- Email: [email protected]
Evaluating the effectiveness of outdoor evaporative cooling in a hot, arid climate
Accepted Manuscript Evaluating the effectiveness of outdoor evaporative cooling in a hot, arid climate Jay Dhariwal, Prajowal Manandhar, Lindita Bande, Prashanth Marpu, Peter Armstrong, Christoph F. Reinhart PII:
S0360-1323(19)30022-8
DOI:
https://doi.org/10.1016/j.buildenv.2019.01.016
Reference:
BAE 5916
To appear in:
Building and Environment
Received Date: 27 August 2018 Revised Date:
26 December 2018
Accepted Date: 13 January 2019
Please cite this article as: Dhariwal J, Manandhar P, Bande L, Marpu P, Armstrong P, Reinhart CF, Evaluating the effectiveness of outdoor evaporative cooling in a hot, arid climate, Building and Environment (2019), doi: https://doi.org/10.1016/j.buildenv.2019.01.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Evaluating the effectiveness of outdoor evaporative cooling in a hot, arid climate
RI PT
Jay Dhariwal*1, Prajowal Manandhar2, Lindita Bande3, Prashanth Marpu2, Peter Armstrong2, Christoph F. Reinhart1 1
Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
2
Khalifa University, Abu Dhabi UAE.
3
United Arab Emirates University, Al Ain UAE.
EP
TE D
M AN U
SC
Abstract In a previous paper, we presented a novel approach to validate the capability of the biometeorological index, Universal Thermal Climate Index (UTCI), to predict the likelihood of urban dwellers to be outside in a public space for the heating dominated climate of Cambridge, MA. Occupancy patterns were recorded based on Wi-Fi data. The present study extends this approach to the hot and arid climate of United Arab Emirates (UAE) to evaluate the effect of outdoor evaporative coolers on resident presence in a public courtyard. Over a period of ten months, outdoor Wi-Fi access point data was collected in the public courtyard located on a university campus in Abu Dhabi. An analysis of the resulting MacID probes yields a population of 1200 regulars and 3800 visitors present in the courtyard at some point during the study period. Coincident UTCI simulations using ENVI-met strongly correlated with the number of regulars present during lunchtime both during times when the evaporative coolers were on (R2 = 75%) and off (R2 = 61%). Lunchtime attendance peaked for UTCI values in the thermal comfort range of around 24 oC during all seasons. The outdoor evaporative coolers were able to bring the UTCI down from very strong heat stress to between thermal comfort and moderate heat stress range. These findings confirm that UTCI can be used as a reliable environmental performance metric to support the design and preservation of comfortable outdoor spaces both in a hot and a cold climate, across a variety of cultural settings.
AC C
Keywords: biometeorological indices; outdoor thermal comfort; Universal Thermal Climate Index (UTCI); evaporative cooling; Wi-Fi data; 1. Introduction Vibrant public spaces contribute to the social fabric of a city in unique, irreplaceable ways bringing together locals and visitors of all backgrounds. Public outdoor spaces are particularly important as they usually also contribute to the vitality of surrounding streets and city blocks. More people on the streets can lead to increased community interactions and safer neighborhoods, contributing to the social and psychological health of residents [1-3]. With more than half of the world’s population living in cities and extreme weather conditions occurring with increased frequency, the design of comfortable outdoor spaces becomes both a more challenging and relevant task for urban planners. Apart from sufficient seating areas and food services, a variety of both shaded and exposed spaces are known to be key attractors of public spaces. In order to design comfortable outdoor spaces, researchers have therefore studied the relationship
ACCEPTED MANUSCRIPT
SC
RI PT
between outdoor comfort conditions and environmental variables [4-8]. One of the outcomes of this work are so-called biometeorological indices that can be modelled and measured using urban microclimate modelling tools [9-10]. These indices have been correlated with the number of people present in a given environmental setting to find out the optimum thermal comfort ranges for different climates [7, 8, 11-14]. Past studies mostly relied on a small number of test subjects since the research community didn’t have access to an affordable method that would have supported large scale longitudinal studies [15-18]. To address this limitation of traditional methods of collecting occupant behavior data, the first and last authors previously presented a novel approach of using Wi-Fi data as a proxy for the dwelling patterns [19]. Theywere able to divide the subjects into 16,000 regulars and 676,000 visitors in a public courtyard in Cambridge, MA and found that lunchtime attendance for regulars peaked for UTCI values in the thermal comfort and moderate heat stress ranges. The present manuscript extends this approach for the culturally and climatically distinct setting of Abu Dhabi in order to identify similarities and differences in residentbehavior for a variety of outside thermal comfortsituations.
TE D
M AN U
A number of studies previously explored suitableoutdoor thermal comfort ranges for hot climates: Mahmoud [20] found that thermal comfort perception peaked in the range of 22-30 °C Physiological Equivalent Temperature (PET) for the hot and arid climate of Cairo, Egypt. Similarly, Yahia and Johansson [21] measuredthe PET comfort ranges between21 °C and31.3 °C in the hot dry city of Damascus, Syria. Spagnolo and Dear [22] found the thermal neutrality of 26.8 °C for another thermal comfort index, OUT_SET for the humid subtropical climate of Sydney, Australia. For the hot and humid climates, Huang et al. [6] previously found the Thermal Acceptability Vote to be 17.0-33.0 °C from experiments in Wuhan in China. Other studies reported acceptable thermal conditions between 21.3 °C and 28.5 °C for PET in Taiwan [5] and 26.3 °C to 31.7 °C for Operative Temperature in Singapore [4]. Most of the studies in the hot climates deviated from the UTCI thermal comfort zone of 18 °C to26 °C according to the Commission for Thermal Physiology of the International Union of Physiological Sciences [23].
AC C
EP
A number of previous outdoor comfort studies also evaluated the influence of passive strategies such as trees and grass on outdoor thermal comfort in a hot-arid environment [20]. Shashua-bar et al. [24] found that trees or overhead shading mesh significantly reduced thermal discomfort, with the trees marginally better than the mesh, for the hot and arid climate in Israel. The authors also compared the efficiency of water use, which is a scarce resource in many arid regions, in improving thermal comfort for different vegetative landscaping treatments. Kruger et al. [25] reported the influence of sky view factor for influencing the difference between the mean radiant temperature and air temperature. This suggests that narrow street canyons in hot and dry climates reduce the sky view factor thereby reducing the mean radiant temperature, improving thermal comfort [26]. However, the authors are unaware of outdoor thermal comfort studies evaluating the effectiveness of active devices, including evaporative coolers, downdraft cool towers and high pressure mist systems. This is somewhat surprising since evaporative cooling devices have been in use in arid climate zones for thousands of years [22, 27]. Evaporative coolers cool the air through the evaporation of water, utilizing the fact that water has a large enthalpy of vaporization [28-29]. They are considered to be more energy efficient than mechanical air-conditioning systems and particularly effective in hot and dry climate for both
ACCEPTED MANUSCRIPT
Given this context, the specific questions for this study are:
RI PT
indoor and outdoor uses. Efficient systems can lower the dry air temperature to 95% of wet bulb temperature in these climates [30]. Knez and Thorsson [31] examined the influence of culture (Swedish vs. Japanese) on thermal comfort and found that for comparable thermal comfort conditions, Japanese study participants estimated the weather to be warmer than Swedish participants. Aljawabra and Nikolopoulou’s [32] cross cultural research in a hot and arid climate for the cultural settings of Marrakech, North Africa and Phoenix, Arizona, USA found that participants in Marrakech reported to be thermally comfortable over a wider range of temperatures and less sensitive to solar radiation than participants from Phoenix.
SC
1) How well does the methodology proposed for the Cambridge study work in other outdoor spaces? 2) Do biometeorological indices (more specifically, UTCI) work well in a hot climate?
M AN U
3) Is evaporative cooling effective at keeping residents outdoors for a hot and arid climate?
TE D
For this study, authors simulated the biometeorological comfort indices in select courtyards in a university campus in Abu Dhabi, UAE, over a ten-month period (12th May 2016 to 29th September 2016 and 16th November 2016 to 19th April 2017) and correlated the simulations with the number of individuals present in those spaces during lunchtime using Wi-Fi signals from portable electronic devices. Following descriptions of the site, experimental data collection and simulation procedure, studyresults are compared to previously reported results for Cambridge, MA to identify potential climate-induced differences in resident behavior.
AC C
EP
2. Methodology 2.1 Climate and site description The study was conducted in the Masdar Institute of Science and Technology campus in Abu Dhabi, UAE (24.43° N, 54.65° E, altitude 27 m). Abu Dhabi is situated in the Arabian Peninsula, adjoining the Persian Gulf. As shown in Fig. 1, Abu Dhabi’s hot desert climate is characterized by average noon temperatures above 45oC from May to September at relative humidity levels below 35%, i.e. hot dry air. The corresponding UTCI levels in the shade and with light wind suggests that November to March is moderately hot to mild, while May to October months are in the very strong heat stress range. The UTCI levels shown in Fig. 1 were originally established by the International Society of Biometeorology Commission 6 for individuals “walking lightly” [33]. Given these outside conditions, evaporative coolers are commonly found throughout the city to improve summertime conditions. The goal of this study is to quantify how effective these devices are at keeping residents outside.
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
Fig. 1. Average monthly temperature and relative humidity at noon. Data source: epw file for Abu Dhabi.
