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Building and Environment 46 (2011) 1174e1183

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Building and Environment

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Urban planning indicators, morphology and climate indicators: A case study fora north-south transect of Beijing, China

Caijun Zhao a, Guobin Fu b,c,*, Xiaoming Liu d, Fan Fu e

aChina Urban Construction Design & Research Institute, Beijing 100029, PR ChinabKey Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences,Beijing 100101, PR ChinacCSIRO Land and Water, Private Bag 5, Wembley, WA 6913, Australiad School of Landscape Architecture, Beijing Forestry University, 35 Qinghua East Road, Beijing 100083, PR Chinae School of Architecture, North China University of Technology, 5 Jinyuanzhuang Road, Beijing 100041, PR China

a r t i c l e i n f o

Article history:Received 7 October 2010Received in revised form4 December 2010Accepted 6 December 2010Available online 16 December 2010

Keywords:Urban planning indicatorsUrban morphologyGreen cover ratioSurface temperatureClimate change adaptation

* Corresponding author. CSIRO Land and Water, P6913, Australia.

E-mail address: Guobin.Fu@csiro.au (G. Fu).

0360-1323/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.buildenv.2010.12.009

a b s t r a c t

Eleven sites, representing different urban morphologies across central Beijing, are used to simulate urbanheat island effects and explore the relationship between urban planning indicators and climate indica-tors such as daily maximum and minimum surface temperatures. The results indicate that mesoscaleurban planning indicators can explain the majority of the urban climate differences among the sites. Forexample, green cover ratio and floor area ratio can explain 94.47e98.57% of the variance for dailymaximum surface temperature, green cover ratio and building height can explain 98.94e99.12% of thevariance for daily minimum surface temperature, and floor area ratio, green cover ratio and buildingdensity together can explain 99.49e99.69% of the variance for time of peak surface temperature.Furthermore, green cover ratio is identified as the most significant urban planning indicator affecting theurban thermal environment.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

The process of urban planning has close relationship withclimate and as such Simonds suggests that climate should bea fundamental consideration, given the central purpose of planningis to create an environment suited to human needs [1]. Theimportance of climate to urban planning has been considered a keyfactor over many decades [2e9]. In China, climate considerations inurban planning and design can be traced back more than 3500years ago [7,9,10], when “Feng-Shui” emerged and played a majorrule not only in finding desired locations, but also in planning,designing and managing settlements and individual buildings. Oneof the basic ideas of “Feng-Shui” is to find or create a site withperfect local climate which inhabitants will benefit from, by usingnatural elements, e.g. mountain, river and forests, and man-madeelements, e.g. layout of road system, green space and buildings [11].

rivate Bag 5, Wembley, WA

All rights reserved.

As the process of urbanization produces radical changes in thenature of the surface and atmospheric properties of a region [8,12],the interaction between atmosphere and human settlement resultsin energy flux [13] (Fig. 1) and local climate variations betweenurban and rural areas [14], as well as intra-urban areas [5,8,15e19].These have been investigated in the urban climate and urban plan-ningdisciplines for several decades. It follows that urban climate canbe considered as anthropogenic, i.e. a response to urban develop-ment. Oke defined four significant controls on urban climate [20],namely, urban structure (dimensions of the buildings and the spacesbetween them, street widths and spacing [3,21,22]), urban cover(fractions of built-up, paved, vegetated, bare soil andwater [15e17]),urban fabric (construction and naturalmaterials [22,23]), and urbanmetabolism (heat, water, and pollutants due to human activity).These four controls, playing important roles in creating certainurban climatic environments, are all related to urban morphology.

There are close relationship among urbanplanning,morphology,climate and global climate. Both global and urban climates affecturban morphology, inhabitants’ health, comfort, social life, andenergy consumption, as climatic variables such as solar radiation, airtemperature and wind are vital aspects of the functional andpsychological components of a living place [4]. Urban areas will

Fig. 1. Schematic depiction of radiation and energy fluxes over rural (left) and urban (right) landscapes on a clear day. The width of the arrows approximates the relative size of theflux (adapted from [12,13]).

Fig. 2. Climate averages for Beijing from 1961 to 1990 (Data from: China Meteoro-logical Data Sharing Service System) *wind velocity (m/s) is multiplied by 5 in order toshow seasonal cycle.

