Albedo influence on the microclimate and thermal comfort of courtyards under Mediterranean hot summer climate conditions

Sustainable Cities and Society 81 (2022) 103872 Available online 31 March 2022 2210-6707/© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Albedo influence on the microclimate and thermal comfort of courtyards under Mediterranean hot summer climate conditions Victoria Patricia Lopez-Cabeza , Sebastian Alzate-Gaviria , Eduardo Diz-Mellado , Carlos Rivera-Gomez , Carmen Galan-Marin * Departamento de Construcciones Arquitect´ onicas 1, Instituto Universitario de Arquitectura y Ciencias de la Construcci´ on, Escuela T´ ecnica Superior de Arquitectura, Universidad de Sevilla, Avda. Reina Mercedes, 2, 41012, Seville, Spain ARTICLE INFO Keywords: Courtyard ENVI-met Microclimate simulation Albedo UHI ABSTRACT The Urban Heat Island (UHI) effect represents a threat to the well-being of cities. Cities must adapt to this phenomenon, prioritizing the improvement of outdoor environment quality. Urban materials have a great impact on outdoor environment quality, energy demand, and citizens’ well-being. Based on the literature, it can be stated that changing the albedo of on-site materials (pavements, facades) is a relevant strategy. The environmental impact of reflective materials from buildings to urban microclimate has been widely discussed in the literature. However, few publications assess the role of albedo in inner courtyards. This work uses simulation results to evaluate the impact of different surface albedo on the thermal performance and comfort of a courtyard in Seville. To do so, the simulation tool ENVI-met, one of the most widely used for outdoor spaces, is validated through a comparison with monitoring results. In conclusion, high reflectance compromises user comfort up to 5 ◦C of PET despite the fact that the use of high albedo on surfaces reduces surface temperature up to 25 ◦C in comparison with low albedo as it accumulates less heat by reflecting more solar radiation. Some of the recommendations given include the use of medium albedo (around 0.4) on walls to balance positive and negative effects, and high albedo on the pavements (above 0.7). 1. Introduction By 2030 >80% of the European population will be living in urban areas (IEA, 2008). This development of urban diameter and density in order to accommodate a growing concentration of human activities is accompanied by environmental problems, including the gradual increase in the Urban Heat Island effect (UHI). This microclimatic effect consists of an increase in urban temperatures in relation to surrounding rural and suburban areas (Kamoutsis, Matsoukis & Chronopoulos, 2013; Roth, 2012; Taleb & Abu-Hijleh, 2013). The built-up areas offer a greater surface for the absorption of heat, which radiates slowly during the night. In addition, the presence of tall buildings results in multiple horizontal reflections of incoming radiation, while the reduced sky fraction visible from urban settings also reduces the radiant heat exchange with the sky, causing the energy to remain on the ground (Dirksen, Ronda, Theeuwes & Pagani, 2019). Furthermore, several studies show a direct link between high urban temperatures and the lack of vegetation, highlighting the potential of vegetation to reduce extreme temperatures, particularly during the warm season, due to two effects: evapotranspiration and shading (Matsoukis, Kamoutsis, Bollas & Chronopoulou-Sereli, 2013; QIU et al., 2013). Another factor generating heat input into the urban environment is industrial and domestic activity. In this regard, the impacts of traffic-congested areas and conventional cooling systems in buildings have proven to be remarkable (Shahmohamadi, Che-Ani, Maulud, Tawil & Abdullah, 2011). Some authors describe UHI as a local greenhouse effect that causes a capsule of city gasses to absorb heat from the sun. The gas capsule can only be broken when winds exceed 20 km/h; if there are too many tall buildings on the surface the air is obstructed and the capsule remains in place Ulpiani (2021). Finally, another cause of the UHI effect is the low albedo of urban surfaces (Carpio, Gonz´ alez, Gonz´ alez & Verichev, 2020), which reduces the proportion of solar radiation reflected by a surface. Cities are under threat from the detrimental effects of this UHI phenomenon, which increases cooling energy consumption in buildings, deteriorates air quality, and affects people’s health (Changnon et al., 1996), leading to increased health problems and mortality (Anderson & Bell, 2009; Kalkstein, Greene, Mills & Samenow, 2011, 2013; Vanos, * Corresponding author. E-mail address: [email protected] (C. Galan-Marin). Contents lists available at ScienceDirect Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs https://doi.org/10.1016/j.scs.2022.103872 Received 10 December 2021; Received in revised form 24 February 2022; Accepted 29 March 2022 Sustainable Cities and Society 81 (2022) 103872 2 Kalkstein & Sanford, 2015). Possible symptoms caused by heat stress include swelling, rashes, and heat strokes linked to neurological disorders when body temperature reaches 40.6 ◦C (McGugan, 2001). Recent decades have seen a series of unprecedented extreme summer heat waves in Europe, an increasing trend since the 1970s (Zhang, Sun, Zhu, Zhang & Li, 2020). UHI is therefore a dangerous addition to this gradual urban overheating. Given that climate change will exacerbate the consequences of urban expansion, such as increasing urban temperatures and