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
Fig. 2(a). Plan view of the study site
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
Fig. 2(b). Photo of a shaded colonnade outside the Spinney’s canteen
Fig. 2(c). Photo of people having lunch outside Sumo restaurant
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 2(d). Photo showing evaporative coolers outside Sumo restaurant
AC C
EP
TE D
The study sites are the outdoor public spaces in the campus of Masdar Institute of Science and Technology. Masdar Institute (MI) is the first phase of an aspirational urban project. The campus consists of walkable streets, shaded colonnades and courtyard spaces that were specifically designed with outdoor thermal comfort in mind. Fig. 2(a) shows the plan view of the study sites with plenty of outdoor seating of shaded and unshaded tables, chairs, sofas and benches (Fig. 2(b) and Fig. 2(c)), adjacent to several restaurants, cafes and canteen. The locations of the evaporative coolers used [34] in the summer months are also marked on the map and Fig. 2(d) shows them adjacent to the entrance of the Sumo restaurant. Members of Masdar community and visitors from nearby companies frequent these public spaces during the lunch hours. Same as for our earlier study in Cambridge [19], we interpret an outdoor presence of more than six minutes during lunchtime to constitute an active “choice” to be outside, assuming that urban dwellers have a preference towards sitting outside, whenever the weather permits them to do so. 2.2 Measurement of people’s movement in public spaces To track occupant behavior in these outdoor spaces, we used Media Access Control (MAC) numbers or MacIDs captured by the Wi-Fi routers installed all throughout these outdoor spaces (Fig. 2(a)) by the IT department of the institute. A MacID is a fixed hardware address, unique for a given Wi-Fi device such as a smart phone or a laptop to enable it to communicate over a Wi-Fi network. Each MacID consists of 48 bits, six octets or 12 digits. The first three octets are manufacturer specific and the last three octets make the device to be unique for each manufacturer. For ensuring the privacy of the MacID information of each device, while still keeping the MacIDs data to be unique, we only accessed the last three octets of the MacID. The MacID information allowed us to conduct a longitudinal study by distinguishing between Wi-Fi
ACCEPTED MANUSCRIPT
SC
RI PT
signals from different user groups such as regulars vs. visitors and from non-personal W-Fi devices such as weather stations. Given the ubiquitous useof Wi-Fi enabled devicesin today’s world, the tracking of Wi-Fi devices such as smart phones has been shown to correlate closely with the number of people nearby [35-37]. To confirm the same to be true for the test site, the authors also conducted spot measurements on two different days for about an hour each to make sure that the Wi-Fi device count of the people sitting in an area for more than five minutes correlated closely with the head count. The connection of a Wi-Fi device to the institute’s Wi-Fi network was recorded in terms of the MacID, the router name that this device is connected to, the signal strength, date and time stamp of when this session started and the date and time stamp of when this session ended. Wi-Fi devices typically connect to the nearest router in an area and continuously switch connection to another router when a person moves closer to that router. If a person moves inside the building then Wi-Fi device switches to the next router inside. Wi-Fi data was collected continuously for about ten months during two study periods from 12th May 2016 to 29th September 2016 and 16th November 2016 to 19th April 2017.
AC C
EP
TE D
M AN U
2.3 Biometeorological calculations Hourly UTCI distributions were calculated across the study sites for all the workdays throughout the two study periods. The UTCI metric requires meteorological inputs ranging from shortwave and longwave radiation from the sun to local wind speed, dry bulb temperature and relative humidity. The weather data corresponding to these meteorological inputs was collected from the Beam Down Weather Station for the solar radiation data and the Masdar City Field Station for the wind, air temperature and relative humidity data every one minute. This weather data was fed as boundary conditions to ENVI-met (version 4.1), a state-of-the-art CFD based urban microclimate modelling software [9]. The 3D model of the study sites was set up in ENVImet with a grid resolution of 7 m × 7 m (Fig. 3). Table 1 shows the key input parameters for the ENVI-met simulations for each day. The simulations were executed with a time step of 30 minutes from 9 am to 3 pm for each workday. It is to be noted that ENVI-met version 4.1 only allows to model solar radiation for each simulation run using two factors, “short wave solar radiation adjustment” and “fraction of cloud cover”. These factors were derived for each simulation run using the measured solar radiation during the lunchtime from the weather station. Similarly, only a single wind speed and wind direction pair could be provided for each ENVImet run. These values were computed from the vector additions of the one minute wind vectors during the lunchtime. Boundary conditions for dry bulb temperature and relative humidity were entered in ENVI-met hourly with the weather station data.
SC
RI PT
ACCEPTED MANUSCRIPT
M AN U
Fig. 3. ENVI-met model of the study area and surrounding buildings Table 1. ENVI-met input parameters for each day Start and duration of the model Start time Total simulation time (h) Meteorological conditions Wind speed measured at 10 m height
Vector addition of weather station’s wind speed during lunch time Vector addition of weather station’s wind speed during lunch time Average temperature for the day from weather station Average relative humidity for the day from weather station Ratio of global horizontal solar radiation during lunch time from the weather station and ENVI-met Based on direct solar radiation during lunch time from the weather station
TE D
Wind direction
9:00 am 6
Initial temperature atmosphere
EP
Relative humidity at 2 m height (%)
Factor of shortwave adjustment (0.5 to 1.5)
AC C
Fraction of low clouds
Soil data Initial temperature upper layer (0-20 cm)
Monthly ground temperature data from epw file for Abu Dhabi for 0.5 m depth Initial temperature middle layer (20-50 cm) Monthly ground temperature data from epw file for Abu Dhabi for 0.5 m depth Initial temperature deep layer (below 50 Monthly ground temperature data from epw file cm) for Abu Dhabi for 2 m depth Geometric data Grid size (in m3) 7×7×2 No. of grids 58 × 46 × 29
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 4. Comparison between simulated and measured MRT levels in the study site on November 9, 2017
AC C
EP
TE D
The validity of the ENVI-met simulations was performed similar to our previous outdoor thermal comfort study at Cambridge. A black globe thermometer was installed to measure the MRT along with the sensors to measure other weather parameters. The globe temperature method ISO 7726 [38] was used to estimate MRT. The globe method was initially developed to estimate MRT for indoors and was adapted for outdoor use. For better accuracy in the estimation of MRT using this method, researchers [39] suggested to apply a stable low wind speed of 0.5 m/s, if the measured wind speeds were higher. Whenever the measured wind speeds for the MRT experiments happened to be close to 2 m/s, this correction of a stable wind speed was applied. Fig. 4 shows a comparison between ENVI-met simulations for six sensor grid points around the black globe (grey area in the figure) and the black globe measurements on November 9, 2017 near the Spinney’s Canteen (Fig. 2(a)). The six neighboring grid points were used to account for uncertainty due to shading from neighboring buildings and trees. The fluctuation in the measured data vis-à-vis the simulated ENVI-met data may be due to local effects. The ENVI-met simulations follow the measured data reasonably well, thereby validating our simulations. After running the ENVI-met simulations, air temperature, relative humidity, wind and MRT were found for 24 reference sensors at our study sites from their respective spatio-temporal distributions of the ENVI-met simulation output. Fig. 5 show an example distribution for MRT on July 10th, 2016 at noon with the shaded and unshaded reference sensors marked in the figure. These sensors are representative of the seating spaces throughout our study sites. UTCI was then computed for each reference sensor at noon for each workday from the spatio-temporal
ACCEPTED MANUSCRIPT
distributions for air temperature, relative humidity, wind and MRT. For the preferred UTCI calculations (see Fig. 9 below), the reference sensors marked in Fig. 5 were used.
AC C
EP
TE D
M AN U
SC
RI PT
The computed UTCI metric needs to be corrected for the evaporative coolers deployed in the shaded colonnades at our study sites during the summer months from May until October. Psychrometric relations were used to compute the effect of evaporative cooling. Since the evaporative cooling can be assumed to be a constant enthalpy process, air temperature and relative humidity from their ENVI-met distributions were used to find the enthalpy line (see Fig. 8 below). Then this enthalpy and relative humidity corresponding to saturation was used to find the saturation air temperature. The air temperature and relative humidity at saturation correspond to the maximum cooling possible with evaporative coolers for that instant of time. The air temperature and relative humidity point on the psychrometric chart corresponding to evaporative cooling experienced by a person would lie on this constant enthalpy line. This point would lie between the ambient air temperature and relative humidity from their respective ENVI-met distributions and these parameter values at saturation. This point should correspond to the one, which maximizes the thermal comfort based on the UTCI metric. This point is expected to be close to saturation due to the high air temperatures and low relative humidity during the summer months in Abu Dhabi. The wind speed at saturation would correspond to the air flow rate of the discharge stream from the cooler and would move towards the ambient wind speed found from the corresponding ENVI-met distribution.
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 5. ENVI-met simulated MRT distribution in Masdar campus on July 20th, 2016 at noon
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
3. Results We collected Wi-Fi data for breaks for about ten months or 197 workdays from 12th May 2016 to 29th September 2016 (summer having strong to very strong heat stress period; 90 workdays) and then 16th November 2016 to 19th April 2017 (winter/spring having thermal comfort to moderate heat stress period; 107 workdays). Fig. 6 shows that the total Wi-Fi sessions or breaks collected during the study periods was 296,000, out of which 240,000 were collected during the workdays. About 26,000 breaks were attributed to 143 non-personal, “lurking” Wi-Fi devices that were active for more than 3 hours per day and thus, likely caused by non-personal devices such as weather stations, sprinkler systems, etc. These lurking Wi-Fi devices were excluded from further analysis. The remainder of the breaks were generated by 3749 visitors (30 thousand breaks) and 1198 regulars (184 thousand breaks). We defined a “regular” as a Wi-Fi device that was present on 10% of the total workdays (20 days) or more throughout the study periods. Unlike our previous study at Cambridge, we did not further divide the regulars into staff and students as Masdar Institute only has postgraduate students so the staff vs. students’ distinction is not very useful. We confirmed this fact by finding the number of MacIDs who were present only when the academic session was ongoing. This number was found to be less than 10% of the total regulars, validating our hypothesis.
