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0360-1323/$ - se

doi:10.1016/j.bu

Abbreviations

University of S

Prince George�CorrespondE-mail addr

Building and Environment 42 (2007) 2949–2970

www.elsevier.com/locate/buildenv

Environmental study of the impact of greenery in an institutionalcampus in the tropics

N.H. Wong�, Steve Kardinal Jusuf, Aung Aung La Win, Htun Kyaw Thu,To Syatia Negara, Wu Xuchao

Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore

Received 13 February 2006; received in revised form 25 April 2006; accepted 7 June 2006

Abstract

Urban environment quality is worsening every year. It is a fact that the urban air temperature is gradually rising in all cities and some

effective measures are needed to mitigate it. Planting of vegetation is one of the main strategies to mitigate the urban heat island (UHI)

effect. Large urban parks can extend positive effects to the surrounding built environment. National University of Singapore (NUS)

complex can be considered as a ‘‘city’’ on a smaller scale. The greenery along Kent Ridge Road seems like a ‘‘rural’’ area, with a cooler

ambient temperature. Some methodologies were employed in this study, such as satellite image, field measurement and computer

simulations. The satellite image was used to identify the ‘‘hot’’ and ‘‘cool’’ spots in NUS environment. Field measurement was used to get

the real temperature distribution across the campus and finally, computer simulation was used to predict some scenarios of different

conditions. The result shows that buildings near or surrounded by greenery have lower ambient temperature than the ones away from the

greenery and it is an effective way to lower the ambient temperature. The TAS simulation results also show that a rooftop garden has the

potential of cooling energy savings for NUS buildings.

r 2006 Published by Elsevier Ltd.

Keywords: Effect of greenery; Campus area; Satellite images; Thermal satellite images; Field measurement; Computer simulation

1. Introduction

1.1. Background

Environmental quality is an abstract concept resultingfrom both human and natural factors operating at differentspatial scales. In urban areas the local scale is dominatedby individual buildings, streets and trees, but regional scaleinfluences may include the whole city and beyond. Urbanenvironmental quality is a complex and spatially variableparameter which is a function of interrelated factorsincluding the urban heat island (UHI), the distribution ofgreenery, building density and geometry and air quality [1].

e front matter r 2006 Published by Elsevier Ltd.

ildenv.2006.06.004

: NUH, National University Hospital; NUS, National

ingapore; OED, Office of Estate and Development; PGP,

Park; SDE, School of Design and Environment

ing author.

ess: bdgwnh@nus.edu.sg (N.H. Wong).

Urban environment quality is worsening every year. It isa fact that the urban air temperature is gradually rising inall cities and some effective measures are needed to mitigateit. Several factors become the cause of it, such as thediminishing of green area, low wind velocity due to highbuilding density and change of street surface coatingmaterials [2]. Dousset [3] stated that the main contributingfactors are changes in the characteristics of the surface(albedo, thermal capacity, heat conductivity), replacementof vegetation by asphalt and concrete, the decrease ofsurface moisture available for evapo-transpiration. Mod-ification of land cover in urban areas can cause the local airand surface temperatures to rise several degrees higher thanthe simultaneous temperature of the surrounding ruralareas. This may lead to overheating by human energyrelease and absorption of solar radiation on dark surfacesand buildings. This problem will be further aggravated byincreasing demand on air conditioning, which will againlead to further heating and CO2 release.

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Planting of vegetation is one of the main strategies tomitigate the UHI effect. A single tree can already moderatethe climate well. But its impacts are limited only to themicroclimate [4]. Kawashima [5], based on satellite images,studied the effects of vegetation density on the surfacetemperatures in the urban and rural areas of Tokyo.According to his observation, lower surface temperaturewas generally observed in green areas while highertemperatures were found on the soil and buildings duringthe daytime. However, the effect of vegetation on reducingsurface temperature in the urban area is relatively smallerthan that in the suburbs. In the urban area, the surfacetemperature ranged from 1.4 to 2.7 1C in the green areawhile it ranged from 2.0 to 3.4 1C in buildings and 2.3 to4.9 1C on the soil. In the countryside, the surfacetemperature ranged from 2.6 to 2.8 1C in forests while itranged from 3.3 to 4.2 1C in buildings and 5.1 to 5.9 1C onthe soil. At night, the lower surface temperature wasobserved in green areas in the urban environment whilehigher surface temperature was found in forests in thesuburbs.

From the previous study on the Singapore UHI [6], thesatellite image shows strong evidence that the UHI effectdoes occur in Singapore. The ‘‘hot’’ spots are normallyobserved on exposed hard surfaces in the urban context.‘‘Cool’’ spots are mostly observed on the surface ofgreenery and water catchments. In the analysis of long-term climatic data of Singapore, four meteorologicalstations were chosen with data coverage of at least 10years. Yearly mean dry bulb temperatures are analyzed andfound to be rising significantly in Changi airport. At theother three stations, regression results show that theyearly mean temperatures, have either not changed orare warming at a much slower rate. The analysis of theweather data provide the concrete evidence that thetemperature is increasing in the highly built-up regionwhile it remains unchanged in the well planted area.

Ca [7] did some field measurements to determine thecooling influence of a park in the surrounding area ofthe Tama New Town, a city in the west of Tokyo. Theobservations indicated that vegetation can alter the climateof the town. The surface temperatures measured on thegrass field in the park is much lower than those measuredon the asphalt and on the concrete surfaces. Simulta-neously, air temperature measured at 1.2m above the grassland was more than 2 1C lower than those measured abovehard surfaces in commercial and parking areas. With thesize of 0.6 km2, a park can reduce the air temperature by upto 1.5 1C at noon time in a leeward commercial area at adistance of 1 km.

