SUSB Journal Title No. SUSB-2011/026
Technical Paper http://dx.doi.org/10.5390/SUSB.2011.2.4.323
A Climatic Responsive Urban Planning Model for High Density City: Singapore’s Commercial District Wong Nyuk Hien, Steve Kardinal Jusuf, Rosita Samsudin, Anseina Eliza, and Marcel Ignatius*
Abstract Local climate condition and urban morphology affect air temperature generated within urban canopy layer which related to urban heat island (UHI) intensity and later impacts on outdoor thermal comfort and urban energy usage. Climatic responsive urban planning by careful consideration on urban morphology parameters of urban corridor width, building height, urban surface materials, sky view factor (SVF) and vegetation help to improve urban environment quality. This study mainly focuses on commercial district and observes impacts of various urban structures configurations towards air temperature by interpolating climatic and urban morphology predictors. The urban structures indeed show relation with level of air temperature generated although vegetation also contributes in reducing air temperature through its evapotranspiration process. Therefore the understanding of relation between urban morphology with thermal performance and UHI benefits in future urban planning and development. Keywords: Urban morphology, Temperature map, Urban heat island (UHI), Singapore’s commercial district
1. INTRODUCTION
Cities are growing towards megacities with higher density urban planning, narrower urban corridors and more highrise urban structures. This urban transformation causes day-time and night-time urban heat island (UHI) which leads to declining of urban environment quality. Earlier studies show strong relation between urban morphology and increasing air temperature within cities center. Urban structures absorb solar heat during day-time and release it during night-time. Densely built area tends to trap the heat when it is released from urban structures into urban environment, increases urban air temperature compared to surrounding rural areas and causes UHI effect. UHI affects street level thermal comfort, health, environment quality and may cause increase of urban energy demand. In a built environment at micro-scale, buildings and vegetation influences the incident solar radiation received by urban surface. This is determined by the openness of an urban surface which is called as sky view factor (SVF) as mentioned by Cleugh in his study [1]. SVF explains the percentage of a point’s field of view that is occupied by the sky as opposed to the buildings, trees or any other objects in the landscape. Oke -1987 [2] also related both SVF and height-to-width ratio of urban canyon with UHI intensity. The lower SVF value the higher urban air temperature. Geographically, Singapore is located between latitudes 1o09' North and 1o29' South, longitudes 103o36' East and * Corresponding author. E-mail address:
[email protected] Article history Received November 4, 2011 Accepted December 23, 2011 ©2011 SUSB Press. All rights reserved.
104o25' East. By its location, Singapore falls within hot humid climate region with characteristics of uniform high temperature, humidity and rainfall throughout the year [3]. Singapore as the most developed country within Southeast Asian region has been experiencing rapid urban development. Commercial district is one of the highly developed areas which allows higher building site coverage and plot ratio with rows of high-rise buildings for residential and commercial usage to encourage the country's strong economic growth. Current Singapore’s urban planning policy for commercial district allows high rise developments with plot ratio ranging from 5 to more than 11.2 which can be translated to building height ranging from 25 to more than 50 storeys height. A study conducted by Wong [4] observed from the satellite image that UHI in Singapore is seen during daytime with ‘hot spots’ were identified on commercial districts besides airport and industrial areas. However, ‘cool spots’ were identified as well on large parks, the landscape in between housing estates and the catchment area. Jusut et al. [5] studied the relation between land use and ambient temperature as shown in Fig.1. It is seen that during daytime commercial district experienced lower temperature compared to other land uses. But during night-time, it experienced higher temperature. Local climate condition is the existing factor that permanently affecting macro and micro climate condition. Katzschner [6] mentioned that climate is an ever existing factor in a built environment and the study about climate condition is purposed to improve the climate condition and to reduce the negative micro climate effects. Mills [7] proposed that examining the relationship between urban forms and climate can employ the results of urban climatology