Fig. 6. Sankey diagram of the collected Wi-Fi breaks in the Masdar sites throughout the study periods Fig. 7 shows the median occurrence of Wi-Fi breaks for regulars and visitors on workdays throughout the study periods. The figure also shows expected arrival and departure patterns, which ramp up between 8 am and 9 am, peak around lunchtime and gradually fall between 5 pm and 9 pm. Noon to 1 pm marks the main lunchtime period in Masdar Institute for both the groups throughout the year, showing that the time for taking lunchtime is largely governed by societal norms and institutional logistics (there tend to be less classes during
ACCEPTED MANUSCRIPT
TE D
M AN U
SC
RI PT
midday) rather than ambient weather conditions. Any Wi-Fi session starting between noon and 1 pm was considered a lunch break, if it was longer than six minutes. Six minutes was chosen as the threshold since the dwell time duration data showed a rounding-off happening around every five minutes. All the walking breaks for less than six minutes in the Wi-Fi data rounded off to six minutes. The hourly patterns for median Wi-Fi breaks remained similar even when this analysis was performed for the two study periods separately.
EP
Fig. 7. Median number of Wi-Fi breaks on workdays for regulars and visitors throughout the study periods
AC C
Based on this lunchtime interval, UTCI levels were computed for each workday based on ENVI-met simulations and then evaporative cooling correction was applied for the reference sensors under the shade for all the workdays in summer. For example, for July 20th, 2016, for the reference sensor location under the shaded colonnade next to the Osho restaurant (Fig. 5), Fig. 8 shows the evaporative cooling, a constant enthalpy process, leading to cooling and humidification. The dry bulb temperature changes from 40.2 °C to 24.0 °C on saturation, leading to UTCI reduction from 40.9 °C to 28.2 °C. Fig. 9 correlates preferred UTCI levels to the median number of occupants during lunchtime for regulars and visitors with and without evaporative cooling respectively. Evaporative cooling is used during the summer months at MI. It was observed that due to the limited number of evaporative coolers at our study sites, every individual may not be able to sit close to them to experience UTCI closer to the saturation curve, if he/she wanted to. However, the evaporative coolers had the cooling effect even at a distance, albeit with a higher UTCI. This lead to uncertainty in observed UTCI with the evaporative
ACCEPTED MANUSCRIPT
M AN U
SC
RI PT
cooling. For estimating the correlation in Fig. 9, data was excluded for the Ramadan period falling in June-July 2016 to avoid bias in our results. Ramadan is a holy month for Muslims and it involves them fasting during the day time. The UTCI ranges marked in Fig. 9 from thermal comfort to strong heat stress were established by the International Society of Biometeorology Commission [23, 40]. When faced with the decision to sit under the shaded colonnade or on an unshaded table, it was assumed that the subjects chose the more comfortable condition of the two UTCI predictions. Further, if a subject chose to sit under the shaded colonnade in summer when evaporative coolers are used, it was assumed that for the range of UTCI levels from ambient UTCI to UTCI corresponding to saturation temperature and relative humidity (Fig. 8), a person would choose the UTCI between these values, which maximizes his/her comfort levels. In the case in Fig. 8, the UTCI of 28.2 °C (moderate heat stress) would be the “preferred” UTCI over the UTCI of 40.9 °C (very strong heat stress). The Wi-Fi data collected didn’t allow us to exactly track where the individuals may be located. In order to account for the uncertainty in how close the individuals may be to the evaporative coolers and the uncertainty in MRT predictions, we chose a three degree bin size for UTCI predictions. Three degree bin size of UTCI also helped us to compare our results better with our previous study at Cambridge.
AC C
EP
TE D
Fig. 9 shows regression fits (N>1) for regulars and visitors assuming that the occupancy rates should peak for a certain UTCI range. The corresponding R2 values from 61% to 75% for regulars correspond to and exceed the earlier reported correlations for considerably smaller sample sizes. The lower correlation values for the visitors may be because of them being at Masdar Institute for a random purpose and not making a choice of having lunch outside due to comfort outdoors. Thus, the differentiation between regulars and visitors is helpful in identifying the local behavior patterns. The lunchtime attendance for regulars peaks around 24.5 °C UTCI bin in the thermal comfort range for both the periods with and without evaporative cooling. However, it is possible that people might have preferred a UTCI lower than 24.5 °C in the period with evaporative cooling. Figure 9 also shows the trend line with median values for summer period with the hypothetical case of not having evaporative cooling in summer months. The UTCI values would have gone up to 42.5 °C, if this were the case. This case helps to appreciate how unbearable the conditions would have been without the evaporative cooling and convince the reader that the coolers do their job. This is why the number of lunchtime attendees is so stable over the summer period. The positive and the negative error bars in Fig. 9 are for the 25th and 75th percentile of UTCI values to show the uncertainty in UTCI estimation. The length of the lunch breaks for both the study periods had a median of 20 minutes with a variation from 10 minutes (25th percentile) to 30 minutes (75th percentile). Any appreciable change in the size of dwell times wasn’t observed with the UTCI levels partly because of a rounding off happening at every five minutes but it may also be dictated by the local workplace culture. It is likely that once people choose to have a lunch break outside, they tend to stay outside for a fixed period of time with the weather having a lesser effect.
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
Fig.8. Psychrometric chart layout explaining the evaporative cooling correction for UTCI for a reference sensor on 20th July 2016
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 9. Preferred UTCI levels vs. the median number of regulars (top) and visitors (bottom) during lunchtime for summer and winter/spring
AC C
EP
4. Discussion Our results yield that over the 10 month study period over 1000 members of the Masdar Institute community regularly visited the two courtyards accompanied by around three times as many visitors. This type of data provides fresh insights into the use of these outdoor spaces and confirms the ability of Wi-Fi data to serve as a reliable, low-cost method to quantify how and by whom campus facilities are being used. Thus, the methodology that we had proposed for our Cambridge study works well in other outdoor public spaces as well. In this particular study, the lunchtime attendance peaked for UTCI values in the thermal comfort range of around 24 oC during both the seasons. This suggests that UTCI works well in a hot and arid climate as well. However, it is to be noted that overall UTCI only had a very limited effect on the number of residents frequenting the courtyard for lunch. One of the reasons may be the urban passive design in terms of the narrow street canyons and the shaded colonnades reducing the MRT. This may also be due to the evaporative coolers maintaining outdoor conditions within acceptable bounds, as shown in Fig. 9. The outdoor evaporative coolers were able to bring the UTCI down from very strong heat stress to between thermal comfort and moderate heat stress range for the summer months and they seemed to be effective in keeping the
ACCEPTED MANUSCRIPT
M AN U
SC
RI PT
residents outside. We would like to point the uncertainty in people’s thermal comfort sensations caused by the limited number of evaporative coolers used during our study, since the people closer to the evaporative coolers would have a higher cooling sensation vis-à-vis the others. This may cause the trend for the regulars in Figure 9(top) to move slightly away from the thermal comfort range. However, one may also question how much choice the Masdar community really has in terms of finding other spaces to have lunch. The nearest space for having lunch outside this campus is about 4 km away. Given the somewhat remote location of the campus, the actual choice is to have a home packed lunch by one self at the desk, go to the nearby staff and student quarters or have a communal lunch with colleagues and friends. This choice between solitude and being somewhat warm may have skewed the results, especially for the higher end of the study period without evaporative coolers towards a more tolerant mindset. Or, being more in line with adaptive comfort theory, residents over time get used to warmer weather and accept if it is for lunch, especially if they afterwards return to an air conditioned building. We feel that the objectives of our study have been reasonably met but it would be good to have further studies carried out in hot climates using our approach in other cultural settings to corroborate our findings.
AC C
EP
TE D
Comparing the results between this study for a hot, arid climate at Masdar and colder climate at Cambridge, it is observed that while people looked forward to cooler weather conditions at Masdar in summer (the period with evaporative cooling), the test subjects in Cambridge did not mind a moderate heat stress in winter. The subjects’ choices during the winter/spring (the period without evaporative cooling) at Masdar and summer/fall at Cambridge peaked around 24 °C in the thermal comfort range along expected lines. Another cultural difference observed was in terms of the length of the lunch breaks being longer for Masdar than Cambridge, although a median time of 20 minutes at Masdar is not at all extravagant. The lessons to be learnt from these observations is that it is more important to pay attention to the design of outdoor public spaces for a cooler UTCI (in the thermal comfort range) in summer in a hotter climate and a hotter UTCI (in thermal comfort and moderate heat stress range) in spring in a colder climate. Passive elements such as shading devices, trees and neighboring buildings should be used as much as possible for energy efficiency before the use of active elements such as evaporative coolers in hotter climates. Evaporative coolers use water for cooling but water is a scarce resource in many arid regions. Evaporative coolers will also not be effective in humid regions. Hence, other active options such as radiant cooling, mist fans should also be explored in terms of efficiency of water use to provide similar thermal comfort. Recently, in popular press, there has been an article about Transsolar to use innovative shading systems and solar powered radiant cooling systems to keep people thermally comfortable for FIFA world cup in 2022 in summer time in Qatar [40]. Both of our study sites have been designed well in terms of the passive elements for their respective climates. The buildings in the campus of MI, which are shading each other due to narrow street canyons play a crucial role in the UTCI values obtained above. The Cambridge study is located in a relatively open area, allowing plenty of sunshine in the months with a cooler UTCI.