Yu studied [8] that large urban parks can extend thepositive effects to the surrounding built environment.Through the field measurement, the built environment,which is close to the park, has a lower temperature ofaverage 1.3 1C. Thus, the more the parks are built in anurban area, the lower the urban temperature will be. Thetemperatures measured within parks also have strong

relationship with the density of plants, since plants withhigher LAIs may cause lower ambient temperatures.Results derived from the TAS simulation shows energymay be saved when buildings are built near to parks withmaximum 10% reduction of the cooling load. The ENVI-Met simulation indicates that parks have significantcooling effect on surroundings during both day and night.However, in a country like Singapore which has limited

land area especially in downtown area, provisions of largeparks may not be possible. Another strategy that may beused is the rooftop greenery. The green plants could protectthe hard roof surface from solar radiation, thus it wouldnot emit long wave radiation to the surrounding environ-ment at night and reduce the effect of UHI.Brad [9] explored the role of green roofs in mitigating the

UHI effect in Toronto. The mesoscale community com-pressible (MC2) model was employed in the study. In thesimulation of the UHI in Toronto, 0.58 1C temperaturereduction was observed when 5% of the total area ofthe city was replaced with green roofs. The impact ofgreen roofs in the high density areas is even morepronounced. Temperature across the city was reducedbetween 1 and 2.8 1C.In the previous study on the rooftop garden [10], the

results of field measurements conducted on the two rooftopgardens reveal that the installation of rooftop gardenswould significantly improve the thermal environment ofbuilding roofs in Singapore. Two different types of rooftopgarden systems, intensive and extensive, are tested. Theresults derived from the intensive system reveals that it cansignificantly improve the thermal environment around theroofs and it can benefit not only buildings but alsosurroundings. It can reduce the rooftop ambient tempera-ture up to 3 1C. The extensive green roof systems tend toexperience lower surface temperature up to 18 1C ascompared to the original exposed roof surface, especiallyin areas well covered by vegetation.

1.2. Object of study

National University of Singapore (NUS) complex canbe considered as a ‘‘city’’ on a smaller scale, as shown inFig. 1. The greenery along Kent Ridge Road seems like the‘‘rural’’ area, with a cooler ambient temperature. It isbelieved that the dense greenery makes the ambienttemperature in NUS cooler. It is observed that someopen spaces are left with only grass-covered soil. It is alsoobserved that many buildings in NUS are constructed withflat roof. There is a great potential to apply therooftop greenery in these areas. The advantages, firstlyis that, it will result in a lower heat gain to the building,which leads to a lower cooling load. Secondly, if it iscombined with improvement of the open spaces, it willresult in a lower ambient temperature which indirectlyleads also to a lower cooling load of buildings andpsychologically, provides outdoor thermal comfort forthe people.

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DENSE GREENERY AREA

Fig. 1. National University of Singapore (NUS) map.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–2970 2951

1.3. Objectives

This research has the following objectives:

(a)

to identify the ‘‘hot’’ spots in NUS environment, (b) to study the importance of dense green area for the

microclimate of NUS environment and

(c) to study the energy saving of a building as the result of

lower ambient temperature due to improvement of thegreen area and application of rooftop greenery.

2. Methodology

2.1. Satellite images and thermal satellite image

The interactions of urban surfaces with the atmosphereare governed by surface heat fluxes, the distribution ofwhich is drastically modified by urbanization. The physicalprocesses of these interactions are difficult to monitorsolely with in situ instruments. Satellite images are avery useful tool to get an overall picture of an environment.It has higher spatial distribution but low temporalresolution and shorter data record [11]. NUS environment,specifically the building density and the distribution ofthe greenery, is studied using these images. The satelliteimage and thermal satellite image of Singapore wassuperimposed and zoomed into the campus level, as shownin Fig. 2. Thermal satellite image and NUS mapare analyzed to identify ‘‘hot’’ and ‘‘cool’’ spots in thecampus. Satellite images use different colors to represent

different temperatures, which range from red (hot) to blue(cool).

2.2. Field measurement

The major instruments in this study were HOBO RHand temperature sensors (operating range �20 to þ70 �C,RH accuracy �5%), which were used together with someancillary instruments such as solar cover in conductingmeasurements (as shown in Fig. 3). The HOBO sensorswere configured at an interval of every 10min.Based on the study of satellite images and a walk-

through, the whole campus was divided into three groupswith respect to their different greenery and buildingdistribution conditions. The first group was the dense andvirtually uninterrupted jungle along the Kent Ridge Road.The second group was the less dense greenery areas and thethird group, the areas with sparse greenery. These HOBOmeters were deployed in locations as shown in Fig. 4 andTable 1.The field measurements were conducted on 10th–24th

September 2005. The HOBO sensors contained in solarcovers were all installed on light posts at about 3m abovethe ground.

2.3. Computer simulations

2.3.1. ENVI-Met simulations

ENVI-Met is a three-dimensional microclimate modeldesigned to simulate the surface–plant–air interactions inurban environment [12]. A base model was constructed

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Fig. 2. Singapore thermal image superimposed with satellite image.

Fig. 3. HOBO sensor and solar cover.

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based on the electronic map and building informationprovided by Office of Estate and Development (OED).Parametric variations, as shown in Fig. 5, were also madeto obtain predictions in different conditions. Threescenarios were designed; besides the current condition:replacing the dense greenery along Kent Ridge Road withbuildings, removing all the greenery from NUS environ-ment and converting the grasslands into vertically denserplantation. Then, the variations of ambient temperature inthese scenarios from the base case were recorded andanalyzed.

Basic settings employed in this simulation were asfollows:

1.

Temperature: 303K. 2. Wind speed (at 10m above ground): 1.6m/s. 3. Wind direction: south to north. 4. RH: 84%.

5.

Roughness length in 10m: 0.1. 6. Total simulation: 24 h.

2.3.2. TAS simulations

Engineering building (EA) was chosen as the buildingmodel in the TAS simulations. It is seven storey high andabout 2000m2 footprint area, as shown in Fig. 6. Thissimulation was carried out to compare the cooling energyconsumption for two models. First model is the coolingload due to the difference of ambient temperaturecondition in the different locations and the second modelis the cooling load due to application of different types ofrooftop greenery. In each model, there are two scenarios,without internal load and with internal load.Basic setting for both scenarios:

1.

Air conditioning was on 08.00 am–22.00 pm (extendedoffice hour).

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Fig. 4. Points of measurement location.

Table 1

Measurement location grouping and numbering

Group Location numbera Landmark nearby

Dense greenery (Kent Ridge Road) 1 Water Tank

2 Acoustical Lab

3 Bioinformatics Center

Less dense greenery 4 Faculty of Medicine car park

5 Temasek Hall

6 Computer center

7 Sports field

Sparse greenery 8 PGP Road

9 Engineering Auditorium

10 PGP Canteen

aThe initial numbering was rearranged to facilitate better presentation.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–2970 2953

2.