more frequent and longer heat waves, strategies must be implemented in order to reduce energy consumption, protecting the environment and public health (Carnielo & Zinzi, 2013). This has become a focus of interest in the scientific literature (Rojas-Fern´ andez, Gal´ an-- Marín & Fern´ andez-Nieto, 2017; Santamouris & Yun, 2020). Among the different mitigation strategies to achieve a resilient urban design, those most studied in the scientific literature (Santamouris et al., 2017; M. Taleghani, 2018) are the use of evaporative systems, shading, greenery, earth-to-air exchangers, and reflective technologies. Another strategy for mitigating UHI is the use of courtyards as “Urban Cool Islands” (Leone & Federica Gobattoni, 2020). Inner courtyards are spaces that have been used in vernacular architecture as adapters to climatic conditions, through a proven tempering effect, which improves comfort and helps reduce cooling energy consumption in buildings (L´ opez-Cabeza, Gal´ an-Marín, Rivera-G´ omez & Roa-Fern´ andez, 2018). In hot weather, courtyards play a major role in reducing high temperatures, and in turn improving human comfort in outdoor and indoor environments (Diz-Mellado et al., 2021). The effectiveness of the courtyards depends to a great extent on the geometry, orientation, and thermal characteristics of surfaces (Ghaffarianhoseini, Berardi & Ghaffarianhoseini, 2015; Rivera-G´ omez, Diz-Mellado, Gal´ an-Marín & L´ opez-Cabeza, 2019). According to Muhaisen & Gadi (2006), the solar radiation hitting the surfaces of a courtyard is the most influential factor for its thermodynamic performance and that of the whole building. It therefore becomes important to analyze the ability of surfaces to reflect solar radiation (Diz-Mellado, L´ opez-Cabeza, Rivera-G´ omez, Roa-- Fern´ andez & Gal´ an-Marín, 2020). 1.1. Albedo and urban open spaces The ability of a surface to reflect incident solar radiation, known as albedo, is measured in a range between 0 and 1, representing total absorption and reflectivity, respectively. In the visible spectrum, dark colors are responsible for a low albedo or high absorptivity, while lighter colors present a high albedo, a high reflectivity of solar radiation. However, less than half of the electromagnetic radiation coming from the sun corresponds to the visible spectrum, so some visibly dark surfaces could entail global higher albedo than some visibly light surfaces. The thermal performance of the materials is determined by thermal and optical properties such as the emissivity of long-wave radiation and albedo or reflected short-wave radiation (Doulos, Santamouris & Livada, 2004). Therefore, buildings and surface materials play an important role in the energy balance of the city, as they absorb solar radiation and dissipate the heat through convection and conduction processes to the atmosphere (Santamouris, Synnefa & Karlessi, 2011). Some studies show that the thermal properties of urban surfaces can modify the microclimate around them, increasing air temperature and mean radiant temperature (Gartland, 2008; Voogt & Oke, 2003), and changing thermal and energy performance (Pisello, Cotana, Nicolini & Buratti, 2014). Recently, scientific literature has studied the use of high-albedo materials as a strategy to mitigate the UHI effect. The use of highalbedo materials seems to have direct and indirect benefits at a local scale, reducing surface temperatures and to a reduction in energy consumption and electricity demand for cooling in summer, as well as an increase in thermal comfort (Konopacki & Akbari, 2001). At the urban scale, these materials contribute to reducing air temperature by heat transfer and as an indirect benefit, this reduces the need for cooling systems, increases comfort, and reduces the creation of smog (Rosenfeld, Romm, Akbari & Pomerantz, 1996). A huge proportion of the urban area is covered with materials such as asphalt and concrete which, due to their low albedo (between 0.05 and 0.45), absorb high levels of solar radiation, storing this heat in cities and worsening the UHI effect (Li & Bou-Zeid, 2014). A study in some cities in the United States found that these kinds of surfaces account for 29–45% of surfaces, highlighting their importance in the energy balance of cities. Another study in Rome (Carnielo & Zinzi, 2013) shows how only changing the color of the asphalt, without changing any other property of the material, can reduce both surface and air temperature. Compared to the color black, the greatest differences in temperature were observed with the use of the colors green (7.8 ◦C), blue (7.9 ◦C), gray (10 ◦C), and white (almost 20 ◦C). In an open environment in Athens, 4500 m 2 of conventional pavement were replaced by another with a high reflectance, so that the results obtained for a typical summer day were a reduction of 1.9 ◦C in air temperature and of 12 ◦C in surface temperature (Santamouris et al., 2012). It was concluded that urban spaces covered with materials with high albedo significantly increase the thermal comfort of pedestrians. Nomenclature Abbreviations CFD Computational Fluid Dynamics MRT Mean Radiant Temperature (◦C) PDE Partial Differential Equations PET Psychological Equivalent Temperature (◦C) SVF Sky View Factor UHI Urban Heat Island Parameters TD Thermal Delta (Outdoor Temperature-Courtyard Temperature) (◦C) RMSE Root Mean Square Error (◦C) R 2 Coefficient of Determination MAPE Mean Absolute Percentage Error Simulations W i Wall albedo simulation variation F i Floor albedo