C. Zhao et al. / Building and Environment 46 (2011) 1174e1183 1175

therefore experience great challenges due to global climate vari-ability and changes imposed, for example, by global warming.

Correspondingly urban planning determines urbanmorphology,influencing modes of living and impacting on urban climate. Soundurban planning is crucial globally, for aesthetics, efficiency, and theurban climatic environment. In addition, the integrated effect ofurban climate can influence global climate, for example the urbanheat island phenomenon has resulted in changes in climatic meanand variability at local, regional, national, and global scales [24e27].

Urban climate is a crucial factor not only influencing regional andglobal climates but also urban liveability. Urban climate can bemodified and improved to fulfil resident’s needs by urban planningmeans [5,28]. Therefore the incorporation of urban climate knowl-edge in the urban planning process becomes crucial.

Unfortunately, however, climate knowledge currently has a lowimpact on the urban planning process, although the majority ofthose involved in the urban planning process are aware of urbanarea local climate issues and understood that urban climate couldbe influenced by strategic urban planning [4]. The major reasonsidentified [4] include lack of easily accessible techniques andliterature and lack of confidence in their knowledge. In practice,planners seldom have the time or training to transform complexscientific concepts into planning, relying instead on easily acces-sible techniques such as relatively straightforward guidelines andvisual graphs.

Zhang et al. (2009) have used the scaling of impervious surfacearea and vegetation as indicators to simulate urban land surfacetemperature [29]. The purpose of this study is to explore the rela-tionship between urban planning indicators highly utilized inChina and urban climate indicators. The urban planning indicatorsare normally determined in the very beginning of planning, andserve as a basis for the entire planning and design process. Indi-cators used in this study include floor area ratio, building density,building height limit, green space ratio, green cover ratio, andparameters for between building distance. Section 2.6 providesdetailed definitions for each of these indicators. Daily maximumand minimum surface temperatures and the timing of peak dailytemperature are selected as climatic indicators. Beijing, the capitalcity of China, is presented as a case study. Results can providea reference for urban planners to understand which urban planningindicators couldmodify the local/urban surface temperature and itspeak timing. It could potentially contribute to climate change

adaptation, which is currently a research focus in global climatewarming studies.

2. Methodology and dataset

2.1. Case study area

Beijing (39.8� N,116.5� E), the capital city of China, covers an areaof 16,410 km2, with a population of 17million registered permanentand temporary residents in 2006 [30]. It has a monsoon-influencedhumid continental climate characterized by hot and humidsummers and generally cold, windy and dry winters (Fig. 2). Beijinghasbeen settled formore than3000years, including850years as thecapital. Intensive urbanization during recent decades has greatlymodified the urban thermal environment [31,32]. Developing Bei-jing into a ‘livable city’ is one of the four main goals in the 2004 to2020 Master Plan of Beijing [33].

Central Beijing (Fig. 3) was chosen for this study because 1) itspolitical, cultural and economic relevance [33]; 2) by 2020, 47.2% ofBeijing’s population (8.5 million) will be living in this area [33],

Fig. 3. Map of central Beijing, and location of the eleven sample sites. Among those eleven sites, four are in the Old Town, including (2)Qianmen, (3)Tiananmen Square, (4)JingshanPark, and (5)Gulou; and five sites are in the Core Area, (1)Yang Bridge, (6)Anhua Bridge, (7)Yuantucheng Park, (8)Olympic Park, and (9)Asia Olympic Village; as well as two sites inthe Eco-protection Area, (10)Olympic Forestry Park, and (11)Jiutai Road.

C. Zhao et al. / Building and Environment 46 (2011) 1174e11831176

although it only covers 6.6% (1,085 km2) of Beijing’s total area; 3) itconsists of four characteristic precincts, Old Town, Core Area,Suburbs, and Eco-protection Area; and 4) it is located on a plainwith an average altitude of 20e60 m above sea level, thereforetemperature difference due to altitude can be ignored.

The Old Town, representing the unique scenery of traditionalBeijing, features Siheyuans (courtyards with buildings on four sides)and Hutongs (lanes between Siheyuans), and has a 7.8 km south-enorth central axis. Many buildings have a history of more than700 years. Comprehensive protection planwill be applied to the OldTown according to Master Plan of Beijing [33]. In contrast the CoreArea and Suburbs are mainly occupied by high-rise new buildings,supporting many core urban functions and accommodating about82% of the central city population [33]. The Eco-protection Area, asits name implies, focuses on ecological environment conservationand so urban development for residence, commerce and industry isunder strict control.