Temperature and RH were input using the fieldmeasurement result on 15th September 2005.

3.

Thermostat setting:(a) Temperature upper limit: 24 1C and lower limit: 21 1C.(b) Humidity upper limit: 70% and lower limit: 60%.

In the first scenario, the internal heat load was omittedto get the energy saving with only considering the ambienttemperature heat load and different roof heat loads. In thesecond scenario, some general assumptions were made interms of internal load of the building, as follows:

1.

Lighting gain 15W/m2. 2. Occupant’s sensible heat and latent heat 15W/m2. 3. Equipment sensible gain 20W/m2.

3. Findings and discussions

3.1. Satellite images and thermal satellite image

In Fig. 7, the first image is the satellite image ofSingapore which is zoomed into the NUS campus level.The second one is the combined image of satellite imageand the thermal satellite image of NUS. The thermalimage overlaps the NUS image as a transparent image toshow the hints of thermal distribution according to thebuilding density and distribution of green area. The thirdone is the thermal satellite image of NUS environmentin which the relative thermal distribution is presented bydifferent colors. The available relative temperature imagewas derived from Landsat 7 ETMþ thermal band (28 April

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Fig. 5. Four scenarios in ENVI-Met simulation: (A) current condition; (B) replacing dense trees with buildings; (C) removing all greenery; and (D) adding

more trees.

Fig. 6. Engineering building model.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–29702954

2000, 11:09 am). Since there is not much significant MasterPlan development in NUS campus from year 2000 untilthis study was conducted, it is still reasonable to make useof this image.

In the three images, it can obviously be seen that thelocations of red color distribution in thermal image arealmost the same as the locations of buildings in real image.It means that these buildings and their environments createthe heat island effect as hot spots in thermal image. On the

contrary, it can also be seen that the locations of greencolor distribution in thermal image are mainly at the largeproportion of dense greenery area in real image and createcool spots in thermal image. Further away from the densearea of building, the reddish color of hot spots began tochange to the greenish color of cool spots.Fig. 8 shows the surface temperature distribution in

and around the University Cultural Centre and OEDbuilding. Mostly, it is seen that, reddish color of thermal

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Fig. 7. Satellite image and thermal satellite image of NUS.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–2970 2955

distribution are on and around the buildings. Car parkswith sparse greenery area also create hot spots in thermaldistribution.

Fig. 9(A) shows the School of Design and Environment(SDE) and Faculty of Engineering environment, andFig. 9(B) shows the total environment of National

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Fig. 8. University cultural center and OED office.

(A) (B)

SDE

ENGINEERING

NUH

FACULTY OF

SCIENCE &MEDICINE

Fig. 9. SDE and Faculty of Engineering (A), NUH, Faculty of Science and Faculty of Medicine (B).

N.H. Wong et al. / Building and Environment 42 (2007) 2949–29702956

University Hospital (NUH), Faculty of Science andFaculty of Medicine which have a high density ofbuildings. It can be observed that most buildings weredesigned with a combination of pitched roof and flat roofand there is a tremendous thermal distribution in reddishcolor. This is mainly because of the extensive use ofconcrete and other heat absorbing surfaces, by decreasingthe surface moisture available for evapo-transpiration.Furthermore, more solar radiation is absorbed andreradiated into heat because dry surfaces have higherabsorptivity. So, the latent heat flux is very small comparedto the sensible heat in these areas.

Around Prince George’s Park (PGP) environment, theyellowish color of surface temperature distribution is foundon the clusters of PGP residence which are besides thedense area of plantation. It is believed that the evapo-transpiration from plants and trees can reduce the ambienttemperature of environment nearby. However, in the otherpart of PGP residence and King Edward VII Hall area,

they appear as reddish color in surface temperatureespecially those areas far away from plantation area, asshown in Fig. 10(A).According to the observation on NUS campus, a large

green area is found along Kent Ridge Road and PGP Road(Fig. 10(B)). Due to the shading provided by trees and theevapo-transpiration process of the trees, the deep greencolor distribution in surface temperature can be seenobviously at the central part of dense green area. Asmentioned earlier on, it is believed that this environmentcondition has impacts on the other zones. The buildingssurrounded by or at the perimeter of the green areahave better thermal distribution than other buildings awayfrom it.As studied in the above images, approximately 40% of

the campus area is covered by building rooftops which aremainly concrete flat roofs. Therefore, there is a greatpotential to develop rooftop gardens on NUS buildings toachieve better ambient temperature for the whole campus

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Fig. 10. PGP residence and King Edward VII Hall (A), Kent Ridge Road area (B).

Comparison of air temperature on a typical day (15th Sep.2005)

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Water Tank

Acoustic Lab

Bioinformatics Centre

Medicine Carpark

Temasek Hall

Computer Centre

Sports Field

PGP Road

Engineering Auditorium

PGP Canteen

ddd

Fig. 11. Comparison of air temperature on a typical day (15th September 2005).

N.H. Wong et al. / Building and Environment 42 (2007) 2949–2970 2957

and also to save the cooling energy consumption for thebuildings.

3.2. Field measurement

A typical day on 15th September was randomly chosenfrom others to give a snapshot of the temperature changesthroughout the whole day. It also shows the temperaturedifference among different locations (see Fig. 11). The linesrepresenting sparse green areas are clustered on the top,with maximum temperatures reaching 33 1C or even higher.On the contrary, the ‘‘cool spots’’ lines are mostly at thebottom, much nearer to the X-axis. As can be seen in thegraph, the peak temperature difference between location 1(Kent Ridge Road-Water Tank) and location 10 (inside

PGP residence) can be as high as 4 1C at around 13:00.When the time approaches midnight, as is shown nearthe right edge of the graph, the temperature differencebetween these two locations is about 3 1C. This is almostthe same time condition when temperature differenceinduced by UHI effect can be quantified. The differenceof 3 1C within a community-scale environment is believedto be large.Considering the dominant role solar radiation plays

in air temperature and the possible UHI effect, daytimeand night-time data were analyzed separately. In thisstudy, daytime is defined as from 7 am to 7 pm, and thebalance is night-time. In Singapore, the daytime definedhere is approximately coincident with the solar radiationavailability on sunny days. The averages, minimums, and

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Comparison of daytime temperature

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Minimum Average Maximum

Fig. 12. Comparison of daytime temperature.