simulation variation T1 Test 1 simulation (October 2017) T2 Test 2 simulation (August 2018) PS Previous State simulation BC Best Case simulation Materials M i Wall Material P i Pavement Material Subindexes X LA Material or simulation with low albedo (under 0.3) X MA Material or simulation with medium albedo (0.3–0.7) X HA Material or simulation with high albedo (above 0.7) P S Soil material M W Windows material M GF Green Facade Material V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 3 1.2. Albedo and urban confined/enclosed spaces In confined spaces like courtyards, factors such as geometry and albedo become more relevant when analyzing the UHI effect due to the lesser influence of wind in dissipating thermal stratification. Sky View Factor (SVF), defined as the proportion of visible sky seen from a specific point in the urban space (Al-Sudani, Hussein & Sharples, 2017), is one of the most influential variables in the effectiveness of high reflectance materials in the thermal comfort of pedestrians. The higher the SVF, that is, the lesser the obstacles to see the sky, the more effective the high-albedo materials (Rosso et al., 2018), (Salata, Golasi, Vollaro & Vollaro, 2015). According to this, in urban configurations with a low SVF, such as urban canyons or deep courtyards, the use of high-albedo materials can increase the mean radiant temperature and generate heat stress among users despite reducing air temperature ((Erell, Pearlmutter, Boneh & Kutiel, 2014), (Rosso et al., 2018; M. Taleghani, 2018)). This is due to the multiple reflections of solar radiation which are produced by these materials but cannot go into the atmosphere because of the geometry of the space. In these cases, it is very important to think about the position and orientation of high-albedo surfaces in order to prevent reflection on pedestrians. Erell et al. (2014) analyzed the change in albedo in urban canyons together with their orientation and geometry and concluded that, although high-albedo surfaces can reduce air temperature, they can also cause heat stress among pedestrians, with no improvement in comfort. This study showed that deeper urban canyons had fewer hours of heat stress per day and if surface albedo was increased (both in floors and facades), heat stress also increased. Their results questioned the benefits of high albedo on ground surfaces and facades of urban areas with pedestrians, although they did not rule out their benefits at a higher urban scale. For the specific case of courtyards, studies analyzing the effect of albedo are limited. A study carried out in Portland State University (Taleghani, Sailor, Tenpierik & van den Dobbelsteen, 2014) focused on changing the pavement albedo from 0.37 (black) to 0.91 (white) in an enclosed space similar to a courtyard. This resulted in a reduction of 1.3 ◦C in the air temperature and an increase of 2.9 ◦C in mean radiant temperature. In Kuala Lumpur, Malaysia, an increase from 0.30 to 0.93 in the surface albedo of a courtyard was analyzed, showing an increase of 12 ◦C in the mean radiant temperature at 12.00 in the courtyard, reducing the thermal comfort of users (Ghaffarianhoseini et al., 2015). In Isfahan (Haseh, Khakzand & Ojaghlou, 2018), a study analyzing optimal thermal characteristics for courtyards in a hot arid climate advised the use of low albedo materials in courtyard to avoid extra reflected radiation that could affect user comfort. M. Taleghani (2018) did a similar analysis in a courtyard in Delft University of Technology campus and also stated that increasing the albedo of surfaces in a courtyard led to reduced thermal comfort. A study comparing the effect of different ground variations inside a courtyard and a square in summer conditions in Greece concluded that high albedo reduced surfaces temperatures up to 5 ◦C although the higher mean radiant temperatures increased PET values by 2 ◦C (Chatzidimitriou & Yannas, 2016). 1.3. Research aims and novelty All the studies mentioned above show different interpretations of the benefits of high albedo in outdoor spaces. It seems that it is necessary to find a balance between reducing urban temperatures, energy consumption and thermal comfort. However, as concluded by Yang, Wang & Kaloush (2015), there is a lack of consistent results among these studies on the use of high reflectance surfaces in cities at different scales. Further research is required, particularly to include local climatic conditions that can affect results, improve the accuracy of the models used, and incorporate the influence on thermal comfort and human health. There is an even greater lack of studies examining the courtyard scale, as previous research encountered difficulties in the simulation of courtyard performance with the tools currently available (Lopez-Cabeza et al., 2021). In this sense, the main contributions of this paper are two: - The evaluation of the impact of albedo on the thermal performance and user comfort under hot weather in a courtyard located in the Mediterranean climate, in the south of Spain. - The provision of design recommendations for small-scale courtyards in this specific climate. Although literature analysis shows some experiments on larger courtyards (Mushtaha et al., 2021), smaller courtyards have not been analyzed so thoroughly. In a small courtyard, surface reflections and facade self-shading play an important role and previous literature conclusions cannot be generalized. This study, focusing specifically on domestic-scale courtyards, is