The Vertical Stratification of Air Temperature in the ... - AMS Journals

JUNE 2010

GEORGAKIS ET AL.

1219

The Vertical Stratification of Air Temperature in the Center of Athens C. GEORGAKIS AND M. SANTAMOURIS Group of Building Environmental Physics, University of Athens, Athens, Greece

G. KAISARLIS Department of Mechanical Design and Control Systems, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece (Manuscript received 20 May 2009, in final form 26 October 2009) ABSTRACT The intraurban temperature variation in the center of Athens, Greece, was investigated in relation to urban geometry. This paper describes two main tasks: 1) Air temperature was recorded in the center of Athens and at the Meteorological Service Station at the University of Athens. Experimental data were collected through extensive monitoring at four different heights inside five different urban canyons in the center of Athens during the summer period. A measurement uncertainty analysis was carried out to estimate critical threshold values of air temperature below which differences were not significant. 2) The correlation between urban– suburban air temperature differences was assessed, using the geometrical characteristics of each urban street canyon. Urban–rural air temperature differences were considered to be not important if they were below the threshold value of 0.38C. It was concluded that the major factor controlling urban–suburban air temperature differences was the geometry of the urban area. Other factors were the orientation of the observational sites, the current weather conditions, and the inversion of air masses adjacent to the ground level. An increase in the value of aspect ratios leads to a decrease in the difference between air inside the canyons and at the suburban station. The air temperature profile in an open-space area was the most important defining factor for the stratification of the urban–rural air temperature differences.

1. Introduction The process of urbanization causes radical changes in the nature of land surfaces and atmospheric properties of a region. It involves the transformation of radiative, thermal, moisture, and aerodynamic characteristics and thereby dislocates the natural solar and hydrologic balances. Dense urban construction materials cause systems to store heat and waterproof the soil surface. Block line geometry creates the possibility of radiation trapping. Moreover, wind-tunnel effects in streets and unusual wind turbulence due to improperly designed high-rise buildings are very common. The inevitable increase in air pollution is in line with urbanization and affects the radiation balance.

Corresponding author address: C. Georgakis, Group of Building Environmental Physics, University of Athens, Building Physics 5, University Campus, 157 84 Athens, Greece. E-mail: [email protected] DOI: 10.1175/2010JAMC2290.1 Ó 2010 American Meteorological Society

It is well known that air in the urban canopy is warmer than that in the surrounding countryside; the urbanheat-island phenomenon is mainly due to differences in thermal structures among urban and rural environments. The heat-island phenomenon can occur during both day and night. Numerous research studies have been performed to analyze and understand the mechanisms and effects of the heat-island phenomenon (Eliasson 1996; Goh and Chang 1999; Montavez et al. 2000; Mihalakakou et al. 2002; Lindberg et al. 2003; Georgakis and Santamouris 2006; Santamouris 2007). Most such studies concentrate on night heat islands; relatively few have focused on daytime temperatures. High urban temperatures have a serious impact on electricity demands for air conditioning buildings, increase smog production, and contribute to increased pollutant emissions from power plants, including sulfur dioxide, carbon monoxide, nitrous oxides, and suspended particulates (Santamouris et al. 2007). High temperatures result in discomfort and inconvenience for urban residents. Previous studies in the central Athens, Greece, area, where construction is heavy and where the

1220

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY

city’s center is characterized by deep street canyons, showed that air temperatures could be up to 28–68C warmer than those of the surrounding areas (Santamouris et al. 2001). A recent statistical analysis showed a strong linear relationship between the mean maximum urban-heatisland intensity and the studied urban parameters such as the sky view factor, building height, aspect ratio, and water surface ratio (Bottyan and Unger 2003). Prevailing synoptic circulation, cloud cover, and weak winds (closed anticyclones) mostly favor differences between the center of a city and the countryside (Mihalakakou et al. 2002; Santamouris et al. 2007). The aim of this study was to estimate the spatial variation and vertical stratification of urban–suburban differences in summer daytime air temperatures in the center of Athens, based on real-state measurements. Comparative measurements of air temperature and airflow were carried out inside five deep urban canyons, of different aspect ratios, at four different height levels. Air temperature values recorded at the suburban station at the University of Athens campus were used as a reference. Observed urban–suburban differences in air temperature and air temperature vertical stratification were analyzed for all locations and interpreted as a function of the geometric characteristics of the urban canyons, accounting for measurement uncertainties in the air temperature data.

2. Description of field experiments The greater Athens area is located in a basin surrounded by Penteli Mountain (1107 m) to the northeast, Parnitha Mountain (1426 m) to the north, Hymettus Mountain (1026 m) to the east, and Egaleo Mountain (458 m) to the west. The southern part of the basin abuts the sea (Fig. 1). The climate in the city of Athens is typically ‘‘Mediterranean,’’ characterized by mild winters and dry hot summers. The central part of the city covers an area of about 450 km2 with a population density of approximately 8000 inhabitants per square kilometer and few green spaces. The center of the city is characterized by a high concentration of buildings with deep canyons usually having an aspect ratio (height/width) of greater than 2. Using the framework of the European Commission Project Urbvent, an extended experimental campaign was performed during the summer of 2001 in five different pedestrian streets in the center of Athens (Fig. 2). An extended analysis of the experimental data investigated airflow and thermal phenomena in urban canyons (Georgakis and Santamouris 2004, 2006). Based on these real-state experimental measurements, simple models obtained for wind, temperature, noise, and pollution were used to assess the potential for natural ventilation

