radiation, evaporation, and anthropogenic heat. This study found that in the context of

housing, the main source of heat is the heat storage, anthropogenic heat, evaporation,

radiation, and finally convection. These results show the characteristics of housing outside

the city which is different from the urban characteristics. Heat storage and anthropogenic heat

become the main source of reasonableUHI in housing because this region is a place where

human activities take place in a sustainable and comprehensive way.

Future studies need to develop a study of anthropogenic heat contribution on the UHI,

included the heat of the vehicle and human activities outside the home, but still in the

residential complex. Future studies also need to learn more about the mechanisms that lead

more tall buildings generate more heat than short buildings. Various mechanisms can

proposed and in this study, the mechanism proposed is the heat storage mechanism. Given the

nature of the non-linear equation of UHI, we needs further examination of other mechanisms

that contribute to the increase in temperature due to level addition.

Finally, in line with the rapid growth of housing in the area outside the city, local

authorities and housing developers need to pay attention to policies in sustainable

development of housing, so it not become a large source of heat that can reduce the feeling of

comfortable living in rural areas. If the suggestions accepted and applied, housing outside the

city can become a main attraction for urban residents and provide great added value for the

residents of the city who want to reside outside the city.

Appendices

Table 3. UHI Profile of Three Housing in Palembang

A B C D

No Housing House

Average

Temperature (T)

Average UHI

(ΔT)

1 Taman Sari Kenten I 125 32.15 4.173

2 Taman Ogan Permai 40 33.11 3.100

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Table 4. Data for Analysis

No House ΔT Weather Asbestos

Green

to

Built

Ratio Height

Energy

Density

1 A04 2,6 1 0 0,35 1 1,97

2 A06 3,22 1 0 0 1

3 A07 4,43 1 0 0 1

4 A08 3,5 1 0 0 1 3,97

5 A09 2,92 1 0 1,69 1 1,1

6 A10 3,31 1 0 0 1 1,89

7 A11 2,94 1 0 0 1 3,56

8 B01 2,22 1 0 1,06 1 2,11

9 B02 3,64 1 0 2,04 1 4,44

10 B03 4,84 1 0 2,27 1 5,56

11 B06 2,72 1 0 0 1

12 B07 3,53 1 0 0 2 0,79

13 B08 8,5 1 0 0,36 1 1,78

14 B09 9,05 1 0 0,02 2 0,53

15 B10 11,29 1 0 0,12 2 1,05

16 B11 3,26 1 0 0,03 1 2,1

17 B15 2,77 1 0 0 2

18 B16 7,3 1 0 0 1

19 B17 2,79 1 0 0 1

20 B18 7,15 1 0 0,9 1

21 B20 3,24 1 0 0 2

22 B22 3,12 1 0 0 1

23 B24 3,13 1 0 1,36 1 3,08

24 B25 2,7 1 0 0,02 1

25 B26 3,05 1 0 0,5 1

26 C02 4,26 1 0 0,06 1

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57 No House ΔT Weather Asbestos

Green

to

Built

Energy

Density

28 C05 4,55 1 0 0 1

29 C06 3,43 1 0 0 1 1,95

30 C08 2,91 1 0 0,18 1

31 C09 2,83 1 0 0 1

32 C14 3,17 1 0 0,02 1

33 C15 4,68 1 0 0,11 1

34 C16 4,12 1 0 0 1

35 C17 4,51 1 0 0,44 1

36 D02 2,94 1 0 0,43 1

37 D03 3,89 1 0 0,16 1

38 D06 3,06 1 1 0,08 1 1,85

39 D07 3,17 1 0 0 2

40 D09 2,88 1 0 0,28 1

41 D10 3,52 1 0 0,1 1

42 D11 4,31 1 0 0,03 1

43 D13 4,18 1 0 0 1 2,36

44 D15 6,83 1 1 0,09 1 1,9

45 D16 2,91 3 1 0,1 1 1,9

46 D17 2,73 3 0 0,06 1

47 D18 1,91 1 0 0,02 2 1,81

48 D20 2,3 1 0 0 1 2,09

49 D21 2,25 1 0 0 1 2,16

50 D22 1,92 1 0 0 1

51 D23 2,02 1 0 0,17 1

52 D24 1,97 1 0 0,07 1 1,03

53 D25 2,54 3 0 0,05 1

54 D26 2,89 1 0 0 1

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Green

to

Built

Energy

Density

56 D28 2,72 1 0 0,27 1

57 E01 2,67 1 0 0,07 1

58 E02 8,76 1 0 0,07 1

59 E06 3,52 1 0 0 2

60 E07 3,2 1 0 0 2

61 E12 2,5 1 0 0 1

62 E15 8,74 1 0 0 2 2,53

63 E16 9,23 1 0 0,09 1

64 E17 9,02 1 0 0 2 0,71

65 E18 8,31 1 0 0,1 1

66 E20 1,28 2 1 0 1

67 E22 9,15 1 0 0,18 1 1,79

68 E24 9,51 1 0 0,17 1 3,27

69 E28 9,16 1 0 0 1 2,82

70 E29 9,23 1 0 0 1 7,36

71 F02 6,18 1 0 0,02 1

72 F03 6,02 1 0 0,02 1

73 F05 6,8 1 0 0 1 6,88

74 F07 5,5 1 0 0,02 1 2,11

75 F09 7,17 1 0 0,04 1 1,35

76 F12 5,81 1 0 0 2 2,03

77 F14 3 1 0 0,36 2

78 F15 2,59 1 0 0,14 1

79 F17 4,15 1 0 0 1

80 F18 3,93 1 0 0 1

81 F19 3,06 1 0 0 1

82 F20 4,85 1 0 0 1

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Green

to

Built

Energy

Density

84 F24 4,1 1 0 0 1

85 F26 4,32 1 0 0 1

86 G03 3,91 1 0 0 1 1,62

87 G04 4,41 1 0 0,02 2

88 G09-10 3,33 1 1 0,02 1

89 H01 5,3 1 0 0,07 2

90 H02 3,95 1 0 0 2

91 H03 3,05 1 1 0 1

92 H04 3,31 1 1 0 1

93 H05 2,87 1 1 0 1

94 H06 3,61 1 1 0 1

95 H10 3,18 1 1 0,52 1 0,62

96 H11 2,44 1 1 0 1

97 H15 4,17 1 0 0 1

98 H18 3,32 1 1 0 1

99 I01 4,52 1 1 0 1

100 I02 2,67 1 1 0 1

101 I03 3,96 1 0 0 2

102 I04 3,24 1 1 0 1 0,92

103 I05 4,28 1 1 1,38 1

104 I09 1,9 1 1 0 1

105 I10 3,26 1 1 0,05 2

106 I12 1,9 1 1 0 1

107 I17 2,09 1 1 0,09 2

108 I19 3,15 1 0 0 1

109 I20 6,58 1 1 0 1 1,18

110 I21 4 1 0 1 5,52

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Green

to

Built

Energy

Density

112 J03 3,56 1 1 0 1

113 J05 1,84 1 1 0 1

114 J07 1,96 1 0 0 1

115 J09 4,19 1 0 0,02 2

116 J11 3,49 1 1 0 1

117 J13 4 1 1 0 1

118 J14 3,66 1 1 0 1

119 J15 3,98 1 1 0 1

120 J16 2,28 1 1 0 1

121 J17 4,44 1 1 0 1

122 J18 5 1 1 0 1

123 L04 2,57 1 1 0,3 1

124 L06 2,18 1 1 0 1

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