particularly relevant considering how common this type of courtyard is in the architecture of some areas of the world (Rojas-Fern´ andez, Gal´ an-Marín, Roa-Fern´ andez & Rivera-G´ omez, 2017). In the specific case of Seville, up to 77% of the plots in the city center have courtyards, most of which are under 35 m 2 . In order to analyze them, a methodology for simulation of these spaces was tested and validated using monitoring data. This paper is structured as follows: In the methodology section, the case study for monitoring and model validation is presented, the Fig. 1. Methodology workflow. V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 4 monitoring campaigns are described, the simulation tool used is analyzed, and a summary of the configuration and data for the simulations is presented. Then, the results and discussion section is divided into five parts: (1) the discussion of the results from the monitoring campaigns. (2) The validation simulations and the software calibration. (3) The analysis of the albedo simulation results. (4) A best-case scenario is designed and presented. (5) Some design recommendations stemming from this study are provided. 2. Methodology In order to achieve the objective of this study, the methodology followed comprises both monitoring and simulation stages. The case study was initially selected as it represents a courtyard with simple geometry and proportions, frequently found in Spanish architecture (Rojas-Fern´ andez et al., 2017). Furthermore, the courtyard is located in a city exposed to intense solar radiation and the highest summer severity categorization in the country, very prone to yearly heat waves. These extreme conditions were chosen in order to test our experiments under climate change events. Two different monitoring campaigns were performed in this courtyard, in two stages of the configuration of the courtyard. The subsequent simulation stage of the methodology also had two parts. The monitored data were used to validate the software in order to then simulate greater albedo modifications in the courtyard. Based on the monitoring and simulation stages, this study recommends the best albedo configuration for this courtyard in terms of thermal performance and user comfort, as well as a set of recommendations for consideration when designing this kind of space. Fig. 1 summarizes the methodology described, while the following sections show the individual stages in further detail. 2.1. Case study The case study selected was the courtyard of an educational building in Seville, in the south of Spain (37◦23′37′′N 5◦57′33′′O, 14 m asl). Up to 77% of the plots of the city center in Seville present a courtyard (Rojas-Fern´ andez et al., 2017) which shows the importance of upgrading this passive strategy for buildings (subsequently minimizing UHIs). Fig. 2 shows a top view of the city and the building location. This area is characterized by hot and dry summers (when temperatures can easily reach 40 ◦C) and mild winters, corresponding to a Csa zone in the Koppen classification (Kottek et al., 2006). The building is a two-story detached construction made up of different rectangular areas organized around corridors and illuminated by courtyards. The case study selected is the only inner courtyard in the building, built in 1976 and refurbished in 2017. The courtyard’s main modifications were the enlargement of windows, the white coating Fig. 2. Top view of Seville (top) and the building context (down). Fig. 3. First-floor plan of the building. The courtyard appears in blue. V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 5 added to the walls, and the floor was covered with white gravel instead of soil with vegetation. A green facade was later installed on the southwest wall. The plan and section of the building are shown in Fig. 3 and Fig. 4. Fig. 5 shows the condition of the courtyard before and after refurbishment. The courtyard measures 5.2 ×6.9 m, with walls at least 5.0 m high. If the Aspect Ratio is defined as the relation between the height of the courtyard and its width, as shown in Eq. (1), this courtyard has an aspect ratio of 1 and 0.7 respectively in each of its sections. AR =Height/Width (1) 2.2. Monitoring The first monitoring campaign was developed in October 2017, after the building refurbishment and immediately before the installation of the green facade. The second, in August 2018, was carried out after the installation of the green facade in the courtyard. Both campaigns were over a week long and the building was unoccupied in order to limit external variables due to users. The data used for the validation of the simulation software were one day chosen for Test 1 (T1) from the first campaign and one day for Test 2 (T2) from the second. The campaigns were developed during warm days of the year, when temperatures close to 38 ◦C were reached. This is common in August (summer) but was an anomaly for this year to reach those temperatures in October (autumn) (Informe mensual climatol´ ogico 2017). TESTO 174H data loggers were used to record the variables of dry bulb temperature and relative humidity inside the courtyard, while wind speed and direction, relative humidity, and dry bulb temperature outdoors were recorded through a weather station (model PCE-FWS 20) on the roof of the building. The weather station was placed at 2 m above the highest roof (see Fig. 7) to ensure that the building heat did not affect it and there is no other building interfering with the wind flow, as it can be seen in Fig. 