VOLUME 49

of buildings located in the urban region of Athens (Ghiaus et al. 2006). Air temperatures were measured by means of the mobile meteorological station of the University of Athens inside five different urban street canyons, at four height levels. The five real-state experimental sites were Dervenion canyon (case A), which is oriented with the long axis in a NNW–SSE direction (338 from real north counterclockwise), Kaniggos canyon (case B), which is oriented with the long axis running NNE–SSW (128 from real north), Voukourestiou canyon (case C), which is oriented with the long axis running NE–SW (458 from real north), Miltiadou canyon (case D), which is oriented with the long axis running ENE–WSW (708 from real north), and Ermou canyon (case E), which is oriented with the long axis running E–W (928 from real north). The geometric characteristics of all experimental sites are given in Table 1. The materials used in the external facades of the canyons were white–gray concrete for the buildings, asphalt for the streets, and cement tiles for pavements. Anthropogenic heat during the total monitoring period was low because the experimental campaign took place in five pedestrian streets where human activities are limited. The mobile meteorological station of the University of Athens was placed in the center of each urban canyon for 3 days for 12 h day21. During the 71-day experimental period the elevation angle altered by close to 178. The angle of elevation of the sun on 28 June during daytime, at 1100 LT, was 74.528 whereas at the same time on 6 September it was 57.688. Sunset was observed at 1800 LT on 28 June whereas on 6 September it was observed at 1600 LT. The in-canyon measuring point for all cases was located in the middle of a cross-canyon distance. For cases A–D, the measuring point was 20, 40, 55, and 40 m from the north intersection, respectively. For case E, the measuring point was 40 m from the east intersection. The mobile meteorological station is composed of a bearing vehicle and a telescopic-mast PT8 Combined Collar Mast Assembly with an extended height of 15.3 m, a retracted height of 3.43 m, and a maximum headload of 15 kg. The experimental procedure lasted three consecutive days per canyon, for 12 h from morning until late in the afternoon (Table 1). The temperatures were measured using two methods. The first consisted of air temperature measurements inside the canyon. The thermometers used were PT100, T351-PX 1/3 DIN (information available online at www. windspeed.co.uk). These circular-design thermometers were placed on the telescopic mast at four different heights (3.5, 7.5, 11.5, and 15.5 m). They measured and recorded the air temperature every 30 s. The miniature screen formed a housing for a range of temperature-sensing

JUNE 2010

GEORGAKIS ET AL.

1221

FIG. 1. Location map of the study area (dashed line) and the suburban site (continuous line) in the greater Athens area.

elements, providing weather protection while allowing for a free passage of air. The circular design with polished white surfaces resulted in a uniformly low temperature rise due to the effects of solar radiation by day and in low temperature drops at night due to outward radiation to clear skies. The screen was fitted with a fourwire PRT, a two-wire PRT with a compensating loop, a curve-matched thermistor, or a semiconductor sensor. According to the manufacturer’s specifications, the accuracy of the thermometers was 6(0.18C 1 0.005T ), where T is the temperature above or below 08C. The second method of measuring temperature consisted of air temperature measurements in an open-space area. For the same experimental period, air temperatures were measured every 10 min at the station of the Meteorological Service of the University of Athens. The thermometers used were again PT100, T351-PX 1/3 DIN.

The air temperature was measured at 2, 5, and 10 m above ground level. The reference station is located in a suburban area east of Athens, almost 8 km from the center of the city and at an altitude that is close to 90 m. The station is 500 m from Hymettus Mountain and is surrounded to the east and south by the Kessariani suburban forest and to the north and west by an area with relatively low density buildings. This station can be considered as a reference, because the temperature is expected to be lower at this station than at the other suburban stations, because the University campus is a tree-planted region and is located in a green open space close to a mountain.

3. Uncertainty analysis of meteorological data The estimation of the air temperature measurement uncertainty is critical to a wide range of applications

1222


VOLUME 49

FIG. 2. Location map of the experimental sites.

such as industrial equipment accreditation, laboratory instrument calibration, and meteorological data collection for environmental studies (Crovini et al. 1992; Vasuki et al. 2008; Vardoulakis et al. 2002). When reporting the result of a measurement of a physical quantity, a quantitative indication of the quality of the result should be provided to assess its reliability. Without such an indication, measurement results cannot be compared, either among each other or with reference values given in a specification or standard. In that context, it is necessary that there be a readily implemented and generally accepted procedure for characterizing the quality of the

result of a measurement, that is, for evaluating and expressing its uncertainty (ISO 1995). In the International Vocabulary of Basic and General Terms in Metrology (ISO 1993), a parameter called the ‘‘measurement uncertainty’’ is proposed as a quantitative assessment of a measurement’s accuracy. This parameter is associated with a measurement of the dispersion of values that could be reasonably attributed to the quantity intended to be measured (ISO 1993). The rules for evaluating the measurement uncertainty are presented in the ISO Guide to the Expression of Uncertainty in Measurement (GUM; ISO 1995). These rules are now

TABLE 1. Description of the experimental site of the five street canyons and definition of the in-canyon measuring points. Geometrical features

Case A

Case B

Case C

Case D

Case E

Canyon width W (m) Building length L (m) Building height H (m) H/W ratio L/W ratio Expt period