Comparison of nighttime temperature

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Fig. 13. Comparison of night-time temperature.

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maximums of air temperature during the whole measure-ment period are plotted in Figs. 12 and 13.

As the average temperature is concerned, the pattern isvery obvious. There are fluctuations within some groups,especially in Fig. 13, but the dominant trend, from low tohigh temperature, is unchanged. The daytime averagetemperature ranges from 27.4 to 29.6 1C, and it rangesfrom 25.6 to 27.4 1C during night-time. There is about 2 1Cdifference.

In Fig. 13, it can be perceived that temperaturedifference of daytime maximum among different locationsis very significant. It has a peak of 3.3 1C, which is thedifference between location 1 (Kent Ridge Road-WaterTank) and location 10 (inside PGP residence). In thetropics like Singapore, the maximum temperature is of

special importance, since it determines the sizing of air-conditioning systems. It is also worth taking note of thenight-time minimum temperature in Fig. 13. The line isgenerally very even, but goes up to nearly 23 1C at location10 (inside PGP residence). This verifies the hypothesis thatthe heat accumulated in the day is hard to dissipate duringnight-time due to large concentration of buildings andsparse plantation in PGP.Location 3 (Kent Ridge Road-Bioinformatics) is an eye-

catching lump on the line for maximum daytime tempera-ture (see Fig. 12). It also deviates a bit from the normaltrend on other lines, but is not so evident. Furtherinvestigation was done after preliminary data analysis inan effort to find out whether it is an outlier. It was initiallychosen to represent dense green areas due to its location at

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Fig. 14. Predicted daytime ambient temperature—site plan: (A) current condition; (B) without greenery; (C) replacing forest with buildings; and

(D) introducing denser greenery.

Fig. 15. Predicted daytime ambient temperature—section: (A) current condition; (B) without greenery; (C) replacing forest with buildings; and

(D) introducing denser greenery.

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the east end of Kent Ridge Road. There is a largeconstruction site in close vicinity. The anthropogenic heatfrom the workers, trucks, and construction machinery is

likely to have substantial influence on its vicinity. More-over, the whole construction site is bare soil without anygreen coverage. These two factors may well account for the

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Fig. 16. Predicted night-time ambient temperature—site plan: (A) current condition; (B) without greenery; (C) replacing forest with buildings; and

(D) introducing denser greenery.

Fig. 17. Predicted night-time ambient temperature—section: (A) current condition; (B) without greenery; (C) replacing forest with buildings; and

(D) introducing denser greenery.

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abnormal temperature of location 3 (Kent Ridge Road-Bioinformatics).

On a whole, the field data measurement has achieved thetargeted objectives. The presumed temperature difference

throughout the NUS campus was quantified. Throughelaborate selection of locations according to their greenconditions, the relationship between air temperature andgreenery was verified.

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Table 2

Hourly average ambient temperature on 15th September 2005 (dense green area)

Time Dense green area

Water Tank Acoustical Lab Bioinformatics Average

Temp. Std. dev. RH Std. dev. Temp. Std. dev. RH Std. dev. Temp. Std. dev. RH Std. dev. Temp. RH

09/15/05 00:00:00.0 27.12 0.00 76.10 1.09 27.32 0.22 77.00 1.49 27.59 0.16 74.75 1.10 20.51 75.95

09/15/05 01:00:00.0 26.86 0.20 74.33 4.90 27.12 0.00 74.02 3.71 27.39 0.21 73.15 4.34 20.34 73.83

09/15/05 02:00:00.0 26.93 0.21 74.67 3.99 26.99 0.20 74.77 3.82 27.32 0.22 74.45 3.31 20.31 74.63

09/15/05 03:00:00.0 26.47 0.20 80.90 0.77 26.73 0.00 80.65 0.61 26.73 0.00 80.93 1.16 19.98 80.83

09/15/05 04:00:00.0 26.34 0.00 84.47 1.89 26.73 0.00 83.00 1.37 26.73 0.00 83.53 1.08 19.95 83.67

09/15/05 05:00:00.0 26.34 0.00 88.90 1.24 26.34 0.00 88.17 1.83 26.73 0.00 86.97 0.82 19.85 88.01

09/15/05 06:00:00.0 26.15 0.21 90.63 1.45 26.34 0.00 89.70 0.00 26.41 0.16 89.30 0.98 19.72 89.88

09/15/05 07:00:00.0 26.21 0.20 92.50 0.00 26.54 0.21 89.70 0.00 26.47 0.20 89.70 0.00 19.80 90.63

09/15/05 08:00:00.0 27.06 0.39 86.53 3.01 27.39 0.21 85.10 1.91 27.65 0.41 83.97 2.55 20.52 85.20

09/15/05 09:00:00.0 28.64 0.77 74.92 4.53 28.18 0.32 77.48 2.68 28.90 0.33 74.07 3.61 21.43 75.49

09/15/05 10:00:00.0 28.90 0.22 66.30 2.07 29.03 0.39 66.12 3.43 29.97 0.17 64.92 2.68 21.98 65.78

09/15/05 11:00:00.0 29.43 0.30 56.33 2.13 29.97 0.31 54.18 2.34 30.85 0.33 54.42 4.67 22.56 54.98

09/15/05 12:00:00.0 29.90 0.26 63.02 2.46 30.44 0.21 59.83 1.44 31.59 0.40 58.72 1.99 22.98 60.52

09/15/05 13:00:00.0 30.31 0.26 64.12 1.75 30.71 0.26 60.62 1.48 32.21 0.34 58.65 0.99 23.31 61.13

09/15/05 14:00:00.0 30.24 0.65 66.07 2.16 30.71 0.36 62.02 1.83 32.41 0.31 57.77 1.55 23.34 61.95

09/15/05 15:00:00.0 29.57 0.30 69.12 0.96 30.38 0.30 65.37 1.25 32.28 0.61 59.62 1.70 23.05 64.70

09/15/05 16:00:00.0 28.64 0.30 69.28 1.62 29.30 0.22 65.73 1.72 29.64 0.42 64.62 0.45 21.89 66.54