ACCEPTED MANUSCRIPT
SC
RI PT
5. Conclusions This manuscript extends the approach proposed in a previous paper by the first and the last authors for the use of Wi-Fi data for presence detection of thousands of individuals in public outdoor spaces for the different cultural and climatic setting of Abu Dhabi. Using MacID data, the urban dwellers were divided into regulars and visitors. It was shown that the former group of occupants in the Masdar Institute campus peaked in the thermal comfort range of UTCI levels for both the study periods with and without the evaporative cooling. These results confirm that UTCI can be a useful tool for the design of well-used outdoor spaces. This also demonstrates the effectiveness of the use of outdoor evaporative cooling to bring the UTCI down from very strong heat stress to between thermal comfort and moderate heat stress range. Large urban datasets such as the ones collected for these studies have the potential to provide the city governments with meaningful insights in the use and design of successful outdoor public spaces. Acknowledgments
M AN U
This work was supported by the Cooperative Agreement between the Masdar Institute of Science and Technology (Masdar Institute), Abu Dhabi, UAE, and the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA – Reference 02/Ml/Ml/CP/11/07633/GEN/G/OO for work under the Second Five Year Agreement. References J. Anderson, K. Ruggeri, K. Steemers, F. Huppert, Lively Social Space, Well-Being Activity, and Urban Design: Findings From a Low-Cost Community-Led Public Space Intervention, Environ. Behav. (2016). doi:10.1177/0013916516659108.
[2]
J. Gehl, Cities for people, doi:10.1017/CBO9781107415324.004.
[3]
W. H. Whyte, The social life of small urban spaces, Conservation Foundation, Washington DC, 1980.
[4]
W. Yang, N.H. Wong, S.K. Jusuf, Thermal comfort in outdoor urban spaces in Singapore, Build. Environ. 59 (2013) 426–435.
[6]
[7]
Press,
Copenhagen,
2010.
EP
Island
AC C
[5]
TE D
[1]
T.P. Lin, Thermal perception, adaptation and attendance in a public square in hot and humid regions, Build. Environ. 44 (2009) 2017–2026.
J. Huang, C. Zhou, Y. Zhuo, L. Xu, Y. Jiang, Outdoor thermal environments and activities in open space: An experiment study in humid subtropical climates, Build. Environ. 103 (2016) 238–249. L. Chen, E. Ng, Outdoor thermal comfort and outdoor activities: A review of research in the past decade, Cities. 29 (2012) 118–125.
ACCEPTED MANUSCRIPT
E. Johansson, S. Thorsson, R. Emmanuel, E. Kruger, Instruments and methods in outdoor thermal comfort studies - The need for standardization, Urban Clim. 10 (2014) 346–366.
[9]
M. Bruse et al., ENVI-met, (2017). http://www.model.envi-met.com/hg2e/doku.php.
[10]
A. Matzarakis, F. Rutz, H. Mayer, Modelling Radiation fluxes in simple and complex environments – Basics of the RayMan model, Int. J. Biometeorol. 54 (2010) 131–139.
[11]
M. Nikolopoulou, N. Baker, K. Steemers, Thermal comfort in outdoor urban spaces:
RI PT
[8]
understanding the human parameter. Solar Energy, 70 (2001) 227-235.
L. Katzschner, Behaviour of people in open spaces in dependence of thermal comfort
SC
[12]
conditions. In Paper presented at the PLEA2006 – The 23rd conference on passive and
M AN U
low energy architecture, Geneva, Switzerland. 2006.
J. Zacharias, T. Stathopoulos, H. Wu, Microclimate and downtown open space activity, Environ. Behav. 33 (2001) 296–315.
[14]
M. Nikolopoulou, S. Lykoudis, Thermal comfort in outdoor urban spaces: Analysis across different European countries., Build. Environ. 41 (2006) 1455–1470.
[15]
T. Xi, Q. Li, A. Mochida, Q. Meng, Study on the outdoor thermal environment and thermal comfort around campus clusters in subtropical urban areas, Build. Environ. 52 (2012) 162–170.
[16]
S. Becker, O. Potchter, Y. Yaakov, Calculated and observed human thermal sensation in an extremely hot and dry climate, Energy Build. 35 (2003) 747–756.
[17]
B. Givoni, M. Noguchi, H. Saaroni, O. Pochter, Y. Yaacov, N. Feller, S. Becker. Outdoor comfort research issues, Energy Build. 35 (2003) 77–86.
[18]
V. Cheng, E. Ng, C. Chan, B. Givoni, Outdoor thermal comfort study in a sub-tropical climate: a longitudinal study based in Hong Kong, Int. J. Biometeorol. 56 (2012) 43–56.
AC C
EP
TE D
[13]
[19] C. F. Reinhart, J. Dhariwal and K. Gero, Biometeorological indices explain outside urban lunchbreak patterns collected through Wi-Fi data, Building and Environment, 126 (2017) 422–430. [20] A. H. A. Mahmoud, Analysis of the microclimatic and human comfort conditions in an urban park in hot and arid regions, Building and Environment 46(2011) 2641-2656.
ACCEPTED MANUSCRIPT
[21] M. W. Yahia and E. Johansson, Evaluating the behaviour of different thermal indices by investigating various outdoor urban environments in the hot dry city of Damascus, Syria, Int J Biometeorol 57(2013) 615–630.
RI PT
[22] J. Spagnolo, R. D. Dear, A field study of thermal comfort in outdoor and semi-outdoor environments in subtropical Sydney Australia, Building and Environment 38 (2003) 721 – 738.
L. Shashua-Bar, D. Pearlmutter and E. Erell, The influence of trees and grass on outdoor thermal comfort in a hot-arid environment, Int. J. Climatol. 31 (2011) 1498–1506. (2011) https://doi.org/10.1002/joc.2177
M AN U
[24]
SC
[23] P. Bröde, D. Fiala, K. Błażejczyk, I. Holmér, B. Jendritzky, G. Kampmann, B. Tinz, G. Havenith, Deriving the operational procedure for the Universal Thermal Climate Index (UTCI), Int. J. Biometeorol. 56 (2012) 481–494.
[25] E. L. Kruger, F. O. Minella, F. Rasia, Impact of urban geometry on outdoor thermal comfort and air quality from field measurements in Curitiba, Brazil, Building and Environment 46(2011) 621-34. [26] E. Johansson, Influence of urban geometry on outdoor thermal comfort in a hot dry climate: A study in Fez, Morocco, Building and Environment 41 (2006) 1326–1338.
TE D
[27] A. R. Dehghani-sanij, M. Soltani, K. Raahemifar, A new design of wind tower for passive ventilation in buildings to reduce energy consumption in windy regions, Renewable and Sustainable Energy Reviews 42 (2015), 182-195.
EP
[28] J. K. Nayak, and J.A. Prajapati, Handbook on Energy Conscious Buildings, IIT Bombay and Solar Energy Centre, Ministry of Non-conventional Energy Sources, Government of India: R & D project no. 3/4(03)/99-SEC, 2006.
AC C
[29] N. K. Bansal, G. Hauser and G. Minke, Passive building design, Elsevier Science, New York, 1994. [30] HVAC Systems and Equipment (SI ed.). Atlanta, GA: American Society of Heating Refrigeration and Air-conditioning Engineers (ASHRAE). 2012. p. 41.1. [31] I. Knez, S. Thorsson, Influences of culture and environmental attitude on thermal, emotional and perceptual evaluations of a public square, Int J Biometeorol. 50 (2006) 258–268. [32] F. Aljawabra and M. Nikolopoulou, Thermal comfort in urban spaces: a cross-cultural study in the hot arid climate, International Journal of Biometeorology 62 (2018) 1901–1909.
ACCEPTED MANUSCRIPT
[33] UTCI Assessment Scale (2017). http://www.utci.org/utci_doku.php. Floor Standing Air Cooler (2018). https://outdoorcooler.ae/portable-coolers/floorstanding-air-cooler/
[35]
S. Jiang, J. Ferreira, M.C. Gonzalez, Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore, IEEE Trans. Big Data. PP (2015). doi:10.1109/TBDATA.2016.2631141.
[36]
A. Sevtsuk, S. Huang, Mapping the MIT campus in real time using WiFi, in: M. Foch (Ed.), Handb. Res. Urban Informatics Pract. Promise Real-Time City, IGI Global, 2009: pp. 325–337. doi:10.4018/978-1-60566-152-0.ch022.
[37]
J. Freudiger, Short: How Talkative is your Mobile Device? An Experimental Study of WiFi Probe Requests., in: WiSec’15 June 22-26 2015, New York City, NY, USA. ACM 9781-4503-3623-9/15/06, 2015.
M AN U
SC
RI PT
[34]
[38] ISO, international standard 7726 (1998) Thermal environments: instruments and methods for measuring physical quantities. International Standard Organization, Geneva.
TE D
[39] Y. Chen, T. Lin, A. Matzarakis, Comparison of mean radiant temperature from field experiment and modelling: a case study in Freiburg, Germany, Theor Appl Climatol 118 (2014): 535–551.