8 55 23 3 6.9 4–6 Sep

8 70 28 3.3 8.8 27–29 Aug

9 100 30 3 11.1 7 and 9–10 Aug

6 70 15 2.5 11.7 31 Jul and 2–3 Aug

10 120 20 2 12 28–29 Jun and 3 Jul

JUNE 2010

1223

GEORGAKIS ET AL.

fully accepted by the majority of international and national organizations and standards institutes all over the world. At this time, the evaluation of the uncertainty of measurements of any physical quantity, such as air temperature, is a necessary condition for its technical soundness. In the GUM (ISO 1995), two kinds of uncertainty are considered: type A, which is estimated using statistical procedures, and type B, which is estimated with other methods, including accumulated technical knowledge and mathematical models. The three major concepts that need to be defined prior to the uncertainty analysis of this study’s air temperature data are listed below: 1) The true value is the value determined with a perfect measurement process. The true value is always unknown because all measurement processes are imperfect to some degree. 2) The error is the difference between the measured value and the true value. The error is always unknown because the true value is always unknown. 3) Uncertainty is an estimate of the interval bounding the measured value within which the true value lies. A confidence level (or confidence interval) is the degree of confidence, expressed as a probability percentage, that the true value lies within the stated uncertainty. A proper uncertainty statement would read ‘‘T 5 508C 6 1.0% at a 95% level of confidence.’’ This means that 95 out of every 100 observations are between 49.58 and 50.58C. The evaluation of the measurement uncertainty for the air temperature data in this paper is based on the criteria expressed in the GUM (ISO 1995). It is performed in four sequential steps. Step 1 is type-A evaluation of standard uncertainty. Type-A measurement uncertainty is determined from a statistical analysis of experimental results. Assuming a normal stratification, the following formula can be used: "

n

1 (t t )2 uA 5 n 1 i51 i

å

#1/2 ,

(1)

where 1 t n i51 i

å

Contributor Statistical analysis Thermometer reading capability Thermometer calibration Manufacturers’ accuracy specification Combined standard uncertainty Expanded uncertainty (k 5 2)

(2)

and n is the number of measurements. Step 2 is the type-B evaluation of standard uncertainty. Type-B uncertainty comes from nonstatistical measurement analysis. Three components are considered to be contributors to type-B uncertainty: 1) digital resolution R of the temperature indicator and the uncertainty uRES

Type A/B

Std uncertainty

A B B B

0.0508C 0.0038C 0.0038C 0.1448C 0.1538C 0.3008C

associated with the thermometer-reading capability, which, according to the GUM (ISO 1995) definition, are related by pffiffiffiffiffi uRES 5 R/ 12;

(3)

2) thermometer calibration—the calibration certificate should state the calibration uncertainty UCAL of the thermometer and also the level of confidence or the coverage factor k that was used and then the standard uncertainty uCAL in the thermometer calibration is given by uCAL 5 U CAL /k;

(4)

and 3) manufacturer’s accuracy specification, which is a type-B evaluation in which the uncertainty uSPEC is assumed to be rectangularly distributed, Ac is the accuracy statement indicated on the technical sheet of the thermometer, and the two quantities are related as pffiffiffi uSPEC 5 Ac/ 3.

(5)

The combined type-B uncertainty is calculated by combining the above three contributors quadratically: uB 5 (u2RES 1 u2CAL 1 u2SPEC )1/2 .

(6)

Step 3 consists of evaluation of combined standard uncertainty, by combining the type-A and type-B uncertainties that were calculated in steps 1 and 2, respectively: uC 5 (u2A 1 u2B )1/2 .

n

t5

TABLE 2. Summary of uncertainty estimation for the air temperature data.

(7)

In step 4, the final step, the expanded standard uncertainty is evaluated. To calculate the expanded uncertainty U, the combined standard uncertainty is multiplied by the relevant coverage factor k: U 95 5 k95 uC (l).

(8)

For a 95% confidence level, the value of k is given from the t-stratification table: k95 5 2. In Table 2, the numerical

1224


values of the uncertainties calculated by Eqs. (1)–(8) for the air temperature data l of this study are presented. There are some aspects of uncertainty analysis for the air temperature data that have not been considered in the above method for the sake of brevity. They are briefly listed below, and more detailed information can be found in ISO (1995). The degrees of freedom represent a measure of how closely a small sample approximates the entire population. In the example presented here, the air temperature measurement is only based on a few measurements, corresponding to a small number of degrees of freedom. The coverage factor required to achieve a given confidence interval must be larger. For example, with 12 measurements, the coverage factor required for 95% confidence is k 5 2.201. To achieve the same confidence level with four measurements requires that k 5 3.182. In most practical uncertainty analyses, this is not an issue. In this analysis, it is assumed that all components of uncertainty are independent. If two components of uncertainty are not independent, they are said to be correlated. If a correlation is present, the method for combining components must be adjusted.