2. The building is freestanding and is not affected by the shading from surrounding buildings. Furthermore, weather station sensors are protected by non-ventilated multi-plate radiation shields. The temperature recorded by the weather station was compared with meteorological data obtained from the Spanish Agency of Meteorology (AEMET) (Agencia Estatal de Meteorología - AEMET 2018) in order to check the measurements (see Fig. A.3 of Appendix A). Temperatures recorded by the on-site weather station are a few degrees higher than the meteorological data. This is consistent with the UHI effect reported previously in the city (Romero Rodríguez, S´ anchez Ramos, S´ anchez de la Flor & ´ Alvarez Domínguez, 2020), given that the AEMET station is in a rural area near the city. The sensors inside the courtyard were located 1 and 3 m above the ground in order to record the temperature at user height. These sensors, which were placed on the southeast facade to avoid direct solar radiation, were also protected with a reflective shield with openings to allow natural ventilation and avoid overheating due to solar radiation. The sensors were hanging from the top of the wall through a rope, and the distance from the wall was around 10 cm. In the second campaign, in August 2018, a black globe thermometer was also installed in the center of the courtyard at 1 m height to measure Mean Radiant Temperature to be used in the validation of the simulations. Table 1 shows the characteristics of the instruments used and Fig. 6 shows images of the instrument installed. Fig. 4. Section of the courtyard. Fig. 5. Images of the courtyard at different stages. a) Original courtyard, Previous State (PS). b) Refurbished courtyard (T1). c) Courtyard after green facade installation (T2). Table 1 Technical data of the measurement instruments. Instrument Variable Accuracy Resolution TESTO 174H Dry bulb Temp. ±0.5 ◦C 0.1 ◦C RH ±0.1% 2% PCE-FWS 20 Dry bulb Temp. ±1 ◦C 0.1 ◦C RH ±5% 1% Wind ±1 m/s – QUESTemp◦34 Globe Temperature ±0.5 ◦C 0.1 ◦C V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 6 2.3. Simulation ENVI-met v4.4.5, one of the most widely used Computational Fluid Dynamics (CFD) software for urban microclimate simulations on a global scale, has been validated in many countries (Tsoka, Tsikaloudaki & Theodosiou, 2018). In addition to considering variables such as solar radiation, wind, soil and vegetation, it uses the BioMet tool to calculate comfort indexes and allows the definition of materials by thermal characteristic, such as albedo and emissivity, which makes it appropriate for the analysis of the impact of albedo on the thermodynamic performance of the courtyard and on users’ comfort in this study. ENVI-met uses the Finite Difference numerical method to solve the multitude of partial differential equations (PDE) and other aspects in the model. Its accuracy has been tested in terms of the thermal performance of the outdoor environment, particularly on a large scale. However, some researchers have stated discrepancies between monitored and simulated data in small scale courtyards (L´ opez-Cabeza et al., 2018) where the software underestimates the thermal tempering effect of the microclimate generated withing the courtyard. In terms of MRT, some researchers have found that the software overestimates it in shaded locations and complex geometry (such as courtyards) under heat conditions (Crank et al., 2020; G´ al & K´ antor, 2020). For those reasons, the software needs to be validated and monitoring results for air temperature and MRT are contrasted with simulated data previously to perform the analysis. The simulation process had two stages. In the first, a given day was selected from each monitoring campaign for the comparison of monitoring and simulation data in different scenarios in order to validate and calibrate the software. Once it was validated, it was used to analyze different albedo configurations to establish the optimal design for the courtyard. 2.3.1. Software validation Two days were selected from the monitoring campaigns to validate the software through the comparison of simulated and monitored data. In order to test the software with different albedo on the walls, one of the days selected was prior to the installation of the green facade (October 3, 2017) and the other after the installation of the green facade (August 14, 2018). In both cases, the maximum outdoor temperature reached similar values, around 38 ◦C. The software was calibrated using monitoring data. The recorded wind speed and direction, dry bulb temperature, and humidity were used as boundary conditions for the simulation (hourly data are shown in Appendix B), and the courtyard temperature and mean radiant temperature outputs were compared to the monitored data to validate the software. Two different test configurations were performed: one before the green facade was installed (T1) and the other after installation (T2). ENVI-met uses the Arakawa C-grid numerical discretization scheme (Arakawa & Lamb, 1977). This method limits the model geometry to straight structures and an orthogonal grid. The model had a resolution of 1 m for the x and y axes, with 55 and 61 cells respectively. In the z axes, to avoid any boundary phenomenon caused by a proximity to the upper limit of the model, a telescoping factor of 12% was selected after 10 m height, and 20 cells in total. Model geometry was checked using software recommendations. The dimensionless wall distance (y+) value was 467, which lied between 30 <y+<500 corresponding to the log-low layer where the turbulent effect dominates. This value was calculated using the following Eq. (2): y+= y ρ  τ ω ρ √ μ (2) where y is the distance (normal) of the center point of the groundadjacent cell to the ground (m), τω is the shear stress at the surface (kg m -1 s -2 ), ρ is the air density (kg m-3) and μ is the dynamic viscosity of air (m 2 s -1 ) (Forouzandeh, 2018; Salata, Golasi, de Lieto, Vollaro & de Lieto Vollaro, 2016). Fig. 6. Images of the measuring instrument installed in the building. a) Testo 174H, b) PCE-FWS 20, c) QUESTempº34 Fig. 7. Location of temperature sensors at heights of 1 and 3 m and the weather station on the roof. V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 7 The geometry of the simulation model is summarized in Table 2 and displayed in Fig. 8. The main input variables are shown in Table 3 together with other simulation configurations. More information about the hourly data input can be found in the Appendix. This information about the weather conditions was obtained through monitoring campaigns. The data about specific humidity at 2500 m was calculated using monitored data at ground level using the procedure described by Forouzandeh Forouzandeh (2018). The soil temperature at different levels was obtained from the geological profile for Mediterranean climate used by Salata et al. (2016). Soil relative humidity levels were kept as default. The lateral boundary conditions for temperature, humidity and turbulence in the model are forced. ENVI-met allows dynamic surface temperature and wall temperature calculation for each facade and roof element. The coating materials used were from the ENVI-met library, modifying albedo and reflectivity values according to the literature and documentation from the building project. The green facade was modeled as only vegetation added to an existing wall. The characteristics of the materials used in the walls are described in Table 4 and the materials for the pavement are described in Table 5. The 2-equation Turbulence Kinetic Energy (TKE) model (or K-epsilon model) is used to predict the turbulence in the air. All the simulations began three hours before the day under analysis in order to discard the first hours which, according to the literature, could potentially provide inaccurate results (Forouzandeh, 2018; Salata et al., 2016). The time step for the simulations is 1 s. Before selecting the previously described geometry model for simulation, different models had been simulated changing grid size and area of context to ensure a balance between accuracy and simulation time, following the recommendations of previous research into simulation of courtyards with ENVI-met (L´ opez-Cabeza et al., 2018). Two models were defined to perform the sensitivity analysis: One representing a larger area around the building (252,000 cells of 1 m resolution), and another with a finer grid size of 0.5 m resolution (resulting in 487,500 cells). Data from these models were compared with results from the model finally selected to ensure that the results did not vary significantly, and the model could be validated. The average percentage error was calculated for the hourly results, providing a 0.91% average percentage error between the larger model and the selected model, and a 0.92% between the finer grid size model and the selected model. The simulation time, on the other hand, doubled (from 5 h for the final model to more than 10 for the other). Based on this, the final model (smaller context and 1 m grid resolution) was confirmed as the best Table 2 Simulation model geometry properties. Number of grid cells 55, 61, 20 Size of the cells (m) (x,y,z) 1 ×1 ×1 Telescoping factor 12%. Start at 10 m height. Model dimensions (m) 55 ×61 ×30 Number of cells 61,000 Nesting grids 4 Model rotation out of north grid 45 Fig. 8. Image of the simulation model. Table 3 Main input variables for ENVI-met validation. T1 T2 Start Simulation Day (DD.MM.YYYY) 02.10.2017 13.08.2018 Start Simulation Hour (hh:mm) 21:00 Total Simulation Time (hours) 28 Wind Speed at 10 m (m/s) 0.96 0.79 Wind direction 90◦(E) 225◦(SW) Roughness Length (m) 0.01 Air temperature (min - max) (◦C) 22.0 – 37.0 20.7 – 38.4 Relative Humidity at 2 m (min-max) (%) 29.0–60.0 19.0 – 89.0 Specific Humidity at 2500 m (g/kg) 8.5 9.5 Initial Soil Temperature/Relative Humidity Upper layer: 19.85 ◦C/50% Middle layer: 15.85 ◦C/60% Deep layer: 11.85 ◦C/60% Table 4 Material properties for the coating materials of the walls. Coating Material Albedo Emissivity Transmittance Absorption Specific Heat (J/kgk) Thermal Conductivity (W/mK) Density (kg/m 3 ) M MA Beige brick 0.40 (Santamouris, 2001) 0.90 (Yaghoobian & Kleissl, 2012) 0.00 0.5 850 0.6 1500 M HA White mortar 0.70 (Taha, Sailor & Akbari, 1992) 0.91 (Taha et al., 1992) 0.00 0.3 850 0.6 1500 M LA Dark mortar 0.10 (Oke, 1992) 0.93 (Oke, 1992) 0.00 0.9 850 0.6 1500 M W Windows 0.05 0.90 0.90 0.05 750 1.0 2500 M GF Green facade 0.30 (Djedjig, Bozonnet & Belarbi, 2016) 0.95 (Djedjig et al., 2016) 0.30 – – – – Table 5 Material properties for the coating materials of the pavements. Coating Material Albedo Emissivity Thermal Conductivity(W/mK) Volumetric Heat Capacity (J/m 3 K) P MA White gravel 0.38 (Santamouris et al., 2011) 0.93 (Santamouris et al., 2011) 4.61 2.34 ×10 6 P HA White concrete 0.85 (Taha et al., 1992) 0.96 (Taha et al., 1992) 1.63 2.08 ×10 6 P LA Dark concrete 0.03 (Taha et al., 1992) 0.87 (Taha et al., 1992) 1.63 2.08 ×10 6 P S Soil 0.10 (Taha et al., 1992) 0.80 (Taha et al., 1992) – 1.40 ×10 6 V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 8 option to find a balance between accuracy and time optimization, given that error between them were acceptable. 