AC C
EP
[40] Transsolar, http://www.carltonservices.co.uk/blog/innovative-air-conditioning-at-the-2022qatar-world-cup/
ACCEPTED MANUSCRIPT
Highlights
-
AC C
EP
TE D
M AN U
SC
-
Extended the novel approach from our previous paper to use Wi-Fi data from smartphones to study the dwelling patterns of 5,000 individuals in outdoor public spaces in Masdar Institute campus, Abu Dhabi Lunchbreak patterns of 1,200 regulars correlated with Universal Thermal Climate Index (UTCI) simulations The outdoor evaporative coolers bring the UTCI down from very strong heat stress to between thermal comfort and moderate heat stress range in the hot and arid climate of Abu Dhabi. UTCI is a reliable performance metric for the design of outdoor spaces in both a hot and a cold climate
RI PT
-
S0360-1323(19)30022-8
DOI:
https://doi.org/10.1016/j.buildenv.2019.01.016
Reference:
BAE 5916
To appear in:
Building and Environment
Received Date: 27 August 2018 Revised Date:
26 December 2018
Accepted Date: 13 January 2019
Please cite this article as: Dhariwal J, Manandhar P, Bande L, Marpu P, Armstrong P, Reinhart CF, Evaluating the effectiveness of outdoor evaporative cooling in a hot, arid climate, Building and Environment (2019), doi: https://doi.org/10.1016/j.buildenv.2019.01.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Evaluating the effectiveness of outdoor evaporative cooling in a hot, arid climate
RI PT
Jay Dhariwal*1, Prajowal Manandhar2, Lindita Bande3, Prashanth Marpu2, Peter Armstrong2, Christoph F. Reinhart1 1
Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
2
Khalifa University, Abu Dhabi UAE.
3
United Arab Emirates University, Al Ain UAE.
EP
TE D
M AN U
SC
Abstract In a previous paper, we presented a novel approach to validate the capability of the biometeorological index, Universal Thermal Climate Index (UTCI), to predict the likelihood of urban dwellers to be outside in a public space for the heating dominated climate of Cambridge, MA. Occupancy patterns were recorded based on Wi-Fi data. The present study extends this approach to the hot and arid climate of United Arab Emirates (UAE) to evaluate the effect of outdoor evaporative coolers on resident presence in a public courtyard. Over a period of ten months, outdoor Wi-Fi access point data was collected in the public courtyard located on a university campus in Abu Dhabi. An analysis of the resulting MacID probes yields a population of 1200 regulars and 3800 visitors present in the courtyard at some point during the study period. Coincident UTCI simulations using ENVI-met strongly correlated with the number of regulars present during lunchtime both during times when the evaporative coolers were on (R2 = 75%) and off (R2 = 61%). Lunchtime attendance peaked for UTCI values in the thermal comfort range of around 24 oC during all seasons. The outdoor evaporative coolers were able to bring the UTCI down from very strong heat stress to between thermal comfort and moderate heat stress range. These findings confirm that UTCI can be used as a reliable environmental performance metric to support the design and preservation of comfortable outdoor spaces both in a hot and a cold climate, across a variety of cultural settings.
AC C
Keywords: biometeorological indices; outdoor thermal comfort; Universal Thermal Climate Index (UTCI); evaporative cooling; Wi-Fi data; 1. Introduction Vibrant public spaces contribute to the social fabric of a city in unique, irreplaceable ways bringing together locals and visitors of all backgrounds. Public outdoor spaces are particularly important as they usually also contribute to the vitality of surrounding streets and city blocks. More people on the streets can lead to increased community interactions and safer neighborhoods, contributing to the social and psychological health of residents [1-3]. With more than half of the world’s population living in cities and extreme weather conditions occurring with increased frequency, the design of comfortable outdoor spaces becomes both a more challenging and relevant task for urban planners. Apart from sufficient seating areas and food services, a variety of both shaded and exposed spaces are known to be key attractors of public spaces. In order to design comfortable outdoor spaces, researchers have therefore studied the relationship
ACCEPTED MANUSCRIPT
SC
RI PT
between outdoor comfort conditions and environmental variables [4-8]. One of the outcomes of this work are so-called biometeorological indices that can be modelled and measured using urban microclimate modelling tools [9-10]. These indices have been correlated with the number of people present in a given environmental setting to find out the optimum thermal comfort ranges for different climates [7, 8, 11-14]. Past studies mostly relied on a small number of test subjects since the research community didn’t have access to an affordable method that would have supported large scale longitudinal studies [15-18]. To address this limitation of traditional methods of collecting occupant behavior data, the first and last authors previously presented a novel approach of using Wi-Fi data as a proxy for the dwelling patterns [19]. Theywere able to divide the subjects into 16,000 regulars and 676,000 visitors in a public courtyard in Cambridge, MA and found that lunchtime attendance for regulars peaked for UTCI values in the thermal comfort and moderate heat stress ranges. The present manuscript extends this approach for the culturally and climatically distinct setting of Abu Dhabi in order to identify similarities and differences in residentbehavior for a variety of outside thermal comfortsituations.
TE D
M AN U
A number of studies previously explored suitableoutdoor thermal comfort ranges for hot climates: Mahmoud [20] found that thermal comfort perception peaked in the range of 22-30 °C Physiological Equivalent Temperature (PET) for the hot and arid climate of Cairo, Egypt. Similarly, Yahia and Johansson [21] measuredthe PET comfort ranges between21 °C and31.3 °C in the hot dry city of Damascus, Syria. Spagnolo and Dear [22] found the thermal neutrality of 26.8 °C for another thermal comfort index, OUT_SET for the humid subtropical climate of Sydney, Australia. For the hot and humid climates, Huang et al. [6] previously found the Thermal Acceptability Vote to be 17.0-33.0 °C from experiments in Wuhan in China. Other studies reported acceptable thermal conditions between 21.3 °C and 28.5 °C for PET in Taiwan [5] and 26.3 °C to 31.7 °C for Operative Temperature in Singapore [4]. Most of the studies in the hot climates deviated from the UTCI thermal comfort zone of 18 °C to26 °C according to the Commission for Thermal Physiology of the International Union of Physiological Sciences [23].
AC C
EP
A number of previous outdoor comfort studies also evaluated the influence of passive strategies such as trees and grass on outdoor thermal comfort in a hot-arid environment [20]. Shashua-bar et al. [24] found that trees or overhead shading mesh significantly reduced thermal discomfort, with the trees marginally better than the mesh, for the hot and arid climate in Israel. The authors also compared the efficiency of water use, which is a scarce resource in many arid regions, in improving thermal comfort for different vegetative landscaping treatments. Kruger et al. [25] reported the influence of sky view factor for influencing the difference between the mean radiant temperature and air temperature. This suggests that narrow street canyons in hot and dry climates reduce the sky view factor thereby reducing the mean radiant temperature, improving thermal comfort [26]. However, the authors are unaware of outdoor thermal comfort studies evaluating the effectiveness of active devices, including evaporative coolers, downdraft cool towers and high pressure mist systems. This is somewhat surprising since evaporative cooling devices have been in use in arid climate zones for thousands of years [22, 27]. Evaporative coolers cool the air through the evaporation of water, utilizing the fact that water has a large enthalpy of vaporization [28-29]. They are considered to be more energy efficient than mechanical air-conditioning systems and particularly effective in hot and dry climate for both
ACCEPTED MANUSCRIPT
Given this context, the specific questions for this study are:
RI PT
indoor and outdoor uses. Efficient systems can lower the dry air temperature to 95% of wet bulb temperature in these climates [30]. Knez and Thorsson [31] examined the influence of culture (Swedish vs. Japanese) on thermal comfort and found that for comparable thermal comfort conditions, Japanese study participants estimated the weather to be warmer than Swedish participants. Aljawabra and Nikolopoulou’s [32] cross cultural research in a hot and arid climate for the cultural settings of Marrakech, North Africa and Phoenix, Arizona, USA found that participants in Marrakech reported to be thermally comfortable over a wider range of temperatures and less sensitive to solar radiation than participants from Phoenix.
SC
1) How well does the methodology proposed for the Cambridge study work in other outdoor spaces? 2) Do biometeorological indices (more specifically, UTCI) work well in a hot climate?
M AN U
3) Is evaporative cooling effective at keeping residents outdoors for a hot and arid climate?
TE D
For this study, authors simulated the biometeorological comfort indices in select courtyards in a university campus in Abu Dhabi, UAE, over a ten-month period (12th May 2016 to 29th September 2016 and 16th November 2016 to 19th April 2017) and correlated the simulations with the number of individuals present in those spaces during lunchtime using Wi-Fi signals from portable electronic devices. Following descriptions of the site, experimental data collection and simulation procedure, studyresults are compared to previously reported results for Cambridge, MA to identify potential climate-induced differences in resident behavior.
AC C
EP
2. Methodology 2.1 Climate and site description The study was conducted in the Masdar Institute of Science and Technology campus in Abu Dhabi, UAE (24.43° N, 54.65° E, altitude 27 m). Abu Dhabi is situated in the Arabian Peninsula, adjoining the Persian Gulf. As shown in Fig. 1, Abu Dhabi’s hot desert climate is characterized by average noon temperatures above 45oC from May to September at relative humidity levels below 35%, i.e. hot dry air. The corresponding UTCI levels in the shade and with light wind suggests that November to March is moderately hot to mild, while May to October months are in the very strong heat stress range. The UTCI levels shown in Fig. 1 were originally established by the International Society of Biometeorology Commission 6 for individuals “walking lightly” [33]. Given these outside conditions, evaporative coolers are commonly found throughout the city to improve summertime conditions. The goal of this study is to quantify how effective these devices are at keeping residents outside.
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
Fig. 1. Average monthly temperature and relative humidity at noon. Data source: epw file for Abu Dhabi.