4. Data and method To calculate urban–rural differences in air temperature, it was necessary to measure the air temperature at the same distances from ground level at each site. The heights of 2, 5, 10, and 15 m from ground level were selected as comparison levels, based on experimental considerations. The first level of comparison should not be adjacent to ground level, so airflow and air temperature could be influenced both by thermal and mechanical phenomena (Santamouris et al. 2008). Under this consideration the distance of 2 m from ground level was selected as the first level of comparison. The other three height levels were selected to follow the fixed distance of 5 m from ground level. For the heights of 5 and 10 m inside the canyons, mean hourly values of the air temperature were derived after linear interpolation from the values obtained at 3.5, 7.5, and 11.5 m. It was necessary to calculate the air temperature at a height of 2 m inside the canyons and 15-m height outside the canyons from the extended experimental database. For this purpose, the equations presented in this section were used. The temperature gradient close to the ground level (10–20 m) is determined by insolation, wind speed, cloud cover (distinct from its direct control of insolation), heat transfer through the ground, and the nature of the surface. The impact of the first three factors was greater for the temperature gradient (Tait 1949). The logarithmic law pertaining to temperature differences in the lower

VOLUME 49

part of the atmosphere appears to hold well. It was generalized by using the relation T 5 A logZ 1 R between temperature T and height Z. If T1 and T2 are the temperatures at two heights Z1 and Z2 and I (g cal cm22 min21) is the observed horizontal component of total insolation, then T 1 T 2 5 (0.4 4I) log10 (Z1 /Z2 ).

(9)

Note that if there is no temperature gradient then T1 is equal to T2 and I has the value of 0.1 g cal cm22 min21. Taking into account the fact that the measurements were taken in the summer and that the sky was clear, the recorded insolation I was compared with that to be expected from the position of the sun. It was found empirically that a close agreement was obtained by substituting 1.7 sina 2 0.2 for I, where a is the angle of elevation of the sun. The elevation angle measures the height of the sun in the sky from the horizon and is the complement of the zenith angle of the sun. The constant term 0.2 corrects for increased airmass absorption at low solar elevations and, in turn, is compensated for at high elevations by the fact that the factor 1.7 is slightly greater than the value that is generally accepted for the solar constant after unit airmass absorption. Therefore, the above equation becomes T 1 T 2 5 N(1.2 6.8 sina) log10 (Z1 /Z2 ),

(10)

where N is the effective absence of cloudiness (with N 5 1 corresponding to a clear sky). The angle of elevation of the sun was calculated for all experimental days. Using the measured air temperature at 3.5 m inside the canyon as T1 and the heights of 3.5 and 2 m from ground level as Z1 and Z2, the air temperature values at 2 m were calculated. By the same means, the air temperature values at 15 m were estimated outside the canyons.

5. Urban–suburban air temperature differences in five real-state experimental sites, in the center of Athens, during daytime The urban–rural differences between air temperatures measured inside five different urban canyons, in the center of Athens, and temperatures measured at the suburban station were calculated for the whole experimental period (Table 3; Figs. 3–7). Given that these differences varied around a range of values, the data are shown in box plots. The line in the middle of the box is the median, or the 50th percentile, of the sample. The lower and upper lines of the box are the 25th and 75th percentiles, representing the lower and upper quartile, respectively.

JUNE 2010

GEORGAKIS ET AL.

TABLE 3. Average hourly values between air temperature inside and outside five different urban canyons at four different height levels.

Time Case A 1000 LT 1500 LT 1800 LT Case B 1000 LT 1500 LT 1800 LT Case C 1000 LT 1500 LT 1800 LT Case D 1000 LT 1500 LT 1900 LT Case E 1100 LT 1500 LT

2 m from ground level




1.7678C 20.0998C 21.3408C

0.6428C 20.0518C 20.5418C

1.9268C 1.5978C 0.8548C

2.8048C 1.9878C 0.2448C

2.1088C 2.0028C 20.6658C

1.0418C 1.7788C 20.0468C

2.5558C 3.2228C 1.3998C

3.6568C 3.8938C 0.8808C

2.0778C 3.1448C 20.4798C

0.7848C 2.0808C 20.1048C

2.2858C 2.8898C 1.4438C

3.5268C 2.6888C 0.9738C

3.1388C 3.1928C 0.6468C

1.7708C 2.7278C 1.1458C

2.9668C 4.1848C 2.3448C

4.1108C 4.2468C 1.9238C

5.6578C 5.2548C

4.1048C 4.2728C

3.5548C 4.2368C

3.9588C 4.0408C

Data are considered to be outliers if they are located 1.5 times the interquartile range away from the top or bottom of the box. Each figure contains four boxes. The first box depicts urban–suburban air temperature differences at a height of 2 m from ground level. The second, third, and fourth depict the values calculated at 5, 10, and 15 m from ground level, respectively. The four different box plots correspond to each canyon for 1000 LT in the morning, 1500 LT at noon, and 1800 LT in the afternoon, as representative hours for the experimental period (Figs. 8–10). For the following cases, H is height, W is width, and L is length for the urban canyon.

a. Case A (H/W 5 2.9; L/W 5 6.9) In the morning (1000 LT), the air adjacent to the ground level (2 m) was 1.88C warmer than the air at the suburban station at the same height level. Urban air 5 m from the ground was only 0.68C warmer than air at the same level height in the open space. Urban–suburban differences at 10 and 15 m from ground level were estimated to be close to 28 and 38C, respectively (Table 3; Fig. 8). About midday (1500 LT), the air at 2 m was cooler by 0.18C than the air in the suburban station. The same case was observed at 5 m from ground level, where the air was slightly cooler (by 0.058C) than the air at the same height level at the suburban station. These differences are characterized as not significant since they are below 0.38C, the critical value due to expanded uncertainty, for