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Nyuk Hien Wong is Associate Professor in the Department of Building,
National University of Singapore. His area of expertise and research interests includes urban heat island, urban greenery, thermal comfort in the tropics and building energy simulation. He is the principal investigator of a number of research projects in collaboration with the various government agencies in Singapore. Prof. Wong has published more than 150 international referred journal and conference papers and was the co-authors of 3 books on r ooftop and urban greenery and has been invited to deliver keynote papers and research findings in various conferences and symposiums. He has also been invited to serve in the various advisory committees both locally and internationally. Steve Kardinal Jusuf has a Ph.D. degree in Building Science from the
Department of Building, National University of Singapore. Currently he is a Research Fellow at Centre for Sustainable Asian Cities, NUS. His research interests include urban microclimate and urban climatic mapping with Geographical Information Systems. He has worked in a number of research projects with various Singapore government agencies, mainly on urban climatic mapping for sustainable urban development. Rosita Samsudin is Research Assistant at Centre for Sustainable Asian
Cities, NUS. She is architect in practice and holds master degree in building science from Department of Building, NUS. Her research interests include urban heat island, urban climatic mapping, outdoor thermal comfort, sustainable building and urban development. Anseina Eliza is a Master Graduate in Building Science at Department of Building, National University of Singapore. Topic of this paper embarks from her Independent Study research, with Dr. Wong Nyuk Hien as her
supervisor, which highlights the building morphology and density effect on urban temperature. Currently, she is working at green building consultant. Marcel Ignatius is currently a PhD candidate at Department of Building, National University of Singapore, under Prof. Wong Nyuk Hien supervision. The focus of his research is mostly on urban climatic mapping and temperature model for urban morphology in Singapore. He was a research assistant in Centre of Sustainable Asian Cities (CSAC) at NUS in 2009, and he has done his Master Degree in Building Science from the same university in 2008.
into urban design guidelines. To improve the urban environment quality and mitigate UHI effect, a climatic map of an urban area is possible to be developed by using Geographic Information System (GIS) platform with analysis on different information layers. Climatic mapping method has become widely used for
urban planning from macro to micro level and can be used as reference for future urban planning and development. The objective of this study is to see how different design options can be explored on developing a block within a highly dense urban area, along with their impact on the related urban microclimatic condition (in this case, urban temperature on pedestrian level). The design options variation are limited on varying building massing and building physical dimension accordingly, within the same plot ratio control.
2. SCREENING TOOL FOR ESTATE ENVIRONMENT EVALUATION (STEVE TOOL)
STEVE has been developed based on the air temperature prediction models. These prediction models were based on the empirical data collected over a period of close to 3 years as part of the development of an assessment method to evaluate the impact of estate development, which includes the assessment method of existing greenery condition [8] and greenery condition for a proposed master plan in an estate development [9]. In the development of the empirical model, air temperature data that has been gathered in the previous studies were combined with the most recent data, which includes estatewide and canyon types of measurements. m easurements. The measurement points cover various types of land uses, including residential, commercial, business park, education, industrial, park, open space and sport facility. Daily minimum (Tmin (Tmin), ), average (Tavg (Tavg ) and maximum (Tmax) Tmax) temperature of each point of measurements were calculated as the dependent variable of the air temperature prediction model. The independent variables for the models can be categorized into: Climate predictors: daily minimum (Ref Tmin), average (Ref Tavg) and maximum (Ref Tmax) temperature at reference point; average of daily solar radiation (SOLAR). For the SOLAR predictor, average of daily solar radiation total (SOLARtotal) was used in Tavg models, while average of solar radiation maximum of the day (SOLARmax) was used in the Tmax model. SOLAR predictor is not applicable for Tmin model. These data are obtained from the weather station. Urban morphology predictors: percentage of pavement area over R 50m surface area (PAVE), average height to building area ratio (HBDG), total wall surface area (WALL),
Fig.1 Urban Heat Island profile in Singapore (Source: Jusuf et al., 2007)
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Fig.2 Sample of urban area measurement point in influence area radius of 25 m, 50 m, 75 m and 100 m. Source [8]
Green Plot Ratio (GnPR), sky view factor (SVF) and average surface albedo (ALB). These data are provided by the government agency and cross-checked by field survey. Before the model was developed, the radius of influence area was determined. A radius of 50 meter was deemed as a suitable one after a series of influence area study by comparing comparing radius radius value value from 25 25 – 100 m (see Fig.2). The temperature models were then developed by examining the variables regression coefficient values and their correlations with the dependent variables. Wind speed, one of the most common variables, was excluded in the model development, since the models focus on calm day conditions conditions (wind speed < 3 m/s). Meanwhile Meanwhile for another common variable, altitude was excluded from the model development since the data collected showed altitude has a very little influence on air temperature condition. In the first stage of model development, trend analysis was done to identify and discuss the behaviour of the models’ variables (based on the data collected on field measurement),
by examining the variables’ regression coefficient values and their correlations with the dependent variable. Not all of the independent variables are significant. However, it is important to analyze how these variables behave in determining the air temperature. The next stage is to develop the air temperature prediction models that use only the significant variables. The air temperature regression models were developed based on the data collected over a period of close to 3 years. It is necessary to validate the models with another period of measurement data, which in this case, with fairly clear and calm day conditions (wind speed < 3 m/s). The air temperature prediction models can be written as follows: o .061 + 0.83 0.839 9 Ref T min .004 PAVE PAVE (%) (%) Tmin (oC) = 4.06 min ( C) + 0.004 – 0.19 0.193 3 GnPR – GnPR – 0.02 0.029 9 HBDG + 1.339E-06 1.339E-06 WALL WALL (m2)
2.347 7 + 0.90 0.904 4 Ref T avg (oC) + 5.78 5.786E 6E-0 -05 5 Tavg (oC) = 2.34 SOLARtotal (W/m2) + 0.007 PAVE (%) – 0.06 0.06 GnPR – GnPR – 0.015 HBDG 0.015 HBDG + 1.311 1.311E-0 E-05 5 WALL (m2) + 0.633 SVF
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Table 1. Matrix of different building configurations on each block MASSING LOCATION
BLOCK AREA (m2)
PLOT RATIO
GFA GFA (m2)
1
2
3
5
10
16
24
24
HEIGHT (STOREYS) 80
80
80
36 2
FOOTPRINT FOR 1 MASSING (m ) BLOCK A
15600
11.2
174720
2184.00
1092.00
728.00
970.67
728.00
455.00
BLOCK B
9180
11.2
102816
1285.20
642.60
428.40
571.20
428.40
267.75
BLOCK C
8190
11.2
91728
1146.60
573.30
382.20
509.60
382.20
238.88
BLOCK D
8775
11.2
98280
1228.50
614.25
409.50
546.00
409.50
255.94
BLOCK E
12920
11.2
144704
1808.80
904.40
602.93
803.91
602.93
376.83
BLOCK F
5550
11.2
62160
777.00
388.50
259.00
345.33
259.00
161.88
BLOCK G
5550
11.2
62160
777.00
388.50
259.00
345.33
259.00
161.88
o 7.542 2 + 0.68 0.684 4 Ref T max 0.003 3 SOLAR SOLARmax Tmax (oC) = 7.54 max ( C) + 0.00 (W/m2) + 0.005 PAVE (%) (%) – 0.016 .016 HBDG HBDG + 6.777E 6.777E-06 -06 2 WALL (m ) + 1.467 SVF + SVF + 1.46 1.466 6 ALB
Since it is impossible to put the all the theoretical background and prediction model development into this paper, the author will only underline the essential elements of the STEVE tool, while a more detailed explanation and data validation can be read from the related paper, which can be found in [8-11].