09/15/05 17:00:00.0 28.18 0.41 71.23 2.22 28.97 0.21 67.13 1.58 29.23 0.33 68.03 1.09 21.59 68.80

09/15/05 18:00:00.0 27.91 0.00 71.60 2.02 28.18 0.21 69.20 1.07 28.51 0.21 68.78 1.66 21.15 69.86

09/15/05 19:00:00.0 27.71 0.33 73.30 1.70 28.24 0.16 71.62 1.13 28.31 0.00 71.00 1.38 21.07 71.97

09/15/05 20:00:00.0 26.41 0.16 79.83 1.73 27.26 0.32 75.63 1.69 27.52 0.25 74.90 1.10 20.29 76.79

09/15/05 21:00:00.0 26.34 0.00 80.40 0.00 26.93 0.21 76.80 0.00 27.19 0.16 76.25 0.60 20.11 77.82

09/15/05 22:00:00.0 26.15 0.21 80.02 0.71 26.80 0.18 77.35 0.60 27.19 0.16 75.55 0.37 20.03 77.64

09/15/05 23:00:00.0 25.82 0.20 80.97 0.72 26.47 0.20 78.30 0.62 26.99 0.20 75.73 0.05 19.82 78.33

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Table 3

Hourly average ambient temperature on 15th September 2005 (less dense green area)

Time Less dense green area

Medical school car park Temasek Hall car park Computer centre Sports field Average

Temp. Std. dev. RH Std. dev. Temp. Std. dev. RH Std. dev. Temp. Std. dev. RH Std. dev. Temp. Std. dev. RH Std. dev. Temp. RH

09/15/05 00:00:00.0 27.52 0.00 71.43 0.84 27.85 0.16 74.42 0.77 27.59 0.16 73.98 1.06 27.59 0.16 74.45 1.11 27.63 73.57

09/15/05 01:00:00.0 27.39 0.21 69.85 3.58 27.91 0.00 69.90 4.50 27.52 0.00 71.30 5.26 27.65 0.20 72.90 3.39 27.62 70.99

09/15/05 02:00:00.0 26.99 0.20 70.62 3.88 27.72 0.21 70.25 4.24 27.39 0.21 71.18 4.50 27.06 0.30 73.90 4.00 27.29 71.49

09/15/05 03:00:00.0 26.15 0.21 80.52 1.71 27.25 0.21 78.95 1.41 26.93 0.21 78.10 0.49 26.15 0.21 83.95 2.52 26.62 80.38

09/15/05 04:00:00.0 26.34 0.00 83.27 1.27 27.12 0.00 82.23 1.89 26.99 0.20 80.73 1.71 26.28 0.16 87.70 0.98 26.68 83.48

09/15/05 05:00:00.0 26.34 0.00 85.97 1.03 26.99 0.20 86.32 1.11 26.73 0.00 86.00 1.57 26.02 0.16 91.10 1.53 26.52 87.35

09/15/05 06:00:00.0 25.89 0.29 88.87 1.21 26.54 0.21 89.77 1.65 26.47 0.20 87.30 0.00 25.95 0.00 94.77 1.76 26.21 90.18

09/15/05 07:00:00.0 26.15 0.59 88.53 1.84 26.60 0.32 91.58 1.42 26.60 0.32 87.32 0.04 26.41 0.46 94.78 1.73 26.44 90.55

09/15/05 08:00:00.0 28.11 0.33 75.75 2.90 28.24 0.30 81.37 4.28 27.91 0.43 79.78 4.19 28.44 0.32 78.60 4.77 28.18 78.88

09/15/05 09:00:00.0 29.30 0.42 66.80 3.48 29.37 0.21 72.07 1.66 28.90 0.42 71.35 1.74 29.77 0.49 67.25 4.23 29.33 69.37

09/15/05 10:00:00.0 30.31 0.36 57.00 1.25 30.11 0.42 61.53 4.57 29.77 0.33 61.75 3.43 30.58 0.33 57.97 1.80 30.19 59.56

09/15/05 11:00:00.0 31.19 0.30 45.35 4.55 30.92 0.34 50.63 1.17 30.78 0.31 51.25 2.05 31.59 0.40 48.47 3.33 31.12 48.93

09/15/05 12:00:00.0 31.66 0.21 50.65 2.19 31.25 0.21 55.17 2.70 31.52 0.00 55.13 1.76 31.39 0.21 55.52 1.77 31.45 54.12

09/15/05 13:00:00.0 31.52 0.26 53.43 1.52 31.32 0.34 58.12 0.75 31.86 0.48 56.42 1.26 31.59 0.31 56.65 1.50 31.57 56.15

09/15/05 14:00:00.0 31.32 0.34 54.97 1.93 30.85 0.33 61.97 2.06 31.87 0.55 56.80 2.13 31.52 0.26 57.33 1.72 31.39 57.77

09/15/05 15:00:00.0 30.92 0.34 59.32 1.08 30.51 0.22 64.17 0.48 31.25 0.33 60.17 1.82 31.12 0.45 61.55 1.79 30.95 61.30

09/15/05 16:00:00.0 29.63 0.21 60.45 1.99 29.77 0.21 62.00 2.48 29.50 0.25 63.07 0.94 29.70 0.22 63.20 2.46 29.65 62.18

09/15/05 17:00:00.0 29.10 0.50 63.32 2.79 29.57 0.16 64.27 1.20 29.30 0.33 65.32 1.85 29.04 0.68 66.17 3.31 29.25 64.77

09/15/05 18:00:00.0 28.57 0.20 64.97 1.94 28.83 0.21 65.27 0.43 28.57 0.32 66.67 0.80 28.70 0.00 66.97 2.10 28.67 65.97

09/15/05 19:00:00.0 28.24 0.16 66.83 1.28 28.64 0.16 68.52 1.13 28.31 0.00 69.82 1.22 28.31 0.43 69.13 1.93 28.37 68.58

09/15/05 20:00:00.0 27.12 0.25 72.65 1.10 27.65 0.20 73.85 1.40 27.59 0.16 73.85 1.28 27.32 0.22 75.53 0.41 27.42 73.97

09/15/05 21:00:00.0 26.86 0.20 73.90 0.80 27.32 0.22 74.02 0.66 27.19 0.16 74.48 0.45 26.99 0.20 76.98 0.45 27.09 74.85