1225

the 95% confidence level (Table 2). The air at 10 and 15 m was about 1.68 and 28C warmer, respectively, than the air at the same level height in the open-space area (Table 3; Fig. 9). During the afternoon (1800 LT), air temperature differences at 2 and 5 m from ground level were negative and considered to be significant based on the uncertainty analysis of this study. Air masses were 1.48 and 0.58C cooler than the air outside the canyon, respectively. In the same period, the air temperature inside the canyon was almost the same as that measured in the suburban station (Table 3; Fig. 10). The fact that urban–suburban differences increased as a function of height (DT5 , DT10 , DT15) is based on the air temperature profiles inside and outside the urban area. It is generally accepted that the air temperature in an open-space area decreases as a function of height. In the morning, inversion of air temperature is present only in air masses adjacent to the ground level. Close to midday, when the ground-level radiation budget becomes positive, the temperature of the adjacent air masses rises. Soon after midday, a lapse profile extends through a deep unstable layer. Just after sunset, the surface budget becomes negative, and surface cooling reestablishes the radiation inversion to air masses close to the ground level (Oke 1987). Therefore, air temperature values in the open-space area determined the urban–suburban differences. The vertical stratification of air temperatures inside the deep canyon was almost invariable. It was interesting that, close to the ground level, the gradient of urban–suburban air temperature differences reversed (DT2 . DT5). During the first hours of the day, airflow was limited inside the canyon because of the very weak ambient wind flow. For ambient flow below 5 m s21, there is no connection between flow inside and outside the canyon and flow inside the canyon base rarely exceeded 1 m s21 (Niachou et al. 2008; Georgakis and Santamouris 2008). Because of the thermal properties of the materials at the ground level inside the pedestrian area and the limited wind flow, air temperature differences decreased with height. After midday until late in the afternoon and for distances close to the ground level, the urban–suburban differences were negative. This was the result of two main physical parameters. The first parameter was the air temperature profiles in the lowest part of the atmosphere. Inversion was predominant in air temperature stratification in the suburban station, soon after midday. The second parameter was the current weather conditions. The air close to the ground inside the canyon was 1.48C cooler than the air at the suburban station as a result of the fact that 48% of the total ambient flow was from the NNW with wind speeds exceeding 5 m s21. In

1226


VOLUME 49

FIG. 3. Vertical stratification of air temperature differences between air temperature inside Dervenion canyon and the suburban station during the total experimental period.

addition, during the specific experimental period late in the afternoon, the elevation angle of the sun was negative. Under these circumstances, the ground did not receive direct shortwave radiation and, thus, the cooling rate was important.

value of 0.38C are not considered to be significant for the 95% confidence level (Table 2). In the same period, the air temperatures at 10 and 15 m inside the canyon were close to 18C higher than that of the air at the suburban station.

b. Case B (H/W 5 3.5; L/W 5 8.8)

c. Case C (H/W 5 3; L/W 5 11)

Urban–suburban air temperature differences during the daytime at 2, 5, 10, and 15 m from ground level were estimated to be 28, 18, 28, and 2.88C, respectively (Table 3; Fig. 8). After midday (1500 LT), the differences were equal to 28, 1.88, 3.28, and 48C, respectively (Table 3; Fig. 9). During the afternoon (1800 LT), the air temperatures inside the canyon at heights of 2 and 5 m were estimated to be 20.78 and 20.058C below the air temperatures at the same height levels in the suburban station (Table 3; Fig. 10). Values below the threshold

Urban–suburban air temperature differences during the daytime were estimated at 28, 0.88, 2.38, and 3.58C for the comparison height levels, respectively (Table 3; Fig. 8). After midday (1500 LT), these differences were 3.28, 2.18, 2.98, and 2.78C, respectively (Table 3; Fig. 9). During the afternoon (1800 LT), the air temperature differences at 2 and 5 m were estimated to be 20.58 and 20.18C, respectively (Table 3; Fig. 10). Values below the threshold value of 0.38C were considered not to be significant, for the 95% confidence level, because of the

FIG. 4. As in Fig. 3 but for Kaniggos.

JUNE 2010

GEORGAKIS ET AL.

1227

FIG. 5. As in Fig. 3 but for Voukourestiou.

combined standard uncertainty (Table 2). For the other two height levels, the air temperature was almost the same inside and outside the canyon. There are many similarities among air temperature differences at sites A, B, and C because of their similar geometric characteristics. The mean height of the buildings for these canyons exceeded by 3 times their cross distance from wall to wall (H/W $ 3). The distance between the cross intersection of these roads was less than 11 times their width (L/W , 11). The main conclusion was that urban–suburban differences in air temperature decreased with an increasing H/W aspect ratio. Based on the vertical geometry of urban canyons for cases A and B, air temperature differences were expected to be greater for case B. This was not the case, mainly because of the current weather conditions mentioned previously.

d. Case D (H/W 5 2.5; L/W 5 11.7) During the daytime, urban–suburban air temperature differences for the four comparison heights were estimated at 3.18, 1.88, 38, and 48C, respectively (Table 3; Fig. 8). After midday, the differences were 3.28, 2.88, 4.28, and 4.38C, respectively (Table 3; Fig. 9). During the afternoon, the air temperature differences were 0.78, 1.18, 2.38, and 1.98C, respectively (Table 3; Fig. 10).

e. Case E (H/W 5 2; L/W 5 12) Urban–suburban air temperature differences during the daytime at 2, 5, 10, and 15 m from ground level were estimated to be 5.78, 4.18, 3.68, and 48C, respectively (Table 3; Fig. 8). After midday, the differences were 5.38, 4.38, 4.38, and 48C, respectively (Table 3; Fig. 9). In this street canyon, the total experimental campaign lasted only until midday because of technical problems.