The 7.8 km southenorth central axis had been set when Beijingwas chosen as the capital city 800 years ago. It is probably thelongest and oldest city axis in the world and one of the mostimportant icons of Beijing. This axis has been extended furthernorth with the Asia Games in 1990 and Olympic Games in 2008. Itrepresents a temporal and spatial axis of the development of Bei-jing over the last several decades and future. Running through allfour urban functional areas (Old Town, Core Area, Suburban andEco-Protection Area) it is representative of almost all urbanmorphologies. Thus sample sites along this axis are used in thisstudy.

2.2. Selection of sample sites

The thermal environment within an inhabitant’s comfortablewalking distance (around 500 m) plays an important role in theirquality of life, so a sample size of 500 m in radius (circle area

0.785 km2) is used in this study. Previous studies have mainlyfocused on the effects of different land use on surface temperature[5,15e17,19]. However sample site selection for this study was notrestricted by land use type as boundary lines (physical or other-wise) between different land uses do not stop environmentaltransfers (e.g., due to advection) [34]. Moreover, the compositionof different land-use categories is more crucial for inhabitants,given the adjacent varied land use categories they interact with.

Eleven sample sites, with varied land use categories and urbanlandscape, were selected along Beijing’s central northesouth axis,covering a wide range of urban morphologies (Fig. 3). Four samplesites are located in the Old Town, namely Qianmen, TiananmenSquare, Jingshan Park and Gulou. Qianmen is comprised ofSiheyuans and Hutongs. Tiananmen Square is probably the largesturban open-square in the world. Jingshan Park, originally animperial garden, is located immediately north of the ForbiddenCity, with huge well established trees. Gulou is the north end oforiginal central northesouth axis. Unlike Qianmen, the Gulouregion is fully established with tree canopies along streets andlakes.

Five sites are located in the Core Area, from south to north YangBridge, Anhua Bridge, Yuantucheng Park, Olympic Park and AsiaOlympic Village. Yang Bridge and Anhua Bridge, along the southand north 3rd ring road, are chosen in order to study the impact ofthe ring road on the surrounding thermal environment. Yuantu-cheng Park is a long narrow park with a canal and lush vegetationchosen to investigate the impact of such parks on the environmentof adjacent areas. Olympic Park is where the 2008 Olympic Gamestook place, which has become another important icon in Beijing.The site includes the National Stadium and National AquaticsCenter. Asia Olympic Village consists of a cluster of high recentlybuilt residential and office towers, as well as large green space (upto 4.2 ha). Two sites are chosen in the Eco-protection Area, OlympicForest Park and Jiutai Road. Olympic Forest Park is located to the

Table 1Reference temperatures (�C) adopted in the study.

99th 95th 90th Median Mean 10th 5th 1st

Daily mean temp. 32.10 30.40 29.50 26.25 26.13 22.80 21.60 19.10

C. Zhao et al. / Building and Environment 46 (2011) 1174e1183 1177

north of Olympic Park, with an area of 680 ha, probably the biggesturban park in Asia. The Jiutai Road site features low density housingsurrounded by farmland.

2.3. Datasets

Twoprimary data sourceswere used in this study: a)Google Earthmap for land cover category analysis, accessed on 24th October 2003.Since both Olympic Park and Olympic Forest Park were still underconstruction at that time, theirGoogleEarth imageof 2008, after bothhad been completed, was used for these two sites; b) daily meteo-rological data from Beijing station (54511) from 1999 to 2008. Theclimate variables used include daily mean, maximum and minimumair temperatures. Beijing has a hot humid summerwhich is warmingas climate changes [32,35], and previous studies indicate that thecontrasts between urbanerural and intra-urban surface tempera-tures often reach their highest in the warm season [14,36], thereforethis study focuses on the urban thermal environment in summer(June, July, and August). The summer daily mean air temperatures often years, 1999e2008, are used as reference temperatures inputs fora surface energy balance model (Section 2.4). In order to investigatethe differences in maximum surface temperature for various sitesunder different dailymean air temperature scenarios, eight referencetemperatures are adopted in this study (Table 1).

