Test box experiment and simulations of a green-roof: Thermal and energy performance of a residential building standard for Mexico

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TEST BOX EXPERIMENT AND SIMULATIONS OF A GREEN-ROOF: THERMAL AND ENERGY PERFORMANCE OF A RESIDENTIAL BUILDING STANDARD FOR MEXICO ´ ´ ´ , I. Hernandez-P ´ ´ A. Avila-Hern andez , E. Sima´ , J. Xaman erez , ´ ´ E. Tellez-Vel azquez , M.A. Chagolla-Aranda PII: DOI: Reference:

S0378-7788(19)32574-5 https://doi.org/10.1016/j.enbuild.2019.109709 ENB 109709

To appear in:

Energy & Buildings

Received date: Revised date: Accepted date:

21 August 2019 27 November 2019 17 December 2019

´ ´ ´ , I. Hernandez-P ´ ´ Please cite this article as: A. Avila-Hern andez , E. Sima´ , J. Xaman erez , ´ ´ E. Tellez-Vel azquez , M.A. Chagolla-Aranda , TEST BOX EXPERIMENT AND SIMULATIONS OF A GREEN-ROOF: THERMAL AND ENERGY PERFORMANCE OF A RESIDENTIAL BUILDING STANDARD FOR MEXICO, Energy & Buildings (2019), doi: https://doi.org/10.1016/j.enbuild.2019.109709

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HIGHLIGHTS 

Thermal, environmental, and economic behavior study was performed for the social interest house in Mexico.

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Comparison of traditional and green roofs under the weather conditions of eight cities in Mexico was analyzed.

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Thermal simulations were validated with experimental data from two test cells (typical roof and green roof).

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Result for the green roof shown that the indoor temperature of the house is reduced (4.7°C) in locations with warm weather.

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It was found that CO2 emissions can be reduced by 45.7%.

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The green roof had a payback period of 8.8 years for the warm weather.

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TEST BOX EXPERIMENT AND SIMULATIONS OF A GREEN-ROOF: THERMAL AND ENERGY PERFORMANCE OF A RESIDENTIAL BUILDING STANDARD FOR MEXICO

A. Ávila-Hernández 1, E. Simá 1, J. Xamán 1, I. Hernández-Pérez 2, E. Téllez-Velázquez1, M.A. Chagolla-Aranda1 1

Tecnológico Nacional de México / CENIDET

Prol. Av. Palmira S/N. Col. Palmira. Cuernavaca, Morelos CP 62490, México. Tel.: +52 (777) 3-62-77-70, E-mail: [email protected], [email protected], [email protected], [email protected], [email protected]

2

Universidad Juárez Autónoma de Tabasco. División Académica de Ingeniería y Arquitectura. Carretera Cunduacán-Jalpa de Méndez km. 1, Cunduacán, Tabasco, CP 86690, México. Tel.: +52 (993) 358 1500, E-mail: [email protected]

ABSTRACT A thermal, environmental, and economic behavior study was performed for social housing in Mexico. This research focuses on the comparison between a traditional roof (TR) and a green roof (GR) under the weather conditions of eight cities in Mexico. The study was carried out by means of thermal simulations using the EnergyPlus software, which were validated with experimental data from two test boxes (TR and GR). For the validation of the simulations, the interior surface temperature and the heat flow of the roof of the test boxes were measured, and a maximum error of 3.55 and 2.17% was found for the interior surface temperature for the traditional and green roof, respectively. The results of the simulation showed that in locations with warm weather the GR reduced the indoor temperature of the 2

house up to 4.7°C. In locations with temperate weather, the GR reduced the cooling energy demand by up to 99%, and at the same time increased the heating energy demand by 25%. From the environmental study, it was found that the GR also reduced the CO2 by 45.7%. In the economic part, the GR had a payback period of 8.8 years, which makes the implementation of the green roof viable.

KEYWORDS: Thermal performance, energy consumption, green roofs, residential building.

NOMENCLATURE

Cp

Specific heat ( Jkg 1 K 1 )

G

Solar radiation ( Wm 2 )

HR

Relative humidity (%)

T

Temperature (ºC )

U

Thermal transmittance (Wm 2 K 1 )

Wind

Wind velocity (ms 1 )

xi

Numerical values

yi

Experimental values

Greek symbols



Density (kgm3 )



Thermal conductivity (Wm 1 K 1 )