FIG. 6. As in Fig. 3 but for Miltiadou.

1228


VOLUME 49

FIG. 7. As in Fig. 3 but for Ermou.

In comparing values of urban–suburban air temperature differences for cases D and E, it is obvious that these values were higher because of the lower aspect ratios of the corresponding canyons. The aspect ratios in cases D and E were similar (H/W # 2 and L/W . 12). Their orientations were similar (708 and 928 from real north), therefore the amounts of solar radiation received were also similar, and therefore the lowest aspect ratio corresponded to the highest urban–suburban air temperature differences.

6. Discussion of urban–suburban air temperature differences during daytime Accurate air temperature measurements are becoming increasingly necessary for scientific, industrial, and

environmental applications. However, the quality of measured values cannot be determined without an analysis of measurement uncertainty. In previous studies, the threshold of measured data accuracy was only set according to the specifications from the manufacturers’ measuring equipment and the calibration procedure (Eliasson 1996). Uncertainty estimation, reliability, traceability, and methods for reducing uncertainty in air temperature measurements are currently considered in the majority of studies as essential points for their technical soundness. The method presented in section 3 of this paper addresses the problem of uncertainty estimation in a systematic, effective, and easily implemented way that is compatible with the well-established GUM guidelines and current metrological knowledge. Even though the

FIG. 8. Urban–suburban air temperature differences for five urban canyons, in the center of Athens, at 1000 LT.

JUNE 2010

GEORGAKIS ET AL.

1229

FIG. 9. As in Fig. 8 but at 1500 LT.

entire range of factors that may influence uncertainty under specific conditions is not taken into consideration, it is evident that a realistic balance between simplicity and reliability is achieved. Starting from the fact that, according to the manufacturers’ specifications, the accuracy of the thermometers that were used in our study was 6(0.18C 1 0.005T ), where T is the temperature above or below 08C (section 2), three significant digits were initially retained in intermediate uncertainty calculations for purposes of resolution and to avoid rounding errors. In accordance with the intermediate uncertainty calculations of section 3, the measurement data presented in Table 3 are also stated with three decimal digits. For the scope of the

expanded uncertainty calculation, rounding values to one decimal place was carried out only at the end of the calculations, and therefore the expanded uncertainty in Table 2 is stated as 0.38C and, consequently, the temperature differences that are discussed in sections 5a–c are also stated to the first decimal place. The estimated expanded uncertainty of air temperature measurements given in Table 2 was taken into account during the data analysis presented in section 5, and it influences the validity of the main conclusions of this paper. In this context, if the estimated measurement uncertainty of the air temperature data was, in general, greater than or equal to the calculated air temperature differences, then the reliability of the experimental

FIG. 10. As in Fig. 8 but at 1800 LT.

1230


TABLE 4. Prevailing meteorological conditions during the experimental period in Athens.

Expt period

Mean daily value of air temperature (8C)

Mean daily value of wind speed (m s21)

Mean daily wind direction

28 Jun 2001 29 Jun 2001 3 Jul 2001 31 Jul 2001 2 Aug 2001 3 Aug 2001 7 Aug 2001 9 Aug 2001 10 Aug 2001 27 Aug 2001 28 Aug 2001 29 Aug 2001 4 Sep 2001 5 Sep 2001 6 Sep 2001

28 29 24 30 31 30 32 34 34 29 29 29 27 27 26

2.6 3.3 2 5.7 7 7.7 2.6 5.2 4.2 4.5 2.1 2.4 2.7 3.3 5.4

N SW N NNE NNE NNE NNE NNE NE NNE SW SSW SSW SSW W

technique and of the majority of the relevant conclusions would be in reasonable doubt. With the uncertainty analysis of section 3, the quality of the air temperature values recorded inside the different urban canyons in the center of Athens and at the station of the Meteorological Service at the University of Athens is assessed in a quantitative way, safeguarding the veracity of the comparisons made between them so as to study the intensity of the heat-island phenomenon in Athens. Spatial and vertical distributions of urban–suburban air temperature differences for the center of Athens were estimated. During the experimental campaign for all cases, except for case A, the weather conditions were the same. Anticyclone systems were established in the eastern Mediterranean Sea for the summer period, providing a very weak ambient airflow above the measuring points in the center of Athens. The prevailing meteorological conditions during the experimental period in Athens, derived from the central premises of the National Observatory of Athens, are depicted in Table 4. This station, situated on top of Lofos Nymphon at Thissio, is located in the center of the city close to the experimental site area. It was found that the unique characteristics of each urban area strongly affect the urban–suburban air temperature differences. Characteristics that affect the distribution of air temperature inside the city include the flow field above the rooftops, building geometry, and building materials. Traffic and the loss of trees are variables that also affect the vertical distribution of air temperature inside the street canyons. This is in agreement with earlier studies (Shahgedanova et al. 1997; Montavez et al. 2000). The higher the aspect ratio H/W