09/15/05 22:00:00.0 26.86 0.20 72.53 0.44 27.52 0.00 73.13 0.33 27.12 0.00 74.20 0.46 27.12 0.00 74.80 0.57 27.16 73.67

09/15/05 23:00:00.0 26.73 0.00 72.97 0.33 27.12 0.00 73.60 0.46 26.80 0.16 74.65 0.37 26.86 0.20 75.47 0.52 26.88 74.17

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Hourly average ambient temperature on 15th September 2005 (sparse green area)

Time Sparse green area

PGP Road Eng. Auditorium PGP inside Average

Temp. Std. dev. RH Std. dev. Temp. Std. dev. RH Std. dev. Temp. Std. dev. RH Std. dev. Temp. RH

09/15/05 00:00:00.0 27.91 0.00 74.73 1.20 28.18 0.21 69.92 0.92 28.64 0.16 68.82 0.84 28.24 71.16

09/15/05 01:00:00.0 27.98 0.16 71.32 4.83 28.31 0.00 65.97 4.11 28.38 0.16 66.15 4.35 28.22 67.81

09/15/05 02:00:00.0 27.85 0.30 73.45 4.12 28.05 0.32 65.55 3.58 28.51 0.21 66.83 3.47 28.13 68.61

09/15/05 03:00:00.0 27.19 0.16 81.18 1.18 27.19 0.16 74.67 1.29 28.31 0.35 72.42 1.32 27.56 76.09

09/15/05 04:00:00.0 27.12 0.00 83.83 1.29 27.06 0.16 78.12 0.87 28.18 0.21 75.58 1.51 27.45 79.18

09/15/05 05:00:00.0 26.93 0.21 87.40 1.39 26.73 0.00 82.75 1.71 27.98 0.30 79.57 1.63 27.21 83.24

09/15/05 06:00:00.0 26.54 0.21 90.63 1.45 26.54 0.21 86.30 1.10 27.85 0.30 81.40 0.77 26.97 86.11

09/15/05 07:00:00.0 26.93 0.33 90.20 1.13 27.19 0.63 84.18 1.99 28.05 0.32 80.93 1.16 27.39 85.11

09/15/05 08:00:00.0 28.57 0.48 79.45 3.78 29.30 0.55 71.47 3.74 29.44 0.64 72.03 3.35 29.10 74.32

09/15/05 09:00:00.0 29.50 0.44 72.22 1.93 30.92 0.56 58.67 3.91 30.58 0.55 65.08 2.30 30.33 65.32

09/15/05 10:00:00.0 30.85 0.21 62.02 2.49 32.07 0.50 48.18 2.70 31.59 0.31 56.02 1.93 31.50 55.41

09/15/05 11:00:00.0 32.07 0.33 49.57 3.61 32.90 0.34 40.75 2.70 32.90 0.21 44.12 2.69 32.62 44.81

09/15/05 12:00:00.0 32.62 0.22 53.75 2.67 33.31 0.22 45.85 1.30 33.52 0.32 47.48 2.59 33.15 49.03

09/15/05 13:00:00.0 32.69 0.17 56.72 1.01 33.17 0.37 49.72 1.90 32.97 0.22 52.65 1.41 32.94 53.03

09/15/05 14:00:00.0 32.27 0.31 58.02 1.49 32.21 0.56 54.77 2.58 32.41 0.31 55.15 1.72 32.30 55.98

09/15/05 15:00:00.0 31.80 0.56 61.03 1.91 31.66 0.49 57.62 1.99 31.32 0.61 59.88 2.04 31.59 59.51

09/15/05 16:00:00.0 30.04 0.21 63.83 1.46 30.51 0.34 56.83 1.17 30.31 0.26 60.12 3.14 30.29 60.26

09/15/05 17:00:00.0 29.77 0.42 65.93 1.78 29.77 0.61 59.67 2.93 30.04 0.33 61.93 1.88 29.86 62.51

09/15/05 18:00:00.0 28.90 0.22 68.58 1.07 29.30 0.22 61.85 2.10 29.30 0.33 63.67 0.65 29.17 64.70

09/15/05 19:00:00.0 28.83 0.21 70.00 1.05 28.90 0.55 63.90 2.50 29.17 0.47 65.43 1.91 28.97 66.44

09/15/05 20:00:00.0 27.85 0.16 75.07 1.13 27.52 0.25 71.20 1.09 28.57 0.20 68.02 0.97 27.98 71.43

09/15/05 21:00:00.0 27.72 0.21 75.72 0.66 27.59 0.16 70.70 0.80 28.84 0.48 67.25 2.00 28.05 71.22

09/15/05 22:00:00.0 27.65 0.20 74.87 0.41 27.52 0.00 70.82 0.53 29.03 0.30 65.07 0.91 28.07 70.25

09/15/05 23:00:00.0 27.45 0.16 74.87 0.41 27.12 0.00 70.90 0.69 28.37 0.30 67.18 1.08 27.65 70.98

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Greenery Condition Places CoolingLoad (KWh)

Increase of Energy Usage (Compared

with Kent Ridge Road -Water Tank)

PGP Inside 6,486.62 14.30%Auditorium Engineering 6,298.47 10.98%

%67.923.922,6PGP Road%04.505.189,5SPORTS FIELD

COMPUTER CENTRE 5,993.55 5.61%TEMASEK HALL CAR PARK 6,038.81 6.41%MEDICAL SCHOOL CAR PARK 5,899.49 3.95%

%23.620.430,6BIO INFORMATICS%43.252.808,5ACOUSTICAL LAB

%042.576,5WATER TANK

SPARSE GREENERY

LESS DENSE GREENERY

DENSE GREENERY

COMPARISON OF COOLING LOAD ON TYPICAL DAY AMBIENT TEMPERATURE WITHOUT INTERNAL LOAD- CURRENT CONDITION

4,500.00

5,000.00

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Fig. 18. Cooling load in 10 points of different locations with current condition—without internal load.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–29702964