Subscript amb

Ambient

c

Comfort

o

Average monthly outdoor ambient

Acronyms BES

Building Energy Simulation 3

GR

Green Roof

LAI

Leaf Area Index

LowF

Low Floor

MBE

Mean Bias Error

R1

Bedroom 1

R2

Bedroom 2

R3

Bedroom 3

RMSE

Root Mean Square Error

SR

Stomatal Resistance

TopF

Top Floor

TR

Traditional Roof

UHI

Urban Heat Island

1. INTRODUCTION The world's energy consumption until now has been mainly from fossil resources, according to the International Energy Agency (IEA), in 2015 oil and coal accounted for 60% of the worldwide energy sources, which were essentially used for generation of electricity (IEA, 2016). Global emissions of carbon dioxide related to energy increased by 1.4% in 2017, after three years without changes, reaching a historical maximum of 32.5 gigatonnes (IEA, 2018). Three major contributors of CO2 related to the use of fossil fuels are the US, China, and India, which accounted for 70% of total emissions and in 2018 these countries experienced a decrease in their emissions mainly due to the use of renewable energy (IEA, 2019). However, medium and small economies such as Turkey, Mexico, and Cyprus also contribute to emissions (Duan & Jiang, 2018). One of the most important sectors that contribute to emissions is the residential and commercial building sector; this sector worldwide consumes 21.9% of total energy, and in Mexico the building sector is responsible of the 17.2% the total energy (SENER, 2017). Currently, worldwide the residential and commercial building sector consumes approximately 4

60% of electricity, and this value in Latin America is 42% (Michels et al. 2018). In Mexico, residential electricity consumption is mainly used by air conditioning systems, representing 21.2% of the national total (CEPAL, 2018). For this reason, in order to mitigate polluting emissions and reduce electricity consumption in buildings, researchers around the world are analyzing different building technologies. Among these technologies, passive building techniques, which do not require electricity for their operation, are becoming increasingly attractive because they can be installed or implemented into the building envelope. Thermal insulation, smart glazing, wind tower, solar chimney, ventilated roofs, and green roofs are some examples of passive technologies. Another alternative to mitigate pollutants is through the use of renewable energy, which has expanded almost all over the world at an accelerated pace, it is expected that the world's electricity supply by wind generation in 2020 would be from 8 to 12% of the total supply (Cabeza et al. 2018; Talat & Reynolds, 2019). The energy used to heat and illuminate a building can be saved by using one or several passive techniques, in particular, roofs occupy around 20-25% of urban surfaces, which represent an attractive part of improving the building envelope, as their performance is affected by solar absorption. On the roofs impact up to 1000 W/m², of which 20-95% of energy is absorbed by them (Qin et al. 2017). Some materials that form roof layers can help capture CO2 from the atmosphere and control environmental pollution. Therefore, several studies available in the literature are focused on the improvement of the thermal behavior of building roofs. For instance, the work presented by Guzmán et al. (2018) compares four types of roofs (self-protected, gravel finish, floating floor, and green) according to their thermal behaviour and contribution to sustainability. Gagliano et al. (2015) analyzed the thermal behavior of standard, cool, and green roofs. Another example of passive alternative is a ventilated roof, several studies that have analyzed the thermal behavior of this technology such as Dimoudi et al. (2006). According to the literature, green roofs installed in residential and non-residential buildings, in addition 5

to the benefits they provide at building scale such as improvement of thermal comfort conditions and reduction of energy consumption, they also bring benefits at urban scale as the improvement of air quality by producing a microclimate in the urban environment (Zhang en al. 2017), retain rainwater and mitigate runoff, thus helping water flow more slowly through the streets and minimize the risk of flooding (Besir & Cuce, 2018). They increase the aesthetic and economic value of the building, green roofs work as acoustic insulation and therefore present a sustainable solution that improves the conditions of the urban environment (Djedjig et al. 2012; Gagliano et al. 2016ª; Alexandri & Jones, 2007). The thermal effects of the green roofs are noticed especially, on the upper floors when they are implemented in multi-level buildings (Musy et al. 2017). A series of layers form green roofs, and their designs vary widely (Scharf & Zluwa, 2017). For its proper functioning they should have a protective layer or anti-root (to keep the building dry, free of moisture and destructive action that can cause the roots of plants), a drainage layer, a substrate and vegetation (Francis & Jensen, 2017), which retains most of the incident radiation in the roof and reduces the local temperature due to photosynthesis, respiration and evaporation (Hodo et al. 2012); therefore, its it is important that green roof can cover the highest percentage of land and thus contribute to the reduction of building thermal loads (He et al. 2017). The vegetation used in green roofs is varied, ranging from grasses, sedum, gramineae, succulents, and shrubs, and it is applicable to extensive roofs (6-15 cm of substrate), intensive (20-70 cm of substrate) and semi-intensive that they have intermediate characteristics between extensive and intensive roofs (Ascione et al. 2013). Several studies related to the thermal-energetic behavior of green roofs are available in the literature. For instance, Gagliano et al. (2016b) analyzed the thermal behavior of an extensive green roof installed in building in Italy. They used both experimental measurements and building simulations to perform the study The study was carried out for a one-level house and the thermal comfort model was taken from the 6