VOLUME 49

is, the lower the urban–suburban air temperature differences were. An important parameter for the spatial arrangement of urban–suburban air temperature differences was the orientation of the site. Uneven heating inside the canyons depends on exposure to direct solar radiation. For cases A, B, and C, the orientation angles were 338, 128, and 458 from north. For these canyons, during the daytime, only the south wall and part of the canyon’s ground received direct radiation. Inside canyons D and E, with orientations 708 and 928 from north, respectively, direct radiation from the sun was received at the ground level for almost all of the experimental period. Thus, differences between orientation angles of the experimental sites led to different levels of solar radiation penetration into the canyons. It was concluded that urban geometry alone is an insufficient explanation of thermal variations within the city and that current weather conditions play an important role in forming the air temperature regime. This was also the case in many previous studies (Eliasson 1996; Georgakis and Santamouris 2006, 2008; Niachou et al. 2008; Montavez et al. 2000). The greatest urban–suburban air temperature differences for all experimental sites were observed during midday (1500 LT). This was a result of the diurnal circle of the sun in conjunction with the thermal properties of the external building materials and ground materials inside the five different street canyons. The fact that urban–suburban differences increased as a function of height (DT5 , DT10 , DT15) was based on the different air temperature profiles inside and outside the urban area. Air velocity inside a canyon decreases with height as a function of the aspect ratio (Georgakis and Santamouris 2008). Inside urban canyons, with large H/W ratios, the rate of radiative cooling decreases significantly. At the same time, large aspect ratios contribute to a higher absorption of the incoming solar radiation because of multiple reflections from the canyon surfaces (Goh and Chang 1999; Santamouris et al. 2001, 2008; Assimakopoulos et al. 2006; Georgakis and Santamouris 2006, 2008; Niachou et al. 2008). In an open-space area, the air temperature decreases as height increases. By means of urban–suburban differences, it was shown that the air temperature inside a deep canyon during the daytime is almost invariable with height. Microadvection is most likely the cause of the uniform air temperature observed (Eliasson 1996). As a consequence, adjacent to the ground level the gradient of urban–suburban air temperature differences reversed (DT2 . DT5). During nighttime, urban–suburban air temperature differences were found to be equal to 38C, and the frequency of night hours with differences greater than 0.58C was

JUNE 2010

1231

GEORGAKIS ET AL.

unexpectedly low (Eliasson 1996; Lindberg et al. 2003). This was not the case in this study. Differences between urban and suburban air temperatures, during the daytime, sometimes even exceeded 58C (case E, for H/W 5 2). This study proved that after midday until late in the afternoon the air temperature in deep urban canyons (H/W $ 3) was lower than in the suburban area. Negative values of urban–suburban air temperature differences were also observed when strong inversions were predominant in air temperatures close to ground level. This finding is in agreement with previous studies (Lu et al. 1997; Shahgedanova et al. 1997; Hogan and Ferrick 1997; Hafner and Kidder 1999; Montavez et al. 2000; Brown et al. 2004). Anthropogenic heat contributed to the stratification of air temperature differences in the center of Athens. This was proven mainly in case E, in which Ermou canyon, with the highest values of urban– suburban differences, is a well-trafficked pedestrian area in the center of Athens. By means of a statistical model, a correlation coefficient between heat-island intensities and the aspect ratio H/W was calculated (Goh and Chang 1999). This could be a pilot study for future work investigating the quantitative correlation between the possible effects of H/W on urban–suburban differences at several height levels.

7. Conclusions Urban–suburban air temperature differences are attributed to several factors, such as anthropogenic heat released by cars and air-conditioning systems in the city, thermal characteristics of the urban materials, urban geometry, and the reduction of evaporative elements in the city. Urban–rural temperature differences are of interest for comparison between cities, but might, as noted by Oke (1981), be difficult to use within a single city. A good knowledge of the influence of street geometry on temperature over very short distances within cities would be of great value, not least for urban planning (Eliasson 1996). An investigation has been performed to investigate the impact of geometric parameters on potential urban– suburban air temperature differences in the center of Athens during the daytime. The independent parameters were the distance from ground level, the aspect ratio, and the orientation of the canyon in addition to the time period and the current weather conditions. The quality of urban–suburban air temperature differences was determined by the means of measurement uncertainty. It was proven that the vertical geometry (H/W) of the urban canyons plays an important role in the vertical stratification of urban–suburban differences. Higher aspect ratios led to lower urban–suburban air