2.3.2. Albedo simulations After validation and calibration, the software was used to simulate different albedo configurations in the courtyard. These simulations shared the same input variables and are described in Table 6. Hourly data can be found in Appendix B. The hottest day recorded in Seville in summer 2020 was chosen to analyze albedo in extreme heat wave conditions, increasingly common every year. The combination of materials used in each simulation is shown in Table 7. There are five groups of simulations. First, the previous state of the courtyard was simulated (PS), to be taken as reference in the results, and all the other simulations are compared with this one. Subsequently, the two simulations for validation were included. The albedo changes were then classified into two groups: one keeping the existing materials for the walls and changing the albedo of the floors (F) and another keeping the existing floor and changing the albedo of the walls (W). One representative material for each surface was selected for each albedo range analyzed: low albedo (under 0.3: M LA , M W , P LA , P S ), high albedo (above 0.7: M HA , P HA ), and medium-range albedo (0.3–0.7: M GF , M MA , P MA ). The final simulation (BC) represents a design combination to achieve the best results. Fig. 9 displays the model of the courtyard and the material used for each surface in the different simulations. Note that W simulations have no windows. All the walls were treated as a single opaque material with the selected albedo. The parameters simulated were air temperature, floor surface temperature (as monitored in the campaigns), mean radiant temperature (MRT) and Physiological Equivalent Temperature (PET) as a parameter to measure user comfort. PET was used since it is considered an accepted index for analyzing comfort in outdoor environments (Matzarakis, Mayer & Iziomon, 1999). This temperature represents the air temperature required in an outdoor environment to reproduce a standard indoor environment for a standard person (Crawford, Stephan, Walls, Parker & Walliss, 2015). This index accounts for the effects of long and shortwave radiation on the human energy balance. Some studies conclude that mean radiant temperature is the main variable affecting human comfort (Herrmann & Matzarakis, 2010; H¨ oppe, 1999; Lindberg, Holmer & Thorsson, 2008). ENVI-met calculates the Mean Radiant Temperature for a cylindrical-shaped body using the following equation (Huttner, 2012): Tmrt =(1 σ (Qlw,in +ak ε ∗(Qsw−diff,in +Qsw−dir,in)))0.25 (3) where σ is the Stefan-Boltzman constant, ak is the absorption coefficient of the human body (0.7), and ε is the emission coefficient of the human body (0.97). Qlw,in is the incoming longwave radiation, assumed to be half from the sky and half from the ground surface. Qsw−diff,in is the diffuse incoming shortwave radiation and Qsw−dir,in the direct shortwave radiation. 2.3.3. Solar radiation analysis Solar radiation analysis was performed for the courtyard on the simulated day (July 20) in order to understand the evolution of shading inside the courtyard. Fig. 10 shows the configuration of shading inside the courtyard at different hours. As can be observed, the sun hits all the Table 6 Input variables for the albedo simulations. Start Simulation Day (DD.MM.YYYY) 19.07.2020 Start Simulation Hour (hh:mm) 21:00 Total Simulation Time (hours) 28 Wind Speed at 10 m (m/s) 1.6 Wind direction 45◦(NW) Roughness Length (m) 0.01 Air temperature (min-max) (◦C) 23.6 – 44.4 Relative Humidity at 2 m (min-max) (%) 44.4 – 74.8 Specific Humidity at 2500 m (g/kg) 7 Initial Soil Temperature/Relative Humidity Upper layer: 19.85 ◦C/50% Middle layer: 15.85 ◦C/60% Deep layer: 11.85 ◦C/60% Table 7 Modeling scenarios and materials used in each case. Scenario Description Walls Materials Pavement Materials PS Previous state M MA +M HA + M W P S T1 Validation 1 M HA +M W P MA T2 Validation 2 M HA +M GF + M W P MA F HA High albedo floor M HA +M GF + M W P HA F LA Low albedo floor M HA +M GF + M W P LA F MA Medium albedo floor M HA +M GF + M W P MA W HA High albedo walls M HA P MA W LA Low albedo walls M LA P MA W MA Medium albedo walls M MA P MA BC Medium albedo walls +High albedo pavement M MA +M GF + M W P HA Fig. 9. Simulated scenarios of the courtyard. The colors represent the distribution of materials of the surfaces. V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 9 different surfaces inside the courtyard during the day so that albedo has a role to play on all the surfaces of this courtyard at this time of year, given its low Aspect Ratio. 