ISO standard EN 15251 where it is mentioned that there are three comfort intervals: high, medium and acceptable expectation. The results obtained show that an isolated roof has a more significant reduction in temperature and energy consumption compared to a traditional roof, the maximum temperature of the green roof was 36 ºC and for the traditional roof of 54.5 ºC analyzed a four-story building and a basement with and without a green roof. The study considered lighting, cooling, heating, occupation, and natural and mechanical ventilation at a specific schedule. The authors found that the green roof had most stable temperature throughout the day and the whole year compared to the traditional roof; they also observed that the building with the green roof had greater energy savings in winter than in summer because of the use of air conditioning in the warm season. Sailor (2008) analyzed the behavior of a green roof in two buildings located in two cities with different weather. The authors carried out simulations with EnergyPlus software considering the parameters established in the ASHRAE 90.1-2004 standard for buildings. The results showed that the green roof with high foliage density reduced the electricity consumption and increased gas consumption in winter. Silva et al. (2016) following the model of Sailor (2008), compared the energy performance of three types of green roofs (intensive, extensive, and semi-intensive) and two types of traditional roof (black and white). The authors concluded that the roof with green intensive roof with 8 cm of insulation provided the best results by consuming 5.23 kWh/m2/year while the worst results were presented by the extensive green roof without insulation by consuming 23.55 kWh/m2/year. From the traditional roofs, the white roof was the one who consumed less energy in summer but in winter the black roof was more efficient. Parameters of green roofs are critical for their thermal behavior, this is reflected in the study developed by Zeng et al. (2017), who conducted an experimental, theoretical investigation to determine the optimal properties of a green roof (LAI = leaf area index, vegetation height, and substrate thickness) to achieve energy savings and thermal comfort. They concluded that to increase the electricity-saving in cities of 7

China, the optimum soil thickness and the LAI should be equal to 0.3 and 5, respectively, the latter being the parameter with the most significant influence on energy consumption of buildings when using a green roof. The authors also concluded that in areas dominated by cooling energy demand (hot climates), green and cool roofs work similarly. Ferrante et al. (2016) determined the LAI, the cover index and the temperature of the foliage of six plant species to select the optimal vegetation for green roofs. The results obtained, four months after the different species were planted, they were used to model an office of 23.7 m² with a green roof and a traditional roof. It was concluded that a green roof saves 23% of annual energy compared to a traditional roof and that a higher LAI decreases cooling, but it slightly increased heating energy consumption. Regarding the experimental studies, Foustalieraki et al. (2017). the one carried out by Foustalieraki et al. (2017) that validated simulation data of a green roof installed in 2009 to estimate the contribution vegetation provides in the energy consumption of a building, as well as the behavior of different vegetation species. The results showed that vegetation with dark colors reached higher temperatures than light-colored vegetation. From the seven types of vegetation analyzed under the sun, Lanatana Camara vegetation had the highest surface temperature, while Origanum Heraclioticum vegetation had the lowest surface temperature. The authors found that the green roof decreased the cooling energy demand by 18.7% and increased the heating energy demand by 11.4%. Although studies in other countries have shown that green roofs can provide an attractive alternative for energy reduction, in Mexico there are few studies on the evaluation of green roofs (Castañeda & Vecchia, 2007; Quezada et al. 2014; Ordóñez & Pérez, 2015; Ovando et al. 2016; Quezada et al. 2017). Among these studies Chagolla-Aranda et al. (2017) performed an experimental study of the thermal behavior of a green roof in a semi-warm weather in Mexico. The authors conducted the study in two stages. First, they studied five types of plants to select the appropriate one for this weather. They used as 8

a selection parameter the resistance to the drought that the species presented, being the Aeonium subplanum the optimal one. In the second stage, they built two test boxes, one with a green roof and the other with a concrete roof. The results showed that a green roof consumes 10.3% less electricity for air conditioning compared to a concrete roof. The maximum temperature for the green roof was reduced by 6.4 ºC in the vegetation, 4.8 ºC in the substrate, and 1.3 ºC in the slab. Further, several studies of green roofs applied to a whole residential complex are available. For instance, Cerón-Palma et al. (2013) proposed sustainable strategies for energy saving and reduction of greenhouse gases through the change of more efficient refrigerators, the replacement of incandescent bulbs by fluorescent lamps, and the application of gardens and green roofs. The vegetation could be grass or the cultivation of tomatoes; Tomato growing is the best option, the study was conducted for a housing complex of 1903 homes. Other study developed by Tovar Jiménez et al. (2014) has selected the suitable vegetation for green roofs in Mexico City and the metropolitan area. They provided a guide to select the appropriate vegetation for a green roof. Among the parameters of selection is that maintenance is zero, which support high radiation and long periods of drought, durable vegetation and rapid growth, among others. Because of the scarcity of this type of studies in Mexico and the importance of roofs in buildings, this article proposes the annual thermal and energy evaluation of a green roof incorporated into a residential building used in the creation of the NOM-020-ENER-2011 standard. The simulations were performed using the EnergyPlus software, the results were divided into three categories: thermal, economic and environmental analysis. For this purpose, two types of roofs were compared, a traditional roof (TR) and a green roof (GR) under three predominant weather of Mexico: Arid, warm, and temperate. The simulations were validated with experimental data from two roof prototypes (traditional and green), and the vegetation used was Aeonium subplanum.