temperature differences. Sometimes these differences were negative, during afternoon in the lowest parts of deep urban canyons (H/W $ 3) or when strong inversions of air temperature were predominant. At the height levels of 5, 10, and 15 m from the ground, urban– rural differences increased as a function of height. The greatest urban–suburban air temperature differences were observed, for all experimental sites, during midday. The orientation of the experimental site seemed to affect the spatial distribution of air temperature differences inside the city. Acknowledgments. This study is partly financed by the European Commission, Directorate General for Science, Research and Technology under the contract Urbvent: Natural ventilation in urban areas—Potential assessment and optimal facade design, ENK6-CT-2000-00316. The contribution of the commission is gratefully acknowledged. REFERENCES Assimakopoulos, V. D., C. Georgakis, and M. Santamouris, 2006: Experimental validation of computational fluid dynamics code to predict the wind speed in street canyons for passive cooling purposes. Sol. Energy, 80, 423–434. Bottyan, Z., and J. Unger, 2003: A multiple linear statistical model for estimating the mean maximum urban heat island. Theor. Appl. Climatol., 75, 233–243. Brown, M. J., and Coauthors, 2004: Joint Urban 2003 Street Canyon Experiment. Preprints, Symp. on Planning, Nowcasting, and Forecasting in the Urban Zone and Eighth Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, Seattle, WA, Amer. Meteor. Soc., J7.3. [Available online at http://ams.confex.com/ams/pdfpapers/ 74033.pdf.] Crovini, L., A. Actis, G. Coggiola, and A. Mangano, 1992: Accurate thermometry by means of industrial platinum resistance thermometers. Measurement, 10 (1), 31–38. Eliasson, I., 1996: Urban nocturnal temperatures, street geometry and land use. Atmos. Environ., 30, 379–392. Georgakis, C., and M. Santamouris, 2004: On the airflow in urban canyons for ventilation purposes. Int. J. Vent., 3 (1), 53–65. ——, and ——, 2006: Experimental investigation of air flow and temperature stratification in deep urban canyons for natural ventilation purposes. Energy Build., 38, 367–376. ——, and ——, 2008: On the estimation of wind speed in urban canyons for ventilation purposes—Part 1: Coupling between the undisturbed wind speed and the canyon wind. Build. Environ., 43, 1404–1410. Ghiaus, C., F. Allard, M. Santamouris, C. Georgakis, and F. Nicol, 2006: Urban environment influence on natural ventilation potential. Build. Environ., 41, 395–406. Goh, K. C., and C. H. Chang, 1999: The relationship between height to width ratios and the heat island intensity at 22:00 h for Singapore. Int. J. Climatol., 19, 1011–1023. Hafner, J., and S. Q. Kidder, 1999: Urban heat island modeling in conjunction with satellite-derived surface/soil parameters. J. Appl. Meteor., 38, 448–465. Hogan, A., and M. Ferrick, 1997: Winter morning air temperature. J. Appl. Meteor., 36, 52–69.

1232


ISO, 1993: International Vocabulary of Basic and General Terms in Metrology. 2nd ed. International Organisation for Standardisation, 55 pp. ——, 1995: Guide to the Expression of Uncertainty in Measurement. International Organisation for Standardisation, 101 pp. Lindberg, F., I. Eliasson, and B. Holmer, 2003: Urban geometry and temperature variations. Proc. Fifth Int. Conf. on Urban Climate, Vol. 1, University of Lodz, Lodz, Poland, 205–208. Lu, J., S. P. Arya, W. H. Snyder, and R. E. Lawson, 1997: A laboratory study of the urban heat island in a calm and stably stratified environment. Part II: Velocity field. J. Appl. Meteor., 36, 1392–1402. Mihalakakou, P., H. A. Flocas, M. Santamouris, and C. G. Helmis, 2002: Application of neural networks to the simulation of the heat island over Athens, Greece, using synoptic types as a predictor. J. Appl. Meteor., 41, 519–527. Montavez, J. P., A. Rodriguez, and J. I. Jimenez, 2000: A study of the urban heat island of Granada. Int. J. Climatol., 20, 899–911. Niachou, A., I. Livada, and M. Santamouris, 2008: Experimental study of temperature and airflow distribution inside an urban street canyon during hot summer weather conditions—Part I: Air and surface temperatures. Build. Environ., 43, 1383–1392. Oke, T. R., 1981: Canyon geometry and the nocturnal urban heat island: Comparison of scale model and field observations. Int. J. Climatol., 1, 237–254. ——, 1987: Boundary Layer Climates. Routledge, Taylor and Francis Group, 435 pp.

VOLUME 49

Santamouris, M., 2007: Heat island research in Europe: The state of the art. J. Adv. Build. Energy Res., 1, 123–150. ——, N. Papanikolaou, I. Livada, I. Koronakis, C. Georgakis, A. Argiriou, and D. N. Assimakopoulos, 2001: On the impact of urban climate on the energy consumption of buildings. Sol. Energy, 70 (3), 201–216. ——, K. Paraponiaris, and G. Mihalakakou, 2007: Estimating the Ecological footprint of the heat island effect over Athens. Greece Climate Change, 80, 265–276. ——, C. Georgakis, and A. Niachou, 2008: On the estimation of wind speed in urban canyons for ventilation purposes—Part 2: Using data driven techniques to calculate the more probable wind speed in urban canyons for low ambient wind speeds. Build. Environ., 43, 1411–1418. Shahgedanova, M., T. P. Burt, and T. D. Davies, 1997: Some aspects of the three-dimensional heat island in Moscow. Int. J. Climatol., 17, 1451–1465. Tait, G. W. C., 1949: The vertical temperature gradient in the lower atmosphere under daylight conditions. Quart. J. Roy. Meteor. Soc., 75, 287–292. Vardoulakis, S., B. Fisher, N. Gonzalez-Flesca, and K. Pericleous, 2002: Model sensitivity and uncertainty analysis using roadside air quality measurements. Atmos. Environ., 36, 2121– 2134. Vasuki, B., M. Umapathy, and G. Uma, 2008: Uncertainty analysis of temperature measurement system using analytical and interval algorithm. J. Instrum. Sci. Technol., 36 (1), 81–87.