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
Fig. 2(a). Plan view of the study site
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
Fig. 2(b). Photo of a shaded colonnade outside the Spinney’s canteen
Fig. 2(c). Photo of people having lunch outside Sumo restaurant
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 2(d). Photo showing evaporative coolers outside Sumo restaurant
AC C
EP
TE D
The study sites are the outdoor public spaces in the campus of Masdar Institute of Science and Technology. Masdar Institute (MI) is the first phase of an aspirational urban project. The campus consists of walkable streets, shaded colonnades and courtyard spaces that were specifically designed with outdoor thermal comfort in mind. Fig. 2(a) shows the plan view of the study sites with plenty of outdoor seating of shaded and unshaded tables, chairs, sofas and benches (Fig. 2(b) and Fig. 2(c)), adjacent to several restaurants, cafes and canteen. The locations of the evaporative coolers used [34] in the summer months are also marked on the map and Fig. 2(d) shows them adjacent to the entrance of the Sumo restaurant. Members of Masdar community and visitors from nearby companies frequent these public spaces during the lunch hours. Same as for our earlier study in Cambridge [19], we interpret an outdoor presence of more than six minutes during lunchtime to constitute an active “choice” to be outside, assuming that urban dwellers have a preference towards sitting outside, whenever the weather permits them to do so. 2.2 Measurement of people’s movement in public spaces To track occupant behavior in these outdoor spaces, we used Media Access Control (MAC) numbers or MacIDs captured by the Wi-Fi routers installed all throughout these outdoor spaces (Fig. 2(a)) by the IT department of the institute. A MacID is a fixed hardware address, unique for a given Wi-Fi device such as a smart phone or a laptop to enable it to communicate over a Wi-Fi network. Each MacID consists of 48 bits, six octets or 12 digits. The first three octets are manufacturer specific and the last three octets make the device to be unique for each manufacturer. For ensuring the privacy of the MacID information of each device, while still keeping the MacIDs data to be unique, we only accessed the last three octets of the MacID. The MacID information allowed us to conduct a longitudinal study by distinguishing between Wi-Fi
ACCEPTED MANUSCRIPT
SC
RI PT
signals from different user groups such as regulars vs. visitors and from non-personal W-Fi devices such as weather stations. Given the ubiquitous useof Wi-Fi enabled devicesin today’s world, the tracking of Wi-Fi devices such as smart phones has been shown to correlate closely with the number of people nearby [35-37]. To confirm the same to be true for the test site, the authors also conducted spot measurements on two different days for about an hour each to make sure that the Wi-Fi device count of the people sitting in an area for more than five minutes correlated closely with the head count. The connection of a Wi-Fi device to the institute’s Wi-Fi network was recorded in terms of the MacID, the router name that this device is connected to, the signal strength, date and time stamp of when this session started and the date and time stamp of when this session ended. Wi-Fi devices typically connect to the nearest router in an area and continuously switch connection to another router when a person moves closer to that router. If a person moves inside the building then Wi-Fi device switches to the next router inside. Wi-Fi data was collected continuously for about ten months during two study periods from 12th May 2016 to 29th September 2016 and 16th November 2016 to 19th April 2017.
AC C
EP
TE D
M AN U
2.3 Biometeorological calculations Hourly UTCI distributions were calculated across the study sites for all the workdays throughout the two study periods. The UTCI metric requires meteorological inputs ranging from shortwave and longwave radiation from the sun to local wind speed, dry bulb temperature and relative humidity. The weather data corresponding to these meteorological inputs was collected from the Beam Down Weather Station for the solar radiation data and the Masdar City Field Station for the wind, air temperature and relative humidity data every one minute. This weather data was fed as boundary conditions to ENVI-met (version 4.1), a state-of-the-art CFD based urban microclimate modelling software [9]. The 3D model of the study sites was set up in ENVImet with a grid resolution of 7 m × 7 m (Fig. 3). Table 1 shows the key input parameters for the ENVI-met simulations for each day. The simulations were executed with a time step of 30 minutes from 9 am to 3 pm for each workday. It is to be noted that ENVI-met version 4.1 only allows to model solar radiation for each simulation run using two factors, “short wave solar radiation adjustment” and “fraction of cloud cover”. These factors were derived for each simulation run using the measured solar radiation during the lunchtime from the weather station. Similarly, only a single wind speed and wind direction pair could be provided for each ENVImet run. These values were computed from the vector additions of the one minute wind vectors during the lunchtime. Boundary conditions for dry bulb temperature and relative humidity were entered in ENVI-met hourly with the weather station data.
SC
RI PT
ACCEPTED MANUSCRIPT
M AN U
Fig. 3. ENVI-met model of the study area and surrounding buildings Table 1. ENVI-met input parameters for each day Start and duration of the model Start time Total simulation time (h) Meteorological conditions Wind speed measured at 10 m height
Vector addition of weather station’s wind speed during lunch time Vector addition of weather station’s wind speed during lunch time Average temperature for the day from weather station Average relative humidity for the day from weather station Ratio of global horizontal solar radiation during lunch time from the weather station and ENVI-met Based on direct solar radiation during lunch time from the weather station
TE D
Wind direction
9:00 am 6
Initial temperature atmosphere
EP
Relative humidity at 2 m height (%)
Factor of shortwave adjustment (0.5 to 1.5)
AC C
Fraction of low clouds
Soil data Initial temperature upper layer (0-20 cm)
Monthly ground temperature data from epw file for Abu Dhabi for 0.5 m depth Initial temperature middle layer (20-50 cm) Monthly ground temperature data from epw file for Abu Dhabi for 0.5 m depth Initial temperature deep layer (below 50 Monthly ground temperature data from epw file cm) for Abu Dhabi for 2 m depth Geometric data Grid size (in m3) 7×7×2 No. of grids 58 × 46 × 29
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 4. Comparison between simulated and measured MRT levels in the study site on November 9, 2017
AC C
EP
TE D
The validity of the ENVI-met simulations was performed similar to our previous outdoor thermal comfort study at Cambridge. A black globe thermometer was installed to measure the MRT along with the sensors to measure other weather parameters. The globe temperature method ISO 7726 [38] was used to estimate MRT. The globe method was initially developed to estimate MRT for indoors and was adapted for outdoor use. For better accuracy in the estimation of MRT using this method, researchers [39] suggested to apply a stable low wind speed of 0.5 m/s, if the measured wind speeds were higher. Whenever the measured wind speeds for the MRT experiments happened to be close to 2 m/s, this correction of a stable wind speed was applied. Fig. 4 shows a comparison between ENVI-met simulations for six sensor grid points around the black globe (grey area in the figure) and the black globe measurements on November 9, 2017 near the Spinney’s Canteen (Fig. 2(a)). The six neighboring grid points were used to account for uncertainty due to shading from neighboring buildings and trees. The fluctuation in the measured data vis-à-vis the simulated ENVI-met data may be due to local effects. The ENVI-met simulations follow the measured data reasonably well, thereby validating our simulations. After running the ENVI-met simulations, air temperature, relative humidity, wind and MRT were found for 24 reference sensors at our study sites from their respective spatio-temporal distributions of the ENVI-met simulation output. Fig. 5 show an example distribution for MRT on July 10th, 2016 at noon with the shaded and unshaded reference sensors marked in the figure. These sensors are representative of the seating spaces throughout our study sites. UTCI was then computed for each reference sensor at noon for each workday from the spatio-temporal
ACCEPTED MANUSCRIPT
distributions for air temperature, relative humidity, wind and MRT. For the preferred UTCI calculations (see Fig. 9 below), the reference sensors marked in Fig. 5 were used.
AC C
EP
TE D
M AN U
SC
RI PT
The computed UTCI metric needs to be corrected for the evaporative coolers deployed in the shaded colonnades at our study sites during the summer months from May until October. Psychrometric relations were used to compute the effect of evaporative cooling. Since the evaporative cooling can be assumed to be a constant enthalpy process, air temperature and relative humidity from their ENVI-met distributions were used to find the enthalpy line (see Fig. 8 below). Then this enthalpy and relative humidity corresponding to saturation was used to find the saturation air temperature. The air temperature and relative humidity at saturation correspond to the maximum cooling possible with evaporative coolers for that instant of time. The air temperature and relative humidity point on the psychrometric chart corresponding to evaporative cooling experienced by a person would lie on this constant enthalpy line. This point would lie between the ambient air temperature and relative humidity from their respective ENVI-met distributions and these parameter values at saturation. This point should correspond to the one, which maximizes the thermal comfort based on the UTCI metric. This point is expected to be close to saturation due to the high air temperatures and low relative humidity during the summer months in Abu Dhabi. The wind speed at saturation would correspond to the air flow rate of the discharge stream from the cooler and would move towards the ambient wind speed found from the corresponding ENVI-met distribution.
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 5. ENVI-met simulated MRT distribution in Masdar campus on July 20th, 2016 at noon
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
3. Results We collected Wi-Fi data for breaks for about ten months or 197 workdays from 12th May 2016 to 29th September 2016 (summer having strong to very strong heat stress period; 90 workdays) and then 16th November 2016 to 19th April 2017 (winter/spring having thermal comfort to moderate heat stress period; 107 workdays). Fig. 6 shows that the total Wi-Fi sessions or breaks collected during the study periods was 296,000, out of which 240,000 were collected during the workdays. About 26,000 breaks were attributed to 143 non-personal, “lurking” Wi-Fi devices that were active for more than 3 hours per day and thus, likely caused by non-personal devices such as weather stations, sprinkler systems, etc. These lurking Wi-Fi devices were excluded from further analysis. The remainder of the breaks were generated by 3749 visitors (30 thousand breaks) and 1198 regulars (184 thousand breaks). We defined a “regular” as a Wi-Fi device that was present on 10% of the total workdays (20 days) or more throughout the study periods. Unlike our previous study at Cambridge, we did not further divide the regulars into staff and students as Masdar Institute only has postgraduate students so the staff vs. students’ distinction is not very useful. We confirmed this fact by finding the number of MacIDs who were present only when the academic session was ongoing. This number was found to be less than 10% of the total regulars, validating our hypothesis.