2.4. Surface energy balance model

The surface energy balance based model, developed by Tso[37,38] and improved byWhitford et al. [15], was used in this studyto model the daily maximum and minimum surface temperaturesand the timing of peak temperature occurrence. The equation ofenergy balance is

R ¼ Hþ LEþ GþM (1)

Where R is the net radiation flux, H is the sensible heat flux to theair, LE is the latent heat flux, G is the conductive heat flux into thesoil, and M is the heat flux to storage in the built environment[37,38], which is taken as

M ¼ mcCcdT0dt

; (2)

Fig. 4. Land cover map for Gulou (lef

where mc is the building mass per unit surface area, Cc is thespecific heat of concrete, dT0/dt is the rate of change of surface airtemperature T0 [37,38]. This model expresses the surface energybalance of an area in terms of its surface temperature and usesa reference temperature (such as daily mean air temperature or 30-year long-term mean of daily air temperature) and urbanmorphology indicators, such as evaporating area (Ef) and built area(Bf), to simulate the urban heat island (UHI) impacts on urbansurface temperature [15]. Daily maximum surface temperaturegenerally declines in evaporating (i.e. green space) and built areas,as heat is removed from the surface in the day by evaporation fromvegetation and is taken up by buildings. Daily minimum surfacetemperature rises in built areas, because the heat stored in build-ings during the day is re-radiated during the night [17].

2.5. Land covers categories

Nine different land cover categories are identified to meetmodel input requirements: building, other impervious, tree, shrub,mown grass, rough grass, cultivated, water and bare soil/gravel[15,16]. There are two main methods for determining land covercategories: above-the-canopy and under-the-canopy [26]. Theabove-the-canopy method is used in this study as tree canopiesplay an important role in reducing surface temperature andincreasing humidity in urban areas [36,39]. The further classifica-tion of vegetation into several categories, besides tree, allows us toinvestigate the contribution of urban area vegetation distribution tosurface temperature.

Height of buildings is an important factor for urban morphologyand urban planning, as well as for urban climate. Therefore buildingcategory is further subdivided into 3 sub-categories based on to thenumber of floors: 1e3 floors, 4e9 floors, and 10e30 floors(including a few sites of more than 30 floors). On-site inspectionwas carried out to determine building height.

Land cover can be analyzed using a stratified random samplingtechnique [16,26] or by placing a grid (20m� 20m) over the 500mradius circle sample sites and recording the land cover categoriesunder the mid points in each of the 1957 squares. The secondmethod was applied in this study, not only because it results inpotentially lower errors [17], but also the resulting maps (Fig. 4) canbe used in demonstrating the spatial distribution of each land coverfor all sample sites.

2.6. Major urban planning indicators in China

Urban planning indicators are essential for planners and deci-sion makers to control urban morphology and the intensity of

t) and Yuantucheng park (right).

C. Zhao et al. / Building and Environment 46 (2011) 1174e11831178

urban development. The major urban planning indicators used inthis study are:

Floor Area Ratio (Far) is the ratio of the total floor area of thebuilding to the area of the land on which it is located. It is a majorparameter showing development intensity.

Building Density (Bd) is obtained by dividing the footprint area ofall buildings by the area of the land on which they are located.

Building Height Limit (Bh) is the height limit of buildings.Green Space Ratio (Gsr) is the ratio of the total area of all green

spaces (under-the-canopy) to the land. Gsr is an important para-meter indescribingurban surface cover,which is strictlycontrolled inChina.

Green Cover Ratio (Gcr) is the ratio of the total area of all greenspaces (above-the-canopy) to the area of the land. There is alwaysa difference between Gsr and Gcr. Gcr seems more appropriate forurbanmeteorological research because it affects urban climate suchas radiation and surface temperature. It is termed Ef when used asan input variable to the surface energy balance model.

Parameters forbuildingdistanceareused todetermine theminimumspacing between adjacent buildings, especially for multistory build-ings, including sunshine distance, fireproof distance, and ventilation

Fig. 5. Modeled daily maximum surface temperatures (a); in descending order when referen(c); timing of peak surface temperature (d).

distance. These are mandatory requirements in the urban planningprocess in China.

3. Results and discussions

3.1. Surface energy balance model results

The surface energy balance model is used with eight daily meanreference temperatures (percentiles listed in Table 1) for summermonths during the 1999e2008 period to calculate daily minimumand maximum surface temperatures and timing when maximumsurface temperature occurs. As the results for median and meanreference temperature are close, only the results of median refer-ence temperature are reported.