3.3. Computer simulation

3.3.1. ENVI-Met simulation

3.3.1.1. Daytime. Fig. 14 shows temperature profilesthroughout NUS environment for four different conditionsat 13.00 hours. In condition (A), it can be observed that thepresence of dense green area in the central region andmoderate green area around the campus clearly contributeto NUS low ambient temperature. This can be illustratedby ‘‘cool areas’’ indicated by blue and green color in thecentral region. However, areas near Faculty of Engineer-ing, University Cultural Centre and University SportCentre have been generally high in temperature. This isdue to lack of greenery, higher building density andparticularly presence of pavement at the sport field. Incontrast, it is clearly observed that the presence of ‘‘coolerareas’’ has disappeared due to removal of all green areas inNUS as shown in condition (B). The areas have become

much hotter in general as indicated by yellow and red colorrepresentation. It is noted that ENVI-Met simulationmodel assumes that the source of water in the soil is non-depleting. In reality, this is not the case, the water willbecome dry at some time and hence the temperature incondition (B) would be even higher. From Fig. 15(B) it canbe seen that there is no cooling effect in NUS environmentafter removal of greenery. In condition (C) most of thedense green areas in the central region have been replacedwith buildings where the rest of the greenery in NUS is keptunaltered. It is observed that the central region of NUS hasexperienced an increase in temperature and cooling effectof central green areas to NUS environment has consider-ably decreased. Generally, in comparison with currentcondition, the areas have now become hotter as indicatedby more yellow, orange and red color. Fig. 15(C) alsoshows that the cooling effect generated by dense greeneryin central region has been reduced. The increase of

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Fig. 19. Cooling load in 10 points of different locations with current condition—with internal load.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–2970 2965

temperature especially in the central region is due to highbuilding density and reduction of plants which eventuallycontribute to the reduction of cooling effects of thegreenery to the surrounding areas.

In contrast, by adding denser greenery to NUS environ-ment, it is clearly observed that NUS environment has nowbecome much cooler than current condition as representedin Fig. 14(D) and (A). The ‘‘hot spots’’ which initiallyoccur near Faculty of Engineering, University CulturalCenter and Sport center have now become much cooler asa result of cooling effects of much denser greenery in thecentral region of NUS and other areas.

3.3.1.2. Night-time. Temperature profiles and verticaltemperature distributions at 00.00 hours for four differentconditions in NUS environment are shown in Figs. 16and 17, respectively. By comparing all the four conditions,it can be observed that the presence of greenery is veryimportant in keeping low ambient temperature in NUS

environment. Without greenery, it is clearly seen incondition (B) that during night-time areas with highbuilding density, such as: Faculty of Art and SocialSciences, Faculty of Engineering, Faculty of Science andPGP residence are much hotter than the surrounding areas.This is due to the fact that the heat stored in the buildingsduring daytime start to radiate back to the environment,thus making the surrounding areas much hotter at night-time. However, with the presence of greenery, the coolingeffects produced by plants are able to neutralize this heatand even keep the areas cooler as shown in condition (A).Furthermore, the comparison also shows that the

cooling effects of the greenery areas in NUS environmentare affected by building density as indicated by condition(C). The higher the building–plot ratio, the less pro-nounced the cooling effects will be. This observation isclearly seen during night-time, where the heat stored in theconcrete mass in buildings start to be released to theenvironment. It is noticed that cooling effects still exist in

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Applying 100% Shrubs Applying 100% Trees

Applying 100%

Energy Savings of Cooling Load Compared with Current Condition

25.82% 53.67% 56.78%

COMPARISON OF COOLING LOAD ON 7th FLOOR ZONE WITHOUT INTERNAL LOAD - 3 DIFFERENT ROOFTOP GREENERY WITH CURRENT CONDITION

1,900.00

1,700.00

1,500.00

1,300.00

1,100.00

900.00

Current Condition Applying 100% Turfing

Applying 100% Turfing Applying 100% TreesShrubs

Fig. 20. Comparison of seventh floor zone cooling load in 10 points of different locations with different rooftop applications—without internal heat load.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–29702966

the central region of NUS due to dense greenery; however,the effect is not so prevalent to NUS environment ascompared to the heat radiated by buildings.

In contrast to condition (C), it is observed in condition(D) that by adding much denser greenery to NUSenvironment, the cooling effects generated are much moreprevalent than the heat by buildings. Whole NUSenvironment has substantially become much cooler asindicated by blue color representation. Cooling height toNUS environment can be seen in vertical temperaturedistribution as shown in Fig. 17(D).

3.3.2. TAS simulation

The calculated result of energy consumption for currentcondition is presented below. The hourly average ambienttemperature from the field measurement on 15th Septem-ber 2005 was inputted to the weather data, as seen inTables 2–4. In the Kent Ridge Road-Water Tank thetemperature was 27.65 1C and in the PGP complex29.76 1C. Thus, the difference was 2.11 1C.

In the first simulation, the internal heat load was omittedto see the impact of ambient temperature condition to thecooling load. The simulated result is presented in Fig. 18. Itis clearly seen that the ambient temperature has a

significant effect to cooling load of the building. Buildingwith the PGP ambient temperature has 14.30% highercooling load than the building in Kent Ridge Road-WaterTank area.In the second simulation, the internal heat load was

included, as shown in Fig. 19. The lowest cooling load,15,280.88 kWh, was the building along the Kent RidgeRoad-Water Tank area and the highest, 16,110.88 kWh, isthe building in PGP area. The difference of cooling load isabout 5.4%. The cooling loads of the other locations arewithin the range, follows the ambient temperature condi-tion of each location. Transforming the PGP condition tobecome similar to the Kent Ridge Road condition may bedifficult. However, by adding more trees as in the less densegreen area may cut the cooling load difference by 50%, tobecome only about 2.5%.The potential of rooftop garden application in NUS

building was also simulated. There were three types ofrooftop garden, 100% turfing, 100% shrubs and 100%trees. The R-values are 0.84, 2.216 and 1.429m2 k/W,respectively [13]. The simulated result is shown in Fig. 20.Similar to the previous simulation, for the first simula-

tion, the internal heat load was omitted to see theperformance of different rooftop greenery. In the first

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9.05% 18.85% 20.01%

7,000.00

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Current Condition

3 DIFFERENT ROOFTOP GREENERY WITH CURRENT CONDITION

COMPARISON OF OVERALL COOLING LOAD WITHOUT INTERNAL LOAD-

Fig. 21. Comparison of overall cooling load in 10 points of different locations with different rooftop applications—without internal heat load.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–2970 2967

simulation, there are two simulated cooling load value, thecooling load for the rooms below the roof (seventh floor)and the overall value.