Fig. 6. Sankey diagram of the collected Wi-Fi breaks in the Masdar sites throughout the study periods Fig. 7 shows the median occurrence of Wi-Fi breaks for regulars and visitors on workdays throughout the study periods. The figure also shows expected arrival and departure patterns, which ramp up between 8 am and 9 am, peak around lunchtime and gradually fall between 5 pm and 9 pm. Noon to 1 pm marks the main lunchtime period in Masdar Institute for both the groups throughout the year, showing that the time for taking lunchtime is largely governed by societal norms and institutional logistics (there tend to be less classes during
ACCEPTED MANUSCRIPT
TE D
M AN U
SC
RI PT
midday) rather than ambient weather conditions. Any Wi-Fi session starting between noon and 1 pm was considered a lunch break, if it was longer than six minutes. Six minutes was chosen as the threshold since the dwell time duration data showed a rounding-off happening around every five minutes. All the walking breaks for less than six minutes in the Wi-Fi data rounded off to six minutes. The hourly patterns for median Wi-Fi breaks remained similar even when this analysis was performed for the two study periods separately.
EP
Fig. 7. Median number of Wi-Fi breaks on workdays for regulars and visitors throughout the study periods
AC C
Based on this lunchtime interval, UTCI levels were computed for each workday based on ENVI-met simulations and then evaporative cooling correction was applied for the reference sensors under the shade for all the workdays in summer. For example, for July 20th, 2016, for the reference sensor location under the shaded colonnade next to the Osho restaurant (Fig. 5), Fig. 8 shows the evaporative cooling, a constant enthalpy process, leading to cooling and humidification. The dry bulb temperature changes from 40.2 °C to 24.0 °C on saturation, leading to UTCI reduction from 40.9 °C to 28.2 °C. Fig. 9 correlates preferred UTCI levels to the median number of occupants during lunchtime for regulars and visitors with and without evaporative cooling respectively. Evaporative cooling is used during the summer months at MI. It was observed that due to the limited number of evaporative coolers at our study sites, every individual may not be able to sit close to them to experience UTCI closer to the saturation curve, if he/she wanted to. However, the evaporative coolers had the cooling effect even at a distance, albeit with a higher UTCI. This lead to uncertainty in observed UTCI with the evaporative
ACCEPTED MANUSCRIPT
M AN U
SC
RI PT
cooling. For estimating the correlation in Fig. 9, data was excluded for the Ramadan period falling in June-July 2016 to avoid bias in our results. Ramadan is a holy month for Muslims and it involves them fasting during the day time. The UTCI ranges marked in Fig. 9 from thermal comfort to strong heat stress were established by the International Society of Biometeorology Commission [23, 40]. When faced with the decision to sit under the shaded colonnade or on an unshaded table, it was assumed that the subjects chose the more comfortable condition of the two UTCI predictions. Further, if a subject chose to sit under the shaded colonnade in summer when evaporative coolers are used, it was assumed that for the range of UTCI levels from ambient UTCI to UTCI corresponding to saturation temperature and relative humidity (Fig. 8), a person would choose the UTCI between these values, which maximizes his/her comfort levels. In the case in Fig. 8, the UTCI of 28.2 °C (moderate heat stress) would be the “preferred” UTCI over the UTCI of 40.9 °C (very strong heat stress). The Wi-Fi data collected didn’t allow us to exactly track where the individuals may be located. In order to account for the uncertainty in how close the individuals may be to the evaporative coolers and the uncertainty in MRT predictions, we chose a three degree bin size for UTCI predictions. Three degree bin size of UTCI also helped us to compare our results better with our previous study at Cambridge.
AC C
EP
TE D
Fig. 9 shows regression fits (N>1) for regulars and visitors assuming that the occupancy rates should peak for a certain UTCI range. The corresponding R2 values from 61% to 75% for regulars correspond to and exceed the earlier reported correlations for considerably smaller sample sizes. The lower correlation values for the visitors may be because of them being at Masdar Institute for a random purpose and not making a choice of having lunch outside due to comfort outdoors. Thus, the differentiation between regulars and visitors is helpful in identifying the local behavior patterns. The lunchtime attendance for regulars peaks around 24.5 °C UTCI bin in the thermal comfort range for both the periods with and without evaporative cooling. However, it is possible that people might have preferred a UTCI lower than 24.5 °C in the period with evaporative cooling. Figure 9 also shows the trend line with median values for summer period with the hypothetical case of not having evaporative cooling in summer months. The UTCI values would have gone up to 42.5 °C, if this were the case. This case helps to appreciate how unbearable the conditions would have been without the evaporative cooling and convince the reader that the coolers do their job. This is why the number of lunchtime attendees is so stable over the summer period. The positive and the negative error bars in Fig. 9 are for the 25th and 75th percentile of UTCI values to show the uncertainty in UTCI estimation. The length of the lunch breaks for both the study periods had a median of 20 minutes with a variation from 10 minutes (25th percentile) to 30 minutes (75th percentile). Any appreciable change in the size of dwell times wasn’t observed with the UTCI levels partly because of a rounding off happening at every five minutes but it may also be dictated by the local workplace culture. It is likely that once people choose to have a lunch break outside, they tend to stay outside for a fixed period of time with the weather having a lesser effect.
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
Fig.8. Psychrometric chart layout explaining the evaporative cooling correction for UTCI for a reference sensor on 20th July 2016
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
Fig. 9. Preferred UTCI levels vs. the median number of regulars (top) and visitors (bottom) during lunchtime for summer and winter/spring
AC C
EP
4. Discussion Our results yield that over the 10 month study period over 1000 members of the Masdar Institute community regularly visited the two courtyards accompanied by around three times as many visitors. This type of data provides fresh insights into the use of these outdoor spaces and confirms the ability of Wi-Fi data to serve as a reliable, low-cost method to quantify how and by whom campus facilities are being used. Thus, the methodology that we had proposed for our Cambridge study works well in other outdoor public spaces as well. In this particular study, the lunchtime attendance peaked for UTCI values in the thermal comfort range of around 24 oC during both the seasons. This suggests that UTCI works well in a hot and arid climate as well. However, it is to be noted that overall UTCI only had a very limited effect on the number of residents frequenting the courtyard for lunch. One of the reasons may be the urban passive design in terms of the narrow street canyons and the shaded colonnades reducing the MRT. This may also be due to the evaporative coolers maintaining outdoor conditions within acceptable bounds, as shown in Fig. 9. The outdoor evaporative coolers were able to bring the UTCI down from very strong heat stress to between thermal comfort and moderate heat stress range for the summer months and they seemed to be effective in keeping the
ACCEPTED MANUSCRIPT
M AN U
SC
RI PT
residents outside. We would like to point the uncertainty in people’s thermal comfort sensations caused by the limited number of evaporative coolers used during our study, since the people closer to the evaporative coolers would have a higher cooling sensation vis-à-vis the others. This may cause the trend for the regulars in Figure 9(top) to move slightly away from the thermal comfort range. However, one may also question how much choice the Masdar community really has in terms of finding other spaces to have lunch. The nearest space for having lunch outside this campus is about 4 km away. Given the somewhat remote location of the campus, the actual choice is to have a home packed lunch by one self at the desk, go to the nearby staff and student quarters or have a communal lunch with colleagues and friends. This choice between solitude and being somewhat warm may have skewed the results, especially for the higher end of the study period without evaporative coolers towards a more tolerant mindset. Or, being more in line with adaptive comfort theory, residents over time get used to warmer weather and accept if it is for lunch, especially if they afterwards return to an air conditioned building. We feel that the objectives of our study have been reasonably met but it would be good to have further studies carried out in hot climates using our approach in other cultural settings to corroborate our findings.
AC C
EP
TE D
Comparing the results between this study for a hot, arid climate at Masdar and colder climate at Cambridge, it is observed that while people looked forward to cooler weather conditions at Masdar in summer (the period with evaporative cooling), the test subjects in Cambridge did not mind a moderate heat stress in winter. The subjects’ choices during the winter/spring (the period without evaporative cooling) at Masdar and summer/fall at Cambridge peaked around 24 °C in the thermal comfort range along expected lines. Another cultural difference observed was in terms of the length of the lunch breaks being longer for Masdar than Cambridge, although a median time of 20 minutes at Masdar is not at all extravagant. The lessons to be learnt from these observations is that it is more important to pay attention to the design of outdoor public spaces for a cooler UTCI (in the thermal comfort range) in summer in a hotter climate and a hotter UTCI (in thermal comfort and moderate heat stress range) in spring in a colder climate. Passive elements such as shading devices, trees and neighboring buildings should be used as much as possible for energy efficiency before the use of active elements such as evaporative coolers in hotter climates. Evaporative coolers use water for cooling but water is a scarce resource in many arid regions. Evaporative coolers will also not be effective in humid regions. Hence, other active options such as radiant cooling, mist fans should also be explored in terms of efficiency of water use to provide similar thermal comfort. Recently, in popular press, there has been an article about Transsolar to use innovative shading systems and solar powered radiant cooling systems to keep people thermally comfortable for FIFA world cup in 2022 in summer time in Qatar [40]. Both of our study sites have been designed well in terms of the passive elements for their respective climates. The buildings in the campus of MI, which are shading each other due to narrow street canyons play a crucial role in the UTCI values obtained above. The Cambridge study is located in a relatively open area, allowing plenty of sunshine in the months with a cooler UTCI.