The daily maximum surface temperatures varied along the south-enorth axis of Beijing from south (Yang Bridge) to north (Jiutai Road),proportional to daily mean air temperature. The same pattern of vari-ation occurred for the different reference temperatures. This impliesthat daily mean air temperature has little impact on the relationshipbetween land cover and surface temperature (Figs. 5a and 6).

ce temperature is 26.25 �C (median) (b); daily maximum surface temperature anomaly

C. Zhao et al. / Building and Environment 46 (2011) 1174e1183 1179

Among the different sites, the highest surface temperatureoccurs in Qianmen, followed by Tiananmen Square and Gulou, all ofwhich are located in the Old Town (Fig. 5b). Subsequently, thefourth to eighth highest surface temperatures are all located withinthe Core Area, in descending order: Olympic Park, Anhua Bridge,Asia Olympic Village, Yang Bridge, and Yuantucheng Park. Thesurface temperature of Jingshan Park, in the Old Town, ranks 9thwhich is lower than other sites in the Old Town and also in the CoreArea. The two lowest surface temperature sites are both located inthe Eco-protection Area. This spatial distribution clearly shows thatthe Old Town is the UHI center of Beijing. It is also not surprisingthat the three lowest surface temperature sites are all parks, indi-cating the contribution that parks and green spaces make inreducing surface temperature in urban areas.

Daily maximum surface temperature anomaly (i.e., dailymaximum temperature minus reference temperature) is inverselyproportional to reference temperature, thus when daily mean airtemperature increases, the cooling effect of green space increasesaccordingly (Fig. 5c). The difference in daily maximum surfacetemperature between the Qianmen site (highest daily temperature)and the Olympic Forest Park site (lowest daily temperature) is ashigh as 18.54 �C, for the 99th percentile daily reference temperatureof 32.1 �C. The corresponding daily maximum surface temperatureat the Olympic Forest Park site is 6.5 �C lower, at only 25.60 �C. Thisindicates the vegetation’s ability of adjusting surface temperaturethrough transpiration. The cooling effects of green space usuallyincreasewith higher reference temperature. Under global warming,the temperature in urban areas has been and will continue toincrease, thus the cooling effect of green space seems an effectiveand relevant way to maintain comfortable urban environments.

The time when daily maximum surface temperature occurs isinversely proportional to daily mean temperature (Figs. 5d and 6),with the peak time usually slightly earlier when daily mean

Fig. 6. Analysis results of land cover and modeled results of urban morphology thermal envand timing of peak temperature. In the land cover maps for green space, trees are in black

temperature is relatively high, and vice versa. This is because a dayof relatively high daily mean temperature receives greater solarradiation, with the resulting faster rate of surface temperatureincrease producing an earlier peak temperature.

3.2. Land cover and major urban planning indicators

The differences in urban morphology among the eleven samplesites are reflected by urban planning indicators, such as Gcr, Far, Bdand Bh (Fig. 6). The average Gcr value for the Old Town sample sitesis 0.29, increasing to 0.36 for the Core Area sites and 0.72 for theEco-protection Area sites (Fig. 6). Two sites in the Eco-protectionArea have the highest values, followed by two sites containing openspace, Jingshan Park (Old Town) and Yuantucheng Park (Core Area).Thus sites consisting of, or adjacent to, open space are more likelyto have higher Gcr values. It is not surprising Qianmen site has thelowest Gcr value given it has the highest building density (Fig. 6).

Far, a major indicator of development intensity and livability, hasthe highest value (1.87) for the Core Area and the lowest value (0.23)for the Eco-protection Area. Its value for Old Town (1.04) falls inbetween. The four highest Far values are all located in the Core Area,namely Anhua Bridge, Asia Olympic Village, Olympic Park andYangBridge. The average Bd values of the three precincts are 0.45,0.21 and 0.10 for the Old Town, Core Area and Eco-protection Area,respectively (Fig. 6). The four highest values are all in the Old Town,in decreasing order: Qianmen, Gulou, Jingshan Park, and TiananmenSquare. The building density of Jingshan Park is also higher thanthose of all sites in the CoreArea, reflecting thehigh building densityin the Old Town. Due to the urban planning sunshine distancerequirement, there is usually a larger distance between relativelyhigher buildings, resulting in the Bd values for clusters of higherbuildings, such as in the Core Area, smaller than that of lowerbuildings, such as the Old Town.

ironmental parameters, including maximum and minimum daily surface temperatures, other vegetation are in dark gray, with the rest in light gray.

Fig. 7. The differences between modeled daily maximum surface temperature and30.2 �C (a); modeled daily maximum temperature when reference temperature is24.9 �C (b).

C. Zhao et al. / Building and Environment 46 (2011) 1174e11831180

In our land cover classification system, buildings were dividedinto 3 categories based on Bh values: type 21 (1e3 floors), type 22(4e9 floors) and type 23 (more than 10 floors). Results show thattype 21 accounts for 94% of buildings in the Old Town and 97% inthe Eco-protection Area, while Type 23 only occurs in the Core Area.Mean building heights are 2, 9 and 2 floors for the Old Town, CoreArea and Eco-protection Area, respectively. Therefore, vertically,from south to north, the urban canopy line along central axis ofBeijing has lower values in the urban center, surrounding by highervalues in the Core Area, and then lower values again in theperipheral Eco-protection Area. This vertical rhythm of urbanmorphology along the central axis, in accordance with the MasterPlan of Beijing, is clearly shown in Fig. 6.

3.3. Comparison with 30-year standard value (1961e1990)

“High Temperature Day” is a term used in China referring the dayswhen the daily air temperature is higher than the 30-year baseline airtemperature.TheStandardMonthlyMeteorologicalData (1961e1990)of Beijing, fromtheChinaMeteorologicalData Sharing ServiceSystem,is used to produce this baseline air temperature. The baseline dailymean and daily maximum air temperature of Beijing in summermonths are 24.9 �C and 30.2 �C, respectively.

Fig. 7a shows the differences between modeled daily maximumsurface temperature and the long-term baseline of daily maximumair temperature (30.2 �C) for the eleven sample sites under theeight daily mean temperature scenarios. Every day in summer isa High Temperature Day for both Qianmen and Tiananmen Squaresites, as modeled daily maximum surface temperature is alwayshigher than 30.2 �C. In contrast, there are no High TemperatureDays in Olympic Forest Park and Jiutai Road sites in the Eco-protection Area, where Gcr is more than 60%. For Jingshan Park andYuantucheng Park sites, where Gcr is around 50%, the 99-percentilesummer maximum daily surface temperature is lower than thelong-term average value. The remaining five sites, with Gcr valuesof around 30%, have median daily maximum surface temperaturesclose to their long-term mean.

The surface energy balance model was then re-run using thelong-term daily mean temperature of 24.9 �C as a referencetemperature to represent long-term mean solar radiation condi-tions in Beijing. The results show that the daily maximum surfacetemperatures of four sites are higher than 30.2 �C: Qianmen, Tia-nanmen Square, Gulou, and Olympic Park (Fig. 7b).

3.4. Relationship between urban planning indicators and urbanclimate indicators

The stepwise regressions method is used to explore the relation-shipbetweentheurbanplanning indicators (Gcr,Far,Bd,Bh) andurbanclimate indicators (daily maximum surface temperature (Tmax), dailyminimum surface temperature (Tmin) and the timing of peaktemperature (Tpeak)). For theTmaxandTmin, theirdepartures fromdailymean temperature (anomalies) are used instead of absolute values.

Stepwise regression for Tmax (Fig. 8a) shows that Gcr can explain84.16e84.96% of the variance in temperature difference among thedifferent sites. A logarithmic transformation of Gcr increased thecoefficient of determination (R2) to 92.58e97.73%. Far is the secondvariable selected by the stepwise regression predicting Tmax, withthe remaining two predictors, Bd and Bh dropped. When Far isincluded, the adjusted R2 increases to 91.88e96.74%, and to94.47e98.57% for logarithmic transformation. The importance ofFar can be demonstrated at Gulou and Olympic Park sites. The Gcr ofGulou is about 0.02 higher than that of Olympic Park. However, theTmax at Gulou are higher than that of Olympic Park site for all eightreference temperature simulations. This inconsistence relationship

between Gcr and daily maximum surface temperature results fromthe difference in Far at the two sites: the Far value at Olympic Parkis 2.0, while it is 1.3 at the Gulou site. Far represents the totalbuilding floor area, which is equivalent to the total volume ofbuilding mass. Because buildings store energy in the daytime, thisresults in the daily maximum surface temperature at Olympic Parksite being lower than that of the Gulou site.

In general, using the logarithmic transformation of Gcr explainsmore additional variance than adding the variable Far does.However, a combination of Gcr and Far could explain more variancethan any other combination when the reference temperature islower than 20 �C (Fig. 8a). This implies the selection of predictorvariables also depends on reference temperature. For the 30-yearlong-term mean daily mean temperature (24.9 �C) and the 99thpercentile of observed daily mean temperature (32.1 �C), dailymaximum surface temperature could be predicted by the logtransformation of Gcr and Far:

Tmax ¼�7:87LnðGcrÞ�0:77Farþ22:173

Tref ¼ 24:9�C R2 ¼ 96:93%(3)

Fig. 8. Stepwise regression for Tmax (a), Tmin (b), and Tpeak (c). Adjusted R2 is used inmultiple regression.

Fig. 9. Sensitivity results of Tmax (a) and Tmin (b) in 99th (32.1 �C) and Standard(24.9 �C) reference temperatures. Solid symbols are for 99th, and hollow symbols arefor standard.

C. Zhao et al. / Building and Environment 46 (2011) 1174e1183 1181

Tmax ¼�8:96LnðGcrÞ�0:73Farþ24:699

Tref ¼ 32:1�C R2 ¼ 98:57%(4)

For the daily minimum surface temperature, the Gcr is also theprincipal predictor. However, theBh is the secondpredictor instead ofFar. As similar with daily maximum surface temperature, a logtransformation of Gcr increases the explained variance from81.28e85.63% to 95.49e99.12%, and the additional variable Bhaccounts for the unexplained variance for the lower reference casevalues (Fig. 8b). A combination of log transformed Gcr and Bhconsistently explained the daily minimum surface temperature asa functionof reference temperature, incontrast to thedailymaximumsurface temperature simulation. To predict daily minimum surfacetemperature with climate variable (reference temperature) andurban planning indicators, the following formula could be used:

Tmin ¼�3:65LnðGcrÞþ0:052Bhþ12:57� 2 (5)

Tref ¼ 24:9 C R ¼ 99:21%

Tmin ¼ �4:30LnðGcrÞ þ 16:86

Tref ¼ 32:1�C R2 ¼ 99:12%(6)

It is not surprising that a combination of Far, Gcr and Bh explainsalmost all variance when predicting Tpeak (99.49e99.69%), since Gcrand Far are the best predictors for daily Tmax andGcr and Bh for dailyTmin. However, Far, instead of Gcr, becomes the most important

C. Zhao et al. / Building and Environment 46 (2011) 1174e11831182

single predictor (86.04e91.05%). The physical process explanation isthat solar radiation reaches its highest level around 12 pm insummer, and peak temperature occurs when energy balance rea-ches equilibrium (i.e. input is equal to output). Far represents theamount of building mass stored energy before noon, which delaysthe time when this energy balance is reached. The peak timing alsodepends on themaximumsurface temperature. For example, the Farvalue for the Qianmen site is 1.7, which is smaller than that of YangBridge, Anhua Bridge, Olympic Park, and the Asian Olympic Villagesites but larger than the remaining six sites. However, it is the lastsite to reach its maximum surface temperature, because it has thehighest peak temperature among the eleven sites.

The Tpeak for the 30-year long-term mean daily temperature(24.9 �C) can be predicted from the urban planning indicators of theeleven sample sites:

Tpeak ¼ 1:023Far� 0:742LnðGcrÞ � 0:42Bdþ 12:26

R2 ¼ 99:69%(7)

Fig. 9 is a sensitivity analysis of daily maximum and minimumsurface temperatures for different urban planning indicatorscenarios for the 99th percentile (32.1 �C) and 30-year long-termdaily mean temperature. Eqs. (3)e(6), developed in this study, areused to produce these sensitivity results. Tmax seemsmore sensitiveto Gcr values when it is less than 0.29 (Fig. 9a). This implies thatgreen spaces aremore crucial for areas with less green space, wherea small change in Gcr could cause a large change in Tmax given itscooling effects on the thermal environment. This critical point is lessimportant for the dailyminimum surface temperature Tmin (Fig. 9b).

4. Conclusions

A surface energy balance model was used to simulate the UHIeffects for 11 sample sites along the North-South central axis ofBeijing, the capital city of China. The eleven sample sites representdifferent urban morphologies. The results indicate that the dailymaximum surface temperatures fluctuated along the southenorthaxis of Beijing from south (Yang Bridge) to north (Jiutai Road),proportional to daily mean temperature. Among the different sites,the highest surface temperature occurs in Qianmen, followed byTiananmen Square and Gulou, all of which are located in the OldTown (Fig. 5b). Daily maximum surface temperature anomalies (i.e.,dailymaximum surface temperatureminus reference temperature)were inversely proportional to reference temperature, meaningthat when daily mean temperature increases, the cooling effect ofgreen space increases proportionally (Fig. 5c). The time when dailysurface maximum temperature occurs is inversely proportional todaily mean temperature (Figs. 5d and 6), which means the peaktime is usually slightly earlier when the daily mean temperature isrelatively higher, and vice versa. This is because a day of relativelyhigh daily mean temperature receives more solar radiation, withthe resulting faster rate of surface temperature increase producingan earlier peak temperature.

The relationship between urban planning indicators and urbanclimate indicators indicates thatGcrand Farcanexplain94.47e98.57%of variance for daily maximum surface temperature, Gcr and Bh canexplain 98.94e99.12% for daily minimum surface temperature, andFar, Gcr and Bd can explain 99.49e99.69% of variance for the peaktemperature timing. These simple relationships between climate andurbanplanning indicators couldhelpdecisionmakers andplanners totake climate adaptation into account, to ensure climate neutraldevelopment from the beginning of a planning process.

Green cover area has been identified to the most significanturban planning factor to affect urban thermal environment. a) Gcritself can explain up to 97.73% and 99.12% for Tmax and Tmin

respectively; b) cooling effects of green spaces increases when dailymean temperature increases. For example, when the daily meantemperature reaches 32.10 �C (the 99th percentile), daily Tmax at theOlympic Forest Park site is 6.5 �C lower at only 25.60 �C (the highestGcr site, Fig. 7); c) Tmax seems more sensitive to Gcr values when itis less than 0.29, and so a minor increase in Gcr could cause a largerdecrease in surface temperature, and vice versa. Therefore, it is ourrecommendation that urban planners should use green space asa measure to alleviate the thermal discomfort caused by urbani-zation and global warming. This is consistent with previousconclusion of Georgi and Dimitriou [40].Green roof has beenproved to be an efficient approach in reducing urban heat islandeffect, especially in densely built urban area [41,42].

This approach could be improved in two aspects, which are thesubject of future research. First, leaf area index (LAI) should beincorporated. In this current research all vegetation types (trees,shrubs, and grasses) and water bodies are considered together.However, in reality, the cooling contributions of tree and grass arecertainly different. If LAI were incorporated into the model, thecontributions of vegetation will be more accurate. This couldeventually lead to relevant guidelines for vegetation type propor-tion in green space planning to achieve optimal cooling effects.

Second, in addition to the quantity of green cover area, thespatial structure of green cover should also be incorporated infuture studies. The importance of green cover quantity in terms ofGcr has been shown in this study. However, spatial structures,vertically (tree cover) and horizontally (green cover distribution),which play an important role in optimizing ecological performancein any urban density, are not taken into account in the current studyand should be considered in the process of urban planning.

Acknowledgements

This research was partly funded by the National Basic ResearchPrograms (973 Program, 2010CB428406), the “Hundred TalentsProgram” of the Chinese Academy of Sciences (CAS), and theAustralian Commonwealth Scientific and Research Organization(CSIRO) e Ministry of Education (MOE) PhD Research FellowshipProgram (2008e2009) funded by China Scholarship Council. Wewish to thank Dr. Yichi Zhang of Institute of Geographical SciencesandNatural Resources Research of the ChineseAcademyof Sciences,Dr. Stephen P Charles of CSIRO Land andWater, and two anonymousreviewers, for their invaluable comments and constructive sugges-tions used to improve the quality of the manuscript. We would alsolike to thank Haijia Zang, and Zhaofei Liu for their help in datacollection and analysis, aswell as all individuals involved formakingthis study possible.

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