Application of rooftop garden, shows the potential ofenergy saving. The rooftop garden has the potential ofenergy savings of 25.82%, 53.67% and 56.78%, byapplying turfing, shrubs and trees, respectively.

For the overall cooling load, energy savings may become9.08%, 18.85% and 20.01%, by applying turfing, shrubsand trees, respectively, as shown in Fig. 21. These resultsshow that rooftop greenery has potential to reduce thecooling load.

In the second simulation, the internal heat gain wasincluded. The simulated result is shown in Figs. 22 and 23.

The cooling load for the seventh floor zone has thereduction of 14.64% by applying turfing, of 29.96% byapplying shrubs and of 31.73% by applying trees as shownin Fig. 22.

The overall cooling load of the building with differentrooftop application is presented in Fig. 23. Energy savingsof overall cooling load may become 3.29% by applyingturfing, 6.73% by applying shrubs and 7.16% by applyingtrees, see figure below. These simulation results really show

that rooftop greenery has the potential for energy savings,where additional benefits may follow, such as lowering theambient temperature, providing a better view to theoccupants and so on.From these simulations, between applying shrubs and

trees, the energy savings does not seem too different. Thus,the application of shrubs on the rooftop is more reasonableto be applied, because application of trees may haveproblem with the structure of the building in carrying theadditional load.

4. Limitations and constraints

The following are the limitations and constraints inconducting this study:

(a)

The thermal satellite image data could be taken onlyduring the noon time due to the rotation schedule ofthe satellite. Thus, the UHI study by using the thermalimage could not be conducted which needs a mid-nightthermal image.

(b)

In the field measurement, there are limited numbers ofHOBO meter for data collection in order to make the

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Applying 100% Applying 100% Turfing

Energy Savings ofCooling Load Comparedwith Current Condition

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Applying 100% Trees

COMPARISON OF COOLING LOAD ON 7th FLOOR ZONE -

3 DIFFERENT ROOFTOP GREENERY WITH CURRENT CONDITION

3,400.00

3,200.00

3,000.00

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2,600.00

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Current Condition Applying 100% Shrubs

ShrubsApplying 100% Trees

Fig. 22. Comparison of seventh floor zone cooling load in 10 points of different locations with different rooftop applications.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–29702968

collected data more representative for the representa-tion of distribution greenery and building across thecomplex.

(c)

ENVI-Met simulation on rooftop greenery to get thetemperature difference as a result of indirect coolingeffect is not possible due to time and hardwareconstraints as the simulation generally takes 10 timeslonger than that of without rooftop greenery. Similareffect condition, that is introducing more greenery toNUS environment, is then simulated as an alternativemethod.

5. Conclusion

From thermal satellite image of NUS campus, it is seenthat, reddish color of thermal distribution are on andaround the buildings. The greenish color appears in highdense area of plantation and the yellowish in between theseareas. The buildings surrounded by or at the perimeter ofthe green area have better thermal distribution than otherbuildings away from dense green area, shown as yellowishcolor. It can be concluded that a building near or

surrounded by greenery has lower ambient temperaturethan the one away from the greenery.In the field measurement, it was found, on a typical day,

that the peak temperature difference between dense greenarea (Kent Ridge Road-Water Tank) and PGP residencecan be as high as 4 1C at around 13:00. When the timeapproaches mid-night, the temperature difference betweenthese two locations is about 3 1C. The temperaturedifference of daytime maximum among different locationsis very significant. It has a peak of 3.3 1C. The night-timeminimum temperature is generally very even, but goes upto nearly 23 1C at PGP residence. This verifies thehypothesis that the heat accumulated in the day is hardto dissipate during night-time due to large concentration ofbuildings and sparse plantation in PGP residence. Thisstudy also confirms the previous study by Wong [10] on theimpact of large greenery.In the ENVI-Met simulation, it is confirmed that,

firstly, presence of dense greenery in NUS environment isvery important in keeping low ambient temperature.Cooling effect produced by greenery will affect NUSmicroclimate positively as it will make NUS environmentcooler in general. Secondly, cooling effects of greenery on

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%51.7%37.6%92.3

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COMPARISON OF OVERALL COOLING LOAD -

16,000.00

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Shrubs

Energy Savings of Cooling Load Compared with Current Condition

Fig. 23. Comparison of overall cooling load in 10 points of different locations with different rooftop applications.

N.H. Wong et al. / Building and Environment 42 (2007) 2949–2970 2969

surrounding environment is affected by the buildingdensity. The higher the building density, the less pro-nounced the cooling effects will be. This is more clearlyobserved during night-time when heat stored in thebuildings during daytime start getting released to theenvironment.

NUS buildings also have the potential for rooftopgreenery application. It is observed that about 40% NUSbuildings are using concrete flat roofs. The TAS simulationshows that the cooling load for the seventh floor zone has thepotential reduction of 14.64–25.82% by applying turfing, of29.96–53.67% by applying shrubs and of 31.73–56.78% byapplying trees. The overall energy savings of cooling loadrange from 3.29–9.08% by applying turfing, 6.73–18.85% byapplying shrubs and 7.16–20.01% by applying trees. Byplanting trees and intensive rooftop system, the energysaving is much higher. This is confirmed by the previousstudy on the two different rooftop systems [10].

This study has shown the importance of greenery area inkeeping the NUS microclimate comfortable. Some im-provements may be considered by planting more trees inthe less dense greenery and especially in the sparse greenarea. The large green area can be considered as a large parkin the urban area, which has an impact on the surroundingenvironment.

6. Recommendations

In further research, a more comprehensive study can bedone by looking into the extent of the cooling effect of thedense green area along Kent Ridge Road to the surround-ing area. Some weather stations will be employed and thedata compiled into a climatic mapping using GIS system.This will serve as a very useful reference for masterplanning of future NUS campus.

Acknowledgments

This research was supported by the Department Buildingand Office of Estate and Development (OED), NationalUniversity of Singapore. The authors would like to expresstheir sincere thanks to Ms. Lina Goh for the permission ofconducting all the necessary methodology. Great apprecia-tion also goes to Ms. Helen Yip for providing all thedrawings as part of the computer simulation.

References

[1] Nichol J, Wong MS. Modelling urban environmental quality in a

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