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2. METHODOLOGY First a short description on the software used for the simulations (EnergyPlus) is presented. Then, the experiments developed in the test boxes for the TR and the GR are described, from which the temperatures in different locations of test boxes are obtained. The corresponding validations of TR and GR are then made. Finally, the case study is established in detail, and the results of the parametric study of the different cities of Mexico are presented.

2.1 Description of the simulation software For the simulation of the case studies, specialized software was used in the design and thermal analysis of buildings. These programs are known as BES (Building Energy Simulation), which provide users with indicators of building performance, such as energy consumption and demand, temperature, humidity, and costs. Some of the software are BLAST, BSim, ECOTECT, Ener-Win, eQUEST, ICE IDA, PowerDomus, ESP-r, DOE-2, SUNREL, Solene, TRNSYS, and EnergyPlus. One of the advantages of using this type of simulation software is that one can obtain results in a considerably short time compared to experimental studies or studies that use computational fluid dynamics. Another advantages are that the thermophysical properties are included in the program and that changes in the building geometry that can be easily made (Crawley et al., 2008). One of the most used BES is the EnergyPlus software due to the mentioned characteristics and multiple benefits to evaluate a building. EnergyPlus is free, open-source program of one-dimensional energy balances for time-dependent heat and mass transfer simulations. This feature allow one to consider the capacity of the envelope to isolate and store the energy inside, relating thermal conductivity, density and the specific heat of the material; taken into account the constructive characteristics of the enclosure, the 10

electrical equipment, HVAC systems, luminaires, radiative and convective effects, the occupation by people and the operating hours of each equipment. The simulation includes user-defined solution time steps, weather data input files, simultaneous solution of internal and external heat balance, thermal comfort models, etc. For all the above mentioned reasons, EnergyPlus was chosen for the modeling of this work in addition to integrating a green roof model that includes physical characteristics of vegetation such as height, minimum stomatal resistance and LAI, thermal properties such as reflectivity and the emissivity of the leaves and soil moisture conditions (including irrigation and precipitation). The vegetation that incorporates the green roof model are grasses, shrubs, tundra, and desert vegetation (Frankenstein and Koenig 2004).

2.2 Description of experiment Two test boxes were constructed to obtain experimental results for a green roof and a traditional concrete roof. Experimental data is used to validate the results of EnergyPlus simulations. The test cells were mounted on the roof of the National Center for Research and Technological Development, CENIDET-TecNM-SEP (18°52'47.0"N 99°13'17.6"W) located in Cuernavaca, Morelos (Mexico). Figure 1a shows the test boxes with the air conditioning connected, which has a capacity of 1470 W to maintain the constant temperature inside the test boxes at 25 ºC, controlled by an electric thermostat (error ± 1 ºC). The air conditioning systems have two ducts each which are thermally insulated to avoid heat gains and to transport the air form the air conditioner to the test boxes. The walls of test boxes are made of OSB wood (Oriented Strand Board) of 2.5 cm thick, inside the walls thermal insulation (extruded polystyrene of 2.5 cm thick) was installed. The walls are reinforced in the corners with a metal

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structure so that it can support the weight of the slab of concrete which has a thickness of 10 cm and 120 cm per side. The exterior of the walls was painted white to reflect the solar radiation, and an air conditioning system was installed on both test boxes. One test box remained with the conventional slab, and in the other, a green roof was added; which is based on the concrete slab, then a layer of waterproofing that integrates an anti-root, then 6 cm of the substrate and finally the vegetation of the species Aeonium subplanum, commonly known as succulent, who had a height of 8 cm (Figure 1a). These test boxes were described in detail in a previous work of the authors, Chagolla et al. (2017). The substrate used in the GR is a mixture of pumice stone, expanded shale, organic matter and black soil (Sailor et al. 2008). The optical and thermophysical properties of the substrate were considered for a dry substrate, the remaining properties of the GR and concrete slab are shown in Table 1. The weather conditions were measured using the Vaisala Maws100 meteorological station that measures solar radiation (W/m2) with a Vaisala QMS101 pyranometer that has an error of ± 1%, the ambient temperature (ºC), relative humidity (%), and wind speed (m/s) are obtained with a Vaisala WXT536 multisