THERMAL EFFECTS OF BUILDING GEOMETRY AND SPACING ON THE URBAN CANOPY LAYER MICROCLIMATE IN A HOT-HUMID CLIMATE IN SUMMER

INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 24: 1729 1742 (2004) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.1092 TERMAL EFFECTS OF BUILDING GEOMETRY AND SPACING ON TE URBAN CANOPY LAYER MICROCLIMATE IN A OT-UMID CLIMATE IN SUMMER LIMOR SASUA-BAR, YIGAL TZAMIR and MILO E. OFFMAN* Faculty of Architecture and Town Planning, Technion Israel Institute of Technology, aifa 32000, Israel Received 16 February 2004 Revised 17 July 2004 Accepted 17 July 2004 ABSTRACT A quantitative analysis is presented for evaluating the diurnal thermal impact of proposed building arrangements on the urban canopy layer (UCL) air temperature, in summer in a hot-humid region. Building configuration along an urban street is quantitatively specified in this study by the building dimensions, by the spacing of the units and by the width of the street. The generic model described here is representative of the actual form of residential buildings found mostly along urban streets in Israel s cities. Sixty different building configurations were studied. The diurnal air temperature pattern in summer was calculated for each configuration using the analytical Green CTTC model, and compared with that of a nearby representative meteorological station at an open site. The results indicate significant thermal effects in the UCL due to the building form. The extent of the maximum impact is about 6.8 K at 1500h, namely ranging from 4.7 K above the value measured at the reference meteorological station (for shallow open spaces with wide spacing), to 2.1 K below this (for deep open spaces with narrow spacing). The statistical analysis of the results indicates the feasibility of assessing the expected maximum thermal effect of building designs of the generic form studied here, through a general linear relationship. This, thereby, provides a useful tool in judging the expected climatic impact of a proposed building design. Copyright 2004 Royal Meteorological Society. KEY WORDS: hot-humid climate; urban microclimate; urban geometry; generic building forms; Green CTTC model; Israel 1. INTRODUCTION Microclimatic conditions in the urban canopy layer (UCL: the open street space between the buildings, below roof level) of open spaces of urban streets and courtyards may differ significantly even in the same overall climatic context. They can be affected by a variety of factors under the control of the building designer, such as the geometry of the adjoining buildings, the albedo of walls and roofs, vegetation and anthropogenic heat release (mainly from vehicle traffic; Swaid, 1993). In contrast to private concerns, whose interest in building design centres on function and appearance, the public interest in urban planning usually takes into consideration the climatic effect of a proposed design of buildings on the related open spaces. The urban open spaces in question, i.e. urban streets and courtyards, typically cover about two-thirds of the city s total area (excluding attached parks); thus, their microclimate plays an important role in the city s overall climate. The environmental approach in this respect concerns mitigation of the microclimatic variations (especially in hot regions) by means of a set of control variables. The most important variable in this set is vegetation, especially the shade provided by trees. Apart from providing shade for pedestrians, the evaporative cooling of trees in parks and streets accounts for about 3 4 K at midday in summer, in temperate and hot regions (e.g. Bernatzky, 1982; Oke, 1989; Jauregui, * Correspondence to: Milo E. offman, Faculty of Architecture and Town Planning, Technion Israel Institute of Technology, aifa 32000, Israel; e-mail: mhoffman@ftx.technion.ac.il Copyright 2004 Royal Meteorological Society

1730 L. SASUA-BAR, Y. TZAMIR AND M. E. OFFMAN 1990 91; Ca et al., 1998; Potcher et al., 1999; Shashua-Bar and offman, 2000). Dense building, mostly emphasized in vernacular architecture, is also an important control variable whose contribution to the cooling effect depends on the building geometry and may even surpass that of vegetation (Shashua-Bar and offman, 2004). The third important control variable is the albedo of walls and roofs of the adjoining buildings, the thermal effect of which is relatively small, of the order of 1 K (Shashua-Bar and offman, 2004). These three factors, among others, have been recommended by Rosenfeld et al. (1995), in their report on President Clinton s Climate Change Action Plan, for offsetting the heat load in modern cities. Besides the effect of the urban geometry on microclimate features of the UCL (e.g. Oke, 1981; Swaid and offman, 1990 91), the building form has also been found to have a significant effect on the microclimate behaviour, as shown in pavilion and enclosed courtyards (Mills, 1997; Dimoudi and Nikolopoulou, 2000; Ratti et al., 2003). The results of these studies refer to individual cases and were not intended to provide a general solution for any specific generic form. The purpose of this paper is to study quantitatively the UCL microclimate effect of a proposed configuration of buildings, namely the thermal effects of the building dimensions and spacing as related to the UCL geometry. The building configurations chosen for this analysis are derived from a generic form representative of the types of residential building found mostly along urban streets in cities in Israel (regarding generic studies, see Martin and March (1972) and Ratti et al. (2003)). Sixty cases were studied. The diurnal air temperature values for each configuration were generated using the analytical Green CTTC model developed by Shashua-Bar and offman (2002). Summer data (where the heat is the greatest) were used for generating the simulations in a hot-humid urban region near the Mediterranean Sea coast (31 32 N). Following the simulation analysis, a statistical analysis was conducted to examine the interrelationships among the various thermal effects, and to assess the expected potential extent of the thermal impact at midday. The statistical relationship provides a general solution for the building effect on the UCL microclimate related to a specific generic form. 2.1. The generic building form 2. TE RESEARC SET-UP Apartment blocks in Israel, usually of three to six storeys, are built mostly on plots of about 600 m 2 (or multiples of such) along the urban streets, with an attached open courtyard at the rear of the building. The maximum built site coverage allowed is 35 40%, with a minimum spacing of 6 m between the buildings walls. The generic form studied in this paper is representative of this type. The radiative effects of a similar model structure of buildings have been studied by Mills (1997). A schematic plan of this generic model is shown in Figure 1. Four residential sites in the Tel Aviv metropolitan area representing this building types generic form are shown diagrammatically in Figure 2. Sites (a) and (b) illustrate the situation in Tel Aviv city centre: Rothschild Boulevard and Dizengoff Street respectively. The number of storeys is usually three to five, as indicated by the average height of the buildings (12 m), and the plot size is about 600 m 2. Site (c), Recanati Street in Ramat-Aviv (a northern suburb of Tel Aviv), illustrates the case of large, high-rise buildings. Site (d), Ben Zvi Street in Givataim (an inland town near Tel Aviv), also illustrates a case of high-rise buildings, with a slight deviation from the generic form shown in Figure 1. The analysis of the climatic effects due to the various geometric elements was conducted on buildings generated by the proposed generic form. The form contains similar geometric elements to the method of Allon and Tzamir (1971) in classifying typology in residential projects. In this paper, the buildings along the streets under study are specified quantitatively by the following three generic variables, defined by geometric ratios: 1. Spacing to frontal length (spacing ratio L1 : ), relating the distance between adjacent buildings L1 to the frontal length.

ISRAELI URBAN CANOPY LAYER MICROCLIMATE 1731 D L1 D W STREET D L1 D COURTYARD Figure 1. Plan representation of the generic building form and definition of length components., D, refer to the height, depth, frontal length of each unit, L1 refers to the spacing between the units and W refers to the width of the street (a) (b) (c) (d) Figure 2. Diagrammatic illustration of four sites in Tel Aviv metropolitan area. (a) Rothschild Blvd: = 12 m, W = 48 m, L1 = 6m, = 14 m, D = 15 m; (b) Dizengoff Street: = 12 m, W = 24 m, L1 = 6m,= 11 m, D = 20 m; (c) Recanati Street: = 50 m, W = 50 m, L1 = 20 m, = 30 m, D = 30 m; (d) Ben Zvi Street: = 30 m, W = 30 m, L1 = 22 m, = 75 m, D = 15 30 m. (See Figure 1 for definition of length components)

1732 L. SASUA-BAR, Y. TZAMIR AND M. E. OFFMAN 2. Building depth to frontal length (building depth ratio D : ), relating the building depth D to the frontal length. 3. eight-to-width ratio of the UCL (aspect ratio : W ), relating the building height to the width of the street s open space W. Using these three ratios, various building configurations were generated for the thermal analysis. The thermal analysis studies the climatic effect on air temperature on the front side of the building (facing the street s open space), including the spacing areas. The analysis on the rear side with an open attached courtyard follows the same procedure. Inner courtyards, attached-closed (patio) and semi-closed courtyards, are not considered in this study, as they do not fit the plan of the generic form shown in Figure 1. 2.2. Generating data for the analysis The analysis considers the case of a hot-humid climate where the heat in summer is most noticeable. The Tel Aviv metropolitan area, near the Mediterranean Sea coast, fits the case. Climate data for the month of July 1996 on solar radiation, wind velocity, ground surface temperature and humidity were obtained from a nearby representative open site meteorological station at Beit-Dagan. The levels of the generic variables considered cover the geometric relations found mostly in residential building configurations in modern cities: 1. Spacing ratio (L1 : ), 0.33, 0.5, 0.66, 0.83, 1.0 (five levels). 2. Building depth ratio (D : ), 0.67, 1.0, 1.34 (three levels). 3. Aspect ratio ( : W ), 0.25, 0.5, 1.0, 2.0 (four levels). Altogether, 60 cases were analysed. These were arranged in 12 groups of five levels of spacing, according to the three building depth levels and four aspect ratio levels. In the statistical analysis, the groups were numbered 0 to 11, where group 0 contains the configurations with the smallest building depth ratio and aspect ratio (see Table A.I). The average air temperature pattern in the frontal open spaces along a north southoriented urban street was simulated, using the climatic summer data average for July for the meteorological station at Beit-Dagan. The simulation tool is the analytical Green CTTC model for estimating the diurnal urban air temperature pattern (Shashua-Bar and offman, 2002), an extension of the cluster thermal time constant (CTTC) model developed by Swaid and offman (1990 91). It incorporates design principles related directly to the physical structure and properties of the building forms. The following variables and parameters are included in the model: Climatic variables of the region studied (including base regional temperature, direct, diffuse and reflected radiation, net longwave radiation, wind velocity, vapour pressure and cloudiness). Thermodynamic properties of building surfaces (including CTTC, surface solar radiation absorptivity, surface emissivity). Geometry of building forms (including building density, partially shaded areas, open-space geometry and sky-view factor at ground and roof levels). Properties of trees (including tree number density, solar radiation transmissivity through the tree canopy and convective heat exchange between the tree canopy and the ambient air). Anthropogenic heat release factors (including heat release owing to transportation and fuel consumption for domestic use). The predicted air temperature of an urban open space is calculated through the contribution of the heat received from external sources, mainly the net solar radiation, anthropogenic heat release, and vegetation effects. It has been applied successfully in various climatic regions.

ISRAELI URBAN CANOPY LAYER MICROCLIMATE 1733 (a) 32 (b) 20 Air Temperature [ C] 30 28 26 24 22 Air Temperature [ C] 18 16 14 12 10 8 6 4 2 20 0 4 6 8 1012141618202224 2 4 Time [h] 4 6 8 10 12 14 16 18 20 22 24 2 4 Time [h] Simulated values Measured values at meteorological station Figure 3. Simulated and measured patterns of diurnal air temperature at the meteorological station for (a) 21 July and (b) 15 January 1996 (Source: Shashua-Bar and offman (2004)) In this study, the reliability of the simulated diurnal air temperature values was verified by the validity of the analytical model used, as demonstrated in previously published studies (discussed below). 2.3. On the validation of the Green CTTC model The validity of the analytical CTTC model as first formulated by Swaid and offman (1990 91) was confirmed using measured data in Essen (Germany) and Jerusalem (Israel), and by further tests in studies by their colleagues (Mosseri, 1990; Aizenberg, 1992; Swaid, 1993). The predictions using the CTTC model were found to be in good agreement with in situ measurements. The extended version, the Green CTTC model, has also been validated for an open space using data for the meteorological station at Beit-Dagan, for both summer and winter (Shashua-Bar and offman, 2004). The diurnal estimates of the air temperature values simulated for Beit-Dagan for July (maximum heat) and January (maximum cold) are shown in Figure 3 against the measured values. The close fit in the summer and the winter data enhances the validity of the Green CTTC model. The Green CTTC model has also been validated on actual values measured in situ at 0600, 0900, 1500, 1800 and 2400h at 11 urban green areas with trees. The data used were taken from a previous study carried out on calm days in summer of July August 1996 in the Tel Aviv metropolitan, near the Mediterranean Sea coast (Shashua-Bar and offman, 2000). At all the 11 wooded sites studied the root-mean-square errors (RMSEs) of the cooling effect at the times measured were less than 0.5 K (Shashua-Bar and offman, 2002). 3. ANALYSING TE SIMULATION RESULTS 3.1. Graphic representation The simulated diurnal patterns of air temperature over the daily course of 24 hours were calculated using climatic data from the meteorological station at Beit-Dagan. The calculations are illustrated using average data over the month of July 1996. The Green CTTC model was used for estimating the simulated air temperature pattern, along a north southoriented urban street, for each of the 60 building configurations studied. The effect of orientation (east west case) is discussed in Section 3.2. The urban anthropogenic heat release and the effect of vegetation were not considered in this study. The following parameter values, as estimated at experimental sites (Shashua-Bar and offman, 2002), were used in calculating the 60 simulations:

1734 L. SASUA-BAR, Y. TZAMIR AND M. E. OFFMAN The surface solar radiation absorptivity, m was 0.55 for the ground and 0.5 for walls. The overall surface heat transfer coefficient h (W m 2 K 1 ), which is related to wind velocity, was 18, 16, 14 and 12 for : W 0.25, 0.5, 1.0 and 2.0 respectively. The effective atmospheric emissivity Br (Brunt, 1952), which is related to vapour pressure and cloudiness of the climatic region, was, on average, 0.74. The ground surface thermal emissivity e was 0.92. The CTTC was 8 hours, for the ground and 6 hours for walls. 3.1.1. Diurnal patterns. The simulated air temperature patterns in the north south-oriented street show significant diurnal variation among the various building configurations. To illustrate, the diurnal patterns of two simulations with extreme differences in the generic variables defined by geometric ratios are shown in Figure 4, with the diurnal course of air temperature at the meteorological station for comparison. Case (a) represents a street with shallow UCL ( : W = 0.25) and wide spacing, as wide as the front side of the building (L1 : = 1.0), with a small building depth ratio (D := 0.67). The diurnal simulated air temperature values are noticeably higher than those at the Beit-Dagan station, by about 4.7 K at 1500h. By contrast, case (b) represents a street with deep UCL ( : W = 2.0) and relatively narrow spacing ratio (L1 : = 0.33) with a large building depth ratio (D := 1.34). The result is a cooler UCL than that at the Beit-Dagan station, by as much as 2.12 K at 1500h. The range of the impact on the UCL microclimate between these two extreme cases is significant: about 6.8 K (ranging from 4.7 K in case (a) to 2.12 K in case (b)) at 1500h. 3.1.2. Maximum thermal effects. The analysis focuses on the maximum simulated potential thermal effect of the configuration, which occurs at 1500h (14 : 10 solar time), the time at which air temperature reaches its maximum in summer in the region studied. To facilitate the comparison among the various simulations, the thermal effect T is defined here as the difference between the simulated air temperature and that measured at the Beit-Dagan meteorological station. The average values for July air temperature and relative humidity (R) at this meteorological station were: max. 30.2 C with R = 60% at 1500h and min. 22.9 C with R = 90% at 0600h. A graphical representation of the potential thermal effects ( T ) for the 60 simulations is shown in Figures 5 8 for the four aspect ratios ( : W = 0.25, 0.5, 1.0 and 2.0 respectively). Each figure for a particular : W level represents three relative depth levels (D := 0.67, 1.0, 1.34) and each relation (line) represents 36 34 Air Temperature [ C] 32 30 28 26 24 22 4 6 8 10 12 14 16 18 20 22 24 2 4 Time [h] : shallow-built form with wide spacing; : deep-built form with narrow spacing; : Meteorological station data for July Figure 4. Diurnal pattern of average July air temperature, illustrating the potential thermal impact of building geometry

ISRAELI URBAN CANOPY LAYER MICROCLIMATE 1735 five simulations corresponding to the five spacing levels (L1 : = 0.33, 0.5, 0.66, 0.83, 1.0). The numerical values of T are given in Table A.I for each group of the generic variables. The 12 curves in Figures 5 8 indicate linear relationships. Inspection of the 12 curves leads to the following conclusions concerning the expected estimate of the quantitative impacts: 1. Spacing ratio L1 :. The wider the relative spacing between the buildings, the warmer the UCL microclimate. For example, increasing the relative spacing ratio from a level of 0.33 to a level of 1 warms the related UCL by about 2 K in the case of : W = 0.25 (see Figure 5). 1.8 1.6 D: = 0.67 1.4 D: = 1.00 Air Temperature Difference [K] 1.2 1.0 0.8 0.6 0.4 0.2 0.0-0.2 D: = 1.34 L1 D W -0.4 Perspective view from above -0.6-0.8 0.33 0.50 0.66 0.83 1.00 L1: :W = 1.0 Figure 7. The impact of geometry on the air temperature, in north south-oriented streets, in configurations with : W = 1.0, for 1500h in summer. (Air temperature differences are based on July climatic data at Beit-Dagan meteorological station) 0.0-0.2-0.4 D: = 0.67 D: = 1.00 Air Temperature Difference [K] -0.6-0.8-1.0-1.2-1.4-1.6-1.8-2.0-2.2 D: = 1.34 L1 D W Perspective view from above -2.4-2.6 0.33 0.50 0.66 0.83 1.00 L1: :W = 2.0 Figure 8. The impact of geometry on the air temperature, in north south-oriented streets, in configurations with : W = 2.0, for 1500h in summer. (Air temperature differences are based on July climatic data at Beit-Dagan meteorological station)

1736 L. SASUA-BAR, Y. TZAMIR AND M. E. OFFMAN 4.8 4.6 4.4 D: = 0.67 D: = 1.00 D: = 1.34 Air Temperature Difference [K] 4.2 4.0 3.8 3.6 3.4 3.2 3.0 2.8 L1 D W 2.6 Perspective view from above 2.4 2.2 0.33 0.50 0.66 0.83 1.00 L1: :W = 0.25 Figure 5. The impact of geometry on the air temperature, in north south-oriented streets, in configurations with : W = 0.25, for 1500h in summer. (Air temperature differences are based on July climatic data at Beit-Dagan meteorological station) 3.6 Air Temperature Difference [K] 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 D: = 0.67 D: = 1.00 D: = 1.34 L1 D W 1.4 Perspective view from above 1.2 1.0 0.33 0.50 0.66 0.83 1.00 L1: :W = 0.5 Figure 6. The impact of geometry on the air temperature, in north south-oriented streets, in configurations with : W = 0.5, for 1500h in summer. (Air temperature differences are based on July climatic data at Beit-Dagan meteorological station) 2. Building depth ratio D :. In each of Figures 5 8, the lower line corresponds to a higher level of relative depth, indicating a cooling effect. owever, the impact is small, of the order of 0.5 to 1 K. 3. Aspect ratio : W. The deeper the open space (high : W ), the cooler the UCL. This finding is well known and, as estimated here, the impact is very noticeable. At its maximum, the cooling effect is of the order of 4.5 K for configurations with : W = 2.0 (see Figure 8) compared with those with : W = 0.25 (see Figure 5).

ISRAELI URBAN CANOPY LAYER MICROCLIMATE 1737 3.2. Orientation effect The analysis of the simulated data described in Section 3.1 relates to the thermal impact of various building configurations along an urban street with north south axis. Orientation is known to have a distinct distribution effect with regard to solar radiation access to the surfaces of walls and the street floor. It does not follow, however, that this effect would make a difference for the UCL average air temperature, as the same amount of solar radiation is received by the open-space unit depending on the extent of its openness to the sky, irrespective of the orientation. To test the truth of this statement, the two extreme simulations in Figure 4 were recalculated along an urban street with an east west axis. It is assumed that both the north south and east west orientations have the same wind regime, and each is symmetric with regards to the height of its flanking buildings. The results show no noticeable differences from the north south-oriented cases. The thermal effects at 1500h were 4.8 K and 2.16 K in the east west orientation in group 0 and group 11 respectively as against 4.7 K for group 0 and 2.12 K for group 11 in the north south orientation. 3.3. Statistical analysis 3.3.1. Regression analysis. The quantitative thermal effects of the generic variables at 1500h are determined by regression analysis. The statistical analysis could be done by fitting a regression line to each of the 12 curves in Figures 5 8; each curve corresponds to one of the 12 groups. owever, pooling of all the data from the 60 simulations would be more efficient and more informative. The discussion in Section 3.1 on the thermal effects suggests use of the following independent variables in a multiple linear regression: X 1 X 1 X 2 X 1 X 3 D 1, D 2 Spacing ratio (L1 : ), a continuous variable Depth spacing interaction, an interaction variable for estimating the effect of building depth X 2 (building depth ratio, D : ) on the spacing effect of X 1, a continuous variable Aspect ratio spacing interaction, an interaction variable for estimating the effect of the aspect ratio X 3 (aspect ratio, : W ) on the spacing effect of X 1, a continuous variable Dummy variables for the effects of building depth ratio (D : ), where D 1 = { 1 if D := 1.0 0 otherwise D 2 = { 1 if D := 1.34 0 otherwise D 3, D 4, D 5 Dummy variables for the effects of aspect ratio ( : W ), where D 3 = { 1 if : W = 0.5 0 otherwise D 4 = { 1 if : W = 1.0 0 otherwise D 5 = { 1 if : W = 2.0 0 otherwise The regression relation to be estimated is i=5 T = a + (c i D i ) + b 1 X 1 + b 2 X 1 X 2 + b 3 X 1 X 3 (1) i=1 where T is the dependent variable. It is the thermal effect defined as the UCL simulated air temperature deviation from the respective meteorological air temperature at midday. a is a constant term, which includes the effect of the base levels of the dummy variables. c i and b i are the regression coefficients of the D and X variables respectively. D 1, D 2, D 3, D 4, D 5, X 1, X 1 X 2 and X 1 X 3 are independent variables. The effects of the base levels of D := 0.67 and of : W = 0.25 are included in the constant term and are not estimated explicitly. This is characteristic of the dummy variables estimation procedure. The coefficients

1738 L. SASUA-BAR, Y. TZAMIR AND M. E. OFFMAN c of the dummy variables represent differential effects relative to the base level, e.g. the coefficient c 1 of D 1 is c 1 units more than the effect at the base level (regarding the use of dummy variables, see Johnston and Dinardo (1996)). Equation (1) was estimated by a multiple linear regression. The regression set-up plan is given in Table A.I. The regression coefficients, their standard errors and the multiple correlation coefficient R are given in Table I. The thermal effect of individual components can be predicted separately besides the total effect T, using the regression coefficient estimate listed in Table I. As an example, case 3 in group 7 is used, where the geometric relations are X 1 = 0.66, D 1 = D := 1.0, D 4 = : W = 1.0. Predicted effects: Spacing effect = a 1 X 1 = 2.114 0.66 = 1.395 K Depth spacing interaction = b 2 X 1 X 2 = 0.611 0.66 1 = 0.404 K Aspect ratio spacing interaction = b 3 X 1 X 3 = 0.289 0.66 1 = 0.191 K Building depth effect = c 1 D 1 = 0.436 1.0 = 0.436 K Aspect ratio effect = c 4 D 4 = 3.169 1.0 = 3.169 K Constant term = a 1 = 2.538 1.0 = 2.538 K Total predicted thermal effect T = 0.541 K Total simulated thermal effect T = 0.60 K using the Green CTTC model The constant term a = 2.538 K is an estimate of the thermal effect indicated by the difference between a site s air temperature and that at the meteorological station at 1500h in summer. The site being considered is a continuous canyon-type street, with an aspect ratio and building depth ratio of 0.25 and 0.67 respectively and with no spacing between the building units (L1 = 0). 3.3.2. The fit and the extent of the building thermal effects. A graphic representation of the simulated T versus those predicted using regression is shown in Figure 9. The fit, as indicated by the multiple correlation coefficient R = 0.99, is very close. The RMSE for the predicted T is 0.08 K. The graph indicates the extent of the potential thermal impact on microclimate due to the building geometry. The extent is significant, about 6.8 K at 1500 h for the types of geometry studied here, ranging from 4.7 K warmer than the related meteorological station in a shallow open space with wide spacing to 2.12 K in a deep open space with narrow spacing. 3.3.3. Extrapolation of predicted thermal effects. Use of the dummy variables in multiple regressions obviates the need for specifying its form, i.e. whether the relationship in question is linear or non-linear. owever, for extrapolation purposes, it is important to assert its correct form in the relationship in question. In the present case, the dummies are continuous variables that were considered here as categories. ence, for extrapolation, they have to be reconsidered as continuous and whether they are linear or not. Figure 10 shows the relationship of the dummy effects (building depth ratio D : and aspect ratio : W ), as given in Table I, in relation to their levels. The building deepness relationship is found to be linear, whereas the aspect ratio effects are non-linear. The effects of these two relations at 1500 h are estimated for extrapolation purposes as for the building depth ratio T = 0.8 1.2 (D :) (2) Table I. Regression results: pooled data of 60 simulations of building configurations. Time: 1500h, July data Independent variables Constant X 1 X 1 X 2 X 1 X 3 D 1 D 2 D 3 D 4 D 5 Regression coefficient a b 1 b 2 b 3 c 1 c 2 c 3 c 4 c 5 Regression coefficient estimate 2.538 2.114 0.611 0.289 0.436 0.808 1.440 3.168 4.626 Standard error 0.072 0.016 0.190 0.777 0.051 0.090 0.037 0.052 0.097 Multiple correlation R 0.999, significant over 99% for n = 60 observations with nine variables

ISRAELI URBAN CANOPY LAYER MICROCLIMATE 1739 Thermal effects from simulations [K] 6 5 4 3 2 1 0-1 -2-3 -3-2 -1 0 1 2 3 4 5 6 Thermal effects predicted by linear regression [K] Figure 9. Simulated thermal effects according to the Green CTTC model versus the predicted effects according to the regression relation at 1500h 2 Air temperature difference [K] 1 0-1 -2-3 -4-5 -6 0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 Levels of the ratios D: :W Figure 10. The effects of the building depth ratio (D : ) and the aspect ratio ( : W) on the UCL air temperature at 1500h for the aspect ratio effect. T = 3.03 2.233 ln( : W) (3) For : W = 2.5, for example, the thermal effect of the aspect ratio using Equation (3) is expected to be 5.076 K compared with 4.626 K for : W = 2.0. Extrapolation in the case of the continuous variables, the spacing ratio X 1 and the interaction variables X 1 X 2 and X 1 X 3, is straightforward and the thermal effect is calculated directly from the regression relation in Equation (1). 4. CONCLUSIONS This study deals with the UCL microclimatic effect of building design, namely the thermal effects of building dimensions and spacing and UCL geometry. The analysis is applied on simulated summer air temperature data in a hot-humid urban region near the Mediterranean Sea coast. Building configurations were generated according to a generic form representing the residential buildings found mostly along urban streets in Israel.

1740 L. SASUA-BAR, Y. TZAMIR AND M. E. OFFMAN Sixty different building configurations were assessed by the Green CTTC model. The analysis focuses on the potential maximum diurnal thermal effect, at 1500h (14 : 10 solar time) in July. The thermal effects are related to those at the Beit-Dagan meteorological station representing the climatic region near the Mediterranean Sea coast. The main findings are: The extent of the thermal impact due to the various building configurations studied is significant, about 6.8 K at 1500h, ranging from 4.7 K above the reference meteorological station, in shallow open spaces with wide spacing, to 2.1 K below it in deep open spaces with narrow spacing. Recall that the cited cooling effect of vegetation seldom exceeds 3 4 K at midday, whereas the building effect in deep open spaces may surpass this, as shown in this study. Each of the three generic variables specifying the building form in this study has a distinct impact on the UCL microclimate: wide spacing has a warming effect, whereas UCL deepening (high : W ) has a strong cooling effect. The built-up depth ratio has a relatively weak cooling effect, but has a positive interactive influence on the spacing effect. The statistical analysis indicates the feasibility of assessing the total expected maximum thermal effect of a proposed building of the generic form studied here, through use of a linear relationship. The thermal impact on the UCL air temperature of the building configuration studied does not depend on the street orientation. The quantitative analysis described in this paper provides a useful tool for assessing the expected thermal effect on the UCL microclimate at 1500h of a proposed building design. Assessing the expected thermal effect of each building s geometric element separately may also be important in judging the climatic impact of a proposed building. The results obtained in this paper relate to a hot-humid region in summer. For practical use, the methodology of this study may be extended to cover cases in different seasons and in various urban climates. ACKNOWLEDGEMENTS The authors are indebted to E. Goldberg for editorial assistance and comments. APPENDIX Table A.I. Multiple regression set-up. Time: 1500h, July data Group y, a T D : : W X 1 X 1 X 2 X 1 X 3 Constant y, b T (K) D 1 D 2 D 3 D 4 D 5 (K) (0) D := 0.67 3.23 0 0 0 0 0 0.33 0.221 0.083 1 3.35 3.72 0 0 0 0 0 0.50 0.335 0.125 1 3.76 : W = 0.25 4.11 0 0 0 0 0 0.66 0.442 0.165 1 4.16 4.45 0 0 0 0 0 0.83 0.556 0.208 1 4.57 4.70 0 0 0 0 0 1.00 0.670 0.250 1 4.99 (1) D := 1.00 2.94 1 0 0 0 0 0.33 0.330 0.083 1 2.98 3.52 1 0 0 0 0 0.50 0.500 0.125 1 3.43 : W = 0.25 3.97 1 0 0 0 0 0.66 0.660 0.165 1 3.85 4.33 1 0 0 0 0 0.83 0.830 0.208 1 4.30 4.63 1 0 0 0 0 1.00 1.000 0.250 1 4.75 (2) D := 1.34 2.70 0 1 0 0 0 0.33 0.442 0.083 1 2.67 3.34 0 1 0 0 0 0.50 0.670 0.125 1 3.16 : W = 0.25 3.84 0 1 0 0 0 0.66 0.884 0.165 1 3.62 4.24 0 1 0 0 0 0.83 1.112 0.208 1 4.10 4.58 0 1 0 0 0 1.00 1.340 0.250 1 4.59

ISRAELI URBAN CANOPY LAYER MICROCLIMATE 1741 Table A.I. (Continued) Group y, a T D : : W X 1 X 1 X 2 X 1 X 3 Constant y, b T (K) D 1 D 2 D 3 D 4 D 5 (K) (3) D := 0.67 1.81 0 0 1 0 0 0.33 0.221 0.165 1 1.88 2.32 0 0 1 0 0 0.50 0.335 0.250 1 2.29 : W = 0.5 2.76 0 0 1 0 0 0.66 0.442 0.330 1 2.67 3.14 0 0 1 0 0 0.83 0.556 0.415 1 3.07 3.44 0 0 1 0 0 1.00 0.670 0.500 1 3.48 (4) D := 1.00 1.38 1 0 1 0 0 0.33 0.330 0.165 1 1.51 1.97 1 0 1 0 0 0.50 0.500 0.250 1 1.95 : W = 0.5 2.45 1 0 1 0 0 0.66 0.660 0.330 1 2.37 2.86 1 0 1 0 0 0.83 0.830 0.415 1 2.80 3.21 1 0 1 0 0 1.00 1.000 0.500 1 3.24 (5) D := 1.34 1.05 0 1 1 0 0 0.33 0.442 0.165 1 1.21 1.68 0 1 1 0 0 0.50 0.670 0.250 1 1.68 : W = 0.5 2.21 0 1 1 0 0 0.66 0.884 0.330 1 2.13 2.66 0 1 1 0 0 0.83 1.112 0.415 1 2.60 3.04 0 1 1 0 0 1.00 1.340 0.500 1 3.08 (6) D := 0.67 0.11 0 0 0 1 0 0.33 0.221 0.330 1 0.11 0.52 0 0 0 1 0 0.50 0.335 0.500 1 0.49 : W = 1.0 0.98 0 0 0 1 0 0.66 0.442 0.660 1 0.84 1.36 0 0 0 1 0 0.83 0.556 0.830 1 1.22 1.67 0 0 0 1 0 1.00 0.670 1.000 1 1.60 (7) D := 1.00 0.35 1 0 0 1 0 0.33 0.330 0.330 1 0.26 0.15 1 0 0 1 0 0.50 0.500 0.500 1 0.15 : W = 1.0 0.60 1 0 0 1 0 0.66 0.660 0.660 1 0.54 0.99 1 0 0 1 0 0.83 0.830 0.830 1 0.95 1.34 1 0 0 1 0 1.00 1.000 1.000 1 1.37 (8) D := 1.34 0.70 0 1 0 1 0 0.33 0.442 0.330 1 0.57 0.17 0 1 0 1 0 0.50 0.670 0.500 1 0.12 : W = 1.0 0.30 0 1 0 1 0 0.66 0.884 0.660 1 0.31 0.72 0 1 0 1 0 0.83 1.112 0.830 1 0.76 1.09 0 1 0 1 0 1.00 1.340 1.000 1 1.20 (9) D := 0.67 1.45 0 0 0 0 1 0.33 0.221 0.660 1 1.45 1.08 0 0 0 0 1 0.50 0.335 1.000 1 1.12 : W = 2.0 0.74 0 0 0 0 1 0.66 0.442 1.320 1 0.80 0.41 0 0 0 0 1 0.83 0.556 1.660 1 0.47 0.13 0 0 0 0 1 1.00 0.670 2.000 1 0.14 (10) D := 1.00 1.84 1 0 0 0 1 0.33 0.330 0.660 1 1.82 1.44 1 0 0 0 1 0.50 0.500 1.000 1 1.45 : W = 2.0 1.08 1 0 0 0 1 0.66 0.660 1.320 1 1.11 0.74 1 0 0 0 1 0.83 0.830 1.660 1 0.74 0.43 1 0 0 0 1 1.00 1.000 2.000 1 0.38 (11) D := 1.34 2.12 0 1 0 0 1 0.33 0.442 0.660 1 2.12 1.71 0 1 0 0 1 0.50 0.670 1.000 1 1.72 : W = 2.0 1.33 0 1 0 0 1 0.66 0.884 1.320 1 1.34 0.98 0 1 0 0 1 0.83 1.112 1.660 1 0.94 0.65 0 1 0 0 1 1.00 1.340 2.000 1 0.54 a y = T simulated using the analytical Green CTTC model. b y = T predicted by multiple linear regression.

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