ACCEPTED MANUSCRIPT
SC
RI PT
5. Conclusions This manuscript extends the approach proposed in a previous paper by the first and the last authors for the use of Wi-Fi data for presence detection of thousands of individuals in public outdoor spaces for the different cultural and climatic setting of Abu Dhabi. Using MacID data, the urban dwellers were divided into regulars and visitors. It was shown that the former group of occupants in the Masdar Institute campus peaked in the thermal comfort range of UTCI levels for both the study periods with and without the evaporative cooling. These results confirm that UTCI can be a useful tool for the design of well-used outdoor spaces. This also demonstrates the effectiveness of the use of outdoor evaporative cooling to bring the UTCI down from very strong heat stress to between thermal comfort and moderate heat stress range. Large urban datasets such as the ones collected for these studies have the potential to provide the city governments with meaningful insights in the use and design of successful outdoor public spaces. Acknowledgments
M AN U
This work was supported by the Cooperative Agreement between the Masdar Institute of Science and Technology (Masdar Institute), Abu Dhabi, UAE, and the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA – Reference 02/Ml/Ml/CP/11/07633/GEN/G/OO for work under the Second Five Year Agreement. References J. Anderson, K. Ruggeri, K. Steemers, F. Huppert, Lively Social Space, Well-Being Activity, and Urban Design: Findings From a Low-Cost Community-Led Public Space Intervention, Environ. Behav. (2016). doi:10.1177/0013916516659108.
[2]
J. Gehl, Cities for people, doi:10.1017/CBO9781107415324.004.
[3]
W. H. Whyte, The social life of small urban spaces, Conservation Foundation, Washington DC, 1980.
[4]
W. Yang, N.H. Wong, S.K. Jusuf, Thermal comfort in outdoor urban spaces in Singapore, Build. Environ. 59 (2013) 426–435.
[6]
[7]
Press,
Copenhagen,
2010.
EP
Island
AC C
[5]
TE D
[1]
T.P. Lin, Thermal perception, adaptation and attendance in a public square in hot and humid regions, Build. Environ. 44 (2009) 2017–2026.
J. Huang, C. Zhou, Y. Zhuo, L. Xu, Y. Jiang, Outdoor thermal environments and activities in open space: An experiment study in humid subtropical climates, Build. Environ. 103 (2016) 238–249. L. Chen, E. Ng, Outdoor thermal comfort and outdoor activities: A review of research in the past decade, Cities. 29 (2012) 118–125.
ACCEPTED MANUSCRIPT
E. Johansson, S. Thorsson, R. Emmanuel, E. Kruger, Instruments and methods in outdoor thermal comfort studies - The need for standardization, Urban Clim. 10 (2014) 346–366.
[9]
M. Bruse et al., ENVI-met, (2017). http://www.model.envi-met.com/hg2e/doku.php.
[10]
A. Matzarakis, F. Rutz, H. Mayer, Modelling Radiation fluxes in simple and complex environments – Basics of the RayMan model, Int. J. Biometeorol. 54 (2010) 131–139.
[11]
M. Nikolopoulou, N. Baker, K. Steemers, Thermal comfort in outdoor urban spaces:
RI PT
[8]
understanding the human parameter. Solar Energy, 70 (2001) 227-235.
L. Katzschner, Behaviour of people in open spaces in dependence of thermal comfort
SC
[12]
conditions. In Paper presented at the PLEA2006 – The 23rd conference on passive and
M AN U
low energy architecture, Geneva, Switzerland. 2006.
J. Zacharias, T. Stathopoulos, H. Wu, Microclimate and downtown open space activity, Environ. Behav. 33 (2001) 296–315.
[14]
M. Nikolopoulou, S. Lykoudis, Thermal comfort in outdoor urban spaces: Analysis across different European countries., Build. Environ. 41 (2006) 1455–1470.
[15]
T. Xi, Q. Li, A. Mochida, Q. Meng, Study on the outdoor thermal environment and thermal comfort around campus clusters in subtropical urban areas, Build. Environ. 52 (2012) 162–170.
[16]
S. Becker, O. Potchter, Y. Yaakov, Calculated and observed human thermal sensation in an extremely hot and dry climate, Energy Build. 35 (2003) 747–756.
[17]
B. Givoni, M. Noguchi, H. Saaroni, O. Pochter, Y. Yaacov, N. Feller, S. Becker. Outdoor comfort research issues, Energy Build. 35 (2003) 77–86.
[18]
V. Cheng, E. Ng, C. Chan, B. Givoni, Outdoor thermal comfort study in a sub-tropical climate: a longitudinal study based in Hong Kong, Int. J. Biometeorol. 56 (2012) 43–56.
AC C
EP
TE D
[13]
[19] C. F. Reinhart, J. Dhariwal and K. Gero, Biometeorological indices explain outside urban lunchbreak patterns collected through Wi-Fi data, Building and Environment, 126 (2017) 422–430. [20] A. H. A. Mahmoud, Analysis of the microclimatic and human comfort conditions in an urban park in hot and arid regions, Building and Environment 46(2011) 2641-2656.
ACCEPTED MANUSCRIPT
[21] M. W. Yahia and E. Johansson, Evaluating the behaviour of different thermal indices by investigating various outdoor urban environments in the hot dry city of Damascus, Syria, Int J Biometeorol 57(2013) 615–630.
RI PT
[22] J. Spagnolo, R. D. Dear, A field study of thermal comfort in outdoor and semi-outdoor environments in subtropical Sydney Australia, Building and Environment 38 (2003) 721 – 738.
L. Shashua-Bar, D. Pearlmutter and E. Erell, The influence of trees and grass on outdoor thermal comfort in a hot-arid environment, Int. J. Climatol. 31 (2011) 1498–1506. (2011) https://doi.org/10.1002/joc.2177
M AN U
[24]
SC
[23] P. Bröde, D. Fiala, K. Błażejczyk, I. Holmér, B. Jendritzky, G. Kampmann, B. Tinz, G. Havenith, Deriving the operational procedure for the Universal Thermal Climate Index (UTCI), Int. J. Biometeorol. 56 (2012) 481–494.
[25] E. L. Kruger, F. O. Minella, F. Rasia, Impact of urban geometry on outdoor thermal comfort and air quality from field measurements in Curitiba, Brazil, Building and Environment 46(2011) 621-34. [26] E. Johansson, Influence of urban geometry on outdoor thermal comfort in a hot dry climate: A study in Fez, Morocco, Building and Environment 41 (2006) 1326–1338.
TE D
[27] A. R. Dehghani-sanij, M. Soltani, K. Raahemifar, A new design of wind tower for passive ventilation in buildings to reduce energy consumption in windy regions, Renewable and Sustainable Energy Reviews 42 (2015), 182-195.
EP
[28] J. K. Nayak, and J.A. Prajapati, Handbook on Energy Conscious Buildings, IIT Bombay and Solar Energy Centre, Ministry of Non-conventional Energy Sources, Government of India: R & D project no. 3/4(03)/99-SEC, 2006.
AC C
[29] N. K. Bansal, G. Hauser and G. Minke, Passive building design, Elsevier Science, New York, 1994. [30] HVAC Systems and Equipment (SI ed.). Atlanta, GA: American Society of Heating Refrigeration and Air-conditioning Engineers (ASHRAE). 2012. p. 41.1. [31] I. Knez, S. Thorsson, Influences of culture and environmental attitude on thermal, emotional and perceptual evaluations of a public square, Int J Biometeorol. 50 (2006) 258–268. [32] F. Aljawabra and M. Nikolopoulou, Thermal comfort in urban spaces: a cross-cultural study in the hot arid climate, International Journal of Biometeorology 62 (2018) 1901–1909.
ACCEPTED MANUSCRIPT
[33] UTCI Assessment Scale (2017). http://www.utci.org/utci_doku.php. Floor Standing Air Cooler (2018). https://outdoorcooler.ae/portable-coolers/floorstanding-air-cooler/
[35]
S. Jiang, J. Ferreira, M.C. Gonzalez, Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore, IEEE Trans. Big Data. PP (2015). doi:10.1109/TBDATA.2016.2631141.
[36]
A. Sevtsuk, S. Huang, Mapping the MIT campus in real time using WiFi, in: M. Foch (Ed.), Handb. Res. Urban Informatics Pract. Promise Real-Time City, IGI Global, 2009: pp. 325–337. doi:10.4018/978-1-60566-152-0.ch022.
[37]
J. Freudiger, Short: How Talkative is your Mobile Device? An Experimental Study of WiFi Probe Requests., in: WiSec’15 June 22-26 2015, New York City, NY, USA. ACM 9781-4503-3623-9/15/06, 2015.
M AN U
SC
RI PT
[34]
[38] ISO, international standard 7726 (1998) Thermal environments: instruments and methods for measuring physical quantities. International Standard Organization, Geneva.
TE D
[39] Y. Chen, T. Lin, A. Matzarakis, Comparison of mean radiant temperature from field experiment and modelling: a case study in Freiburg, Germany, Theor Appl Climatol 118 (2014): 535–551.
AC C
EP
[40] Transsolar, http://www.carltonservices.co.uk/blog/innovative-air-conditioning-at-the-2022qatar-world-cup/
ACCEPTED MANUSCRIPT
Highlights
-
AC C
EP
TE D
M AN U
SC
-
Extended the novel approach from our previous paper to use Wi-Fi data from smartphones to study the dwelling patterns of 5,000 individuals in outdoor public spaces in Masdar Institute campus, Abu Dhabi Lunchbreak patterns of 1,200 regulars correlated with Universal Thermal Climate Index (UTCI) simulations The outdoor evaporative coolers bring the UTCI down from very strong heat stress to between thermal comfort and moderate heat stress range in the hot and arid climate of Abu Dhabi. UTCI is a reliable performance metric for the design of outdoor spaces in both a hot and a cold climate
RI PT
-