3. METHODOLOGY
Temperature map for Singapore’s commercial district in this study is developed by overlaying layers of urban morphology parameters and predicted T max max, T avg avg and T min min using GIS platform. T max max represents maximum temperature during daytime between and T min min represents minimum temperature during night-time. Predicted temperature are calculated by interpolating historical climatic parameters of temperature and solar radiation obtained from local weather station with urban morphology predictors of building height, exposed surface area, average albedo and sky view factor (SVF). This study compares the existing urban morphology condition with proposed possible scenarios based on current Singapore’s urban planning policy for commercial district. Models of 6 types massing configuration consist of 1 mass, 2 masses, 3 masses, 5 masses, 10 masses and 16 masses are developed and to be observed on 7 blocks in commercial district which presently is densely built and have allowable plot ratio more than 11.2 [12], namely block A, B, C, D, E, F and G. By configuring different massing configuration, various building footprints and building heights are achieved. Building footprint determines urban corridor width and horizontal urban density are achieved while building height contributes in sky view factor (SVF). Table 1 and Fig.3 shows the 6 type massing configuration used in this study. Total of 9 measurement points, out of other measurement points allocated within commercial districts, within 50 meter radius buffer are distributed around the selected blocks and
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Fig.3 Selected 7 blocks in commercial district with plot ratio 11.2
Fig.4 Types of different building configuration located on 7 blocks in Singapore’s commercial district
predicted T max max, T avg avg and T min min are calculated by STEVE tool. This study mainly focuses on effect of urban structures towards urban air temperature therefore greenery variable
W. Hien, S. Jusuf, R. Samsudin, A. Eliza, and M. Ignatius
is not included in the predicted temperature calculations. The open areas in between buildings blocks are assumed as pavement areas. However it is confirmed from many earlier studies that greenery contributes greatly in reducing the urban air temperature by the trees shading and vegetation evapotranspiration process.
4. FINDINGS
Temperature map of predicted T max max, T avg avg and T min min for all scenarios show that there are changes on air temperature accordingly by changing the buildings configuration and density.
4.1 Temperature maximum ( T max ) map Temperature map T max max in Fig.5 indicates higher temperature for some areas in type 1, 2 and 3 compared to type 4, 5 and 6. Building configurations in type 1, 2 and 3 allow more
Fig.5
T
open spaces and receive more direct solar radiation during day-time thus increase air temperature within urban canopy layer. Building height also contributes in reducing T max max, benefits from the building shading that falls onto pavement area, as shown in some area which indicate lower temperature in type 1, 2 and 3. However, particular areas in type 1 still show higher temperature especially in between the buildings which rather far apart. This confirms Oke’s study [2] on correlation between ratio of building height and urban corridor width with urban air temperature. Building configuration in type 4, 5 and 6 results in lower temperature considering effect of shading that falls onto pavement and lower SVF value because of the urban density setting regardless lower building height planned for these types. Predicted T max max also takes account of exposed surface area therefore lower building may possibly have less exposed surface area.
temperature map on existing block condition compared with 6 types of urban structures configuration and density
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4.2 Temperature average ( T avg ) map Similar types of building configuration are modeled to calculate predicted T avg avg . Temperature maps in Fig.6 show that type 1, 2 and 3 indicate lower air temperature compared to existing condition and the other 3 types and it seems that reduction of building height impacts on the increasing of T avg avg as shown in type 4, 5 and 6. However, amongst the last 3 building configurations, type 4 which has the lowest building density but highest building height indicates the lowest air temperature. Temperature map T avg avg also confirms correlation between ratio building height and urban corridor width with SVF value which affect amount of solar radiation coming into urban area. Solar radiation is one of climatic predictors that determine the level of air temperature generated within urban canopy layer.
4.3 Temperature minimum ( T min) map From Fig.7, it can be seen that type 1, 2 and 3 with lesser density of building configuration have lower air temperature compared to existing condition, type 4, 5 and 6. Sparsely planned urban structures allow heat released from building surface to go up and leave urban canopy layer. Inversely, higher density building configurations seem to trap the heat within urban canopy layer and result in higher air temperature which confirms the presence of potential UHI effect. In this study type 1, 2 and 3 have the highest building height compared to type 4, 5 and 6 therefore type 1, 2 and 3 allow more open spaces compared to the other types.