3. Results and discussion 3.1. Monitoring results The monitoring campaigns were developed during October 2017 and August 2018. Monitoring results of air temperature and wind speed are shown for one week of each campaign in Appendix A, Fig. A.1 and Fig. A.2. As Fig. 10 shows, one representative day was chosen from each campaign in order to validate the simulations: October 3, 2017, and August 14, 2018. These days were chosen because they share similar outdoor temperatures and represent a typical summer day in Seville (de Meteorología-AEMET, 2021). On the first day, the courtyard was monitored before the installation of the green facade, while on the second the green facade had already been installed. As Fig. 11 shows, both scenarios had similar outdoor temperatures. In the scenario without the green facade (left) the maximum temperature inside the courtyard was 33.3 ◦C when 37 ◦C outdoors, while after the installation of the green facade (right), the temperature inside the courtyard stood at 33.8 ◦C while the outdoor temperature was 38.4 ◦C. Thermal delta (TD) is defined as the difference between the outdoor and courtyard monitored temperatures, as shown in Eq.4: TD =Outdoor temperature – Courtyard temperature (4) The TD is represented in Fig. 11 with the bars at every hour of the day. In scenario A it reached 5.5 ◦C, while in scenario B it reached 6.1 ◦C. This shows a better tempering effect when the green facade is in place. One possible reason for this difference is the effect of cooling of the green facade, which was already installed on the second day shown. However, many other factors are different between the two simulations (meteorological conditions, solar insolation, and probably ground moisture and thermal conditions) making it difficult to state what is the cause of the difference in the thermal delta observed. In any case, both scenarios demonstrated the tempering effect of the courtyard. However, there was not a significant stratification effect inside this courtyard, as sensors at different heights showed very similar temperatures. This could be because of the geometry of the courtyard, as it is not very deep. Two aspects relating to these results deserve further analysis. The first of these is the shift between the peak temperature outside and inside the courtyard. While the maximum temperature inside the courtyard appears when the sun is at its zenith, outdoors, the maximum temperature is reached a few hours later. (Note that the time in the graphs is local time, GMT+2). Inside the courtyard, the temperature peaks when the highest solar radiation is hitting the walls and floor. After this point, the courtyard geometry results in a decrease in sun radiation, and it starts cooling. Outdoors, the heat continues to accumulate after the zenith time, so that in the exterior the peak temperature is reached later. The second aspect is the relative increase in outdoor temperature observed at night in October. This causes the courtyard temperature, which is usually higher than nighttime outdoor temperatures (because of the heat absorbed by the walls), to fall below the outdoor temperature. The results for October show that in this month in the year in question the temperatures were abnormally warm (Informe mensual climatol´ ogico 2017). This anomaly in outdoor temperature was a combination of high temperature and low wind speed, as can be seen in Fig. A.1. Despite the differences in outdoor temperatures, the courtyard displays similar performance during the nights of both study days. Fig. 10. Solar analysis at different hours on July 20 in the case study courtyard. Top view. Fig. 11. Outdoor and courtyard monitored air temperature. Left: October 3, 2017 (without green facade). Right: August 14, 2018. (With green facade). V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 16 Seville, via the internationalization grant within the VIPPI-US. The authors also want to thank AEMET (State Meteorological Agency. Spanish Government) for the data supplied. Appendix A. Monitored Data See Fig. A.1, A.2, A.3 Fig. A.1. Monitoring data of Air Temperature and Wind Speed in courtyards and outside, October 2–9, 2017. Fig. A.2. Monitoring data of Air Temperature and Wind Speed in courtyards and outside, August 14–21, 2018. Fig. A.3. Comparison between AEMET data and Outdoor Station monitored temperature data. V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 17 Appendix B. Tables of monitored data See Table B.1 Appendix C: Equations for error calculation The following notation is used for the definition of the error measures: Pi: is the forecast from the simulation method, Pr: is the forecast from the least-squares regression Oi: is the actual value at the monitoring, O: is the mean of the monitoring values, n: is the number of series being summarized. - Coefficient of determination: R2=∑n i=1(Oi−Oi)(Pi−Pi) [∑n i=1(Oi−Oi)2∑n i=1(Pi−Pi)2]1/2 - Root Mean Square Error: RMSE =[∑n i=1(Pi−Oi)2 n]1/2 - Mean Absolute Percentage Error MAPE =1 n∑ n i=1 Oi−Pi Oi x100 Table B.1 Outdoor hourly air temperature used as input for the ENVI-met simulations. October 3, 2017 (T1) August 14, 2018 (T2) July 20, 2020 (Rest) Hour Outdoor T. (◦C) Outdoor T. (◦C) Outdoor T. (◦C) 1:00 24.7 23.9 29.9 2:00 24.1 23.4 28.0 3:00 23.8 23.0 26.1 4:00 24.4 22.3 25.4 5:00 24.0 21.7 24.8 6:00 24.1 21.3 25.2 7:00 23.6 20.9 24.1 8:00 22.3 20.6 23.6 9:00 23.2 21.0 26.0 10:00 25.3 21.8 30.4 11:00 28.2 24.1 32.3 12:00 30.4 27.3 34.2 13:00 32.3 31.4 37.6 14:00 33.1 33.8 40.1 15:00 34.4 35.5 41.3 16:00 36.0 37.2 42.8 17:00 36.1 37.8 43.8 18:00 36.5 37.5 44.2 19:00 37.0 37.2 44.4 20:00 33.6 35.1 39.2 21:00 31.6 32.4 37.4 22:00 28.0 29.1 35.5 23:00 26.3 25.7 34.0 00:00 26.2 23.7 33.2 V.P. Lopez-Cabeza et al. Sustainable Cities and Society 81 (2022) 103872 18 References Agencia Estatal de Meteorología - AEMET. Gobierno de Espa˜ na, (n.d.). http://www.aeme t.es/es/portada (accessed April 17, (2018)). Al-Sudani, A., Hussein, H., & Sharples, S. (2017). Sky view factor calculation A computational-geometrical approach. Space Syntax and Ontologies-Volume 2-ECAADe 35, 2, 673–682. http://papers.cumincad.org/data/works/att/ecaade2017_240.pdf. Anderson, B. G., & Bell, M. L. (2009). 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