5. CONCLUSIONS
Besides local climate condition, urban morphology predictors affect air temperature generated within urban canopy layer which later impact on UHI intensity. Building
Fig.6 T temperature map on existing block condition compared with 6 types of urban structures configuration and density
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Fig.7 T
temperature map on existing block condition compared with 6 types of urban structures configuration and density
density and building height are some urban morphology predictors observed in this study. Urban configuration with lower building density allows more open spaces that potentially increases air temperature during day-time due to the amount of solar radiation coming into urban canopy layer. But a sparsely planned building helps for the heat that is released from urban surfaces into urban area to go up and leave urban canopy layer. Inversely, densely planned urban area provides more shading and reduce amount of solar heat absorbed thus potentially reduce air temperature during day-time but it traps the heat released during night-time and causes higher air temperature compared to surrounding areas which less densely planned. Combination of lower density urban configuration with higher building height confirms in to reducing air temperature
during night-time as it allow more open space and allows the heat that is release into urban area to go up and leave urban canopy layer. Proportionally planned building height and urban corridor width affect in minimizing SVF value and solar heat radiation coming into urban canopy layer which help to lower air temperature during day-time. Figure 7 compiles the differences of T max max, T avg avg and T min min observed between existing condition on 7 blocks in Singapore’s commercial district with 6 types of different building configuration proposed. It shows that there is a threshold of optimum density that potentially applied for these blocks. In general all building configuration types reduce existing condition air temperature. However, type 5 and 6 do not seem to have significant contribution. Therefore, it can be concluded that urban configuration with 1 to 5 buildings are effective in reducing UHI effect in the context
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REFERENCES
Fig.8 Average temperature difference on massing configuration types
of blocks used in this study. However, this threshold may not be applicable for other blocks depending on the ground area and allowable plot ratio therefore further detailed study needs to be conducted for other blocks in order to observe particular optimum threshold. This parametric study confirms that understanding and application of climatic responsive urban planning contributes greatly in improving thermal performance within urban area which in further impacts on outdoor thermal comfort, health, air quality and urban energy usage. Limitation to this study is that vegetation variable and urban wind ventilation are not included thus further detailed study can be conducted for more comprehensive urban thermal performance findings and analysis.
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[1] Cleugh, H., Urban Urban Climates in “Future “Future Climates Climates of The World: World: A Modelling Perspective, edited by Henderson-Sellers, A., Amsterdam, New York, Elsevier, 1995, pp.488. [2] Oke, T.R., T.R., Boundary Boundary Layer Climates, Climates, London, London, Routledge, Routledge, 1987. [3] www.ne www.nea.g a.gov ov.sg .sg [4] Wong, N.H., Study of Rooftop Gardens in Singapore, Singapore, 2002. [5] Jusuf, S.K et al., al., The Influenc Influence e of Land Use on The The Urban Heat Island in Singapore, Habitat International , Vol. 31 (2007), pp.232-242. [6] Katzschner, L., The Urban Climate as A Parameter Parameter for Urban Development, Energy and Buildings, Vol. 11 (1988), pp.137147. [7] Mills, G., G., The Radiative Radiative Effects Effects of Building Building Groups on Single Single Structures, Energy Structures, Energy and Buildings, Buildings, Vol. 25 (1997), pp.51-61. [8] Jusuf, S.K. and and Wong, Wong, N. H., An Assessment Assessment Method Method for Existing Greenery Conditions in a University Campus, Architectural Science Review 51 (2008), pp. 116-126. [9] Jusuf, S.K. and Wong, N. H., GIS-based GIS-based greenery greenery evaluation on campus master plan, Landscape and Urban Planning 84 Planning 84 (2008), pp. 166–182. [10] Jusuf, S.K. and Wong, N. H., Development Development of empirical models for an estate level air temperature prediction in Singapore, Second International Conference on Countermeasures to Urban Heat Islands, Islands, Berkeley, United States, 2009. [11] Wong, N.H., Jusuf, S.K., Syafii, Syafii, N.I., Chen, Chen, Y., Y., Hajadi, N., Sathyanarayanan, H. and Manickavasagam, Y.V., Evaluation of The Impact of The Surrounding Urban Morphology on Building Energy Consumption, Solar and Energy, Energy, In Press. [12] [12] www.ur www.ura.g a.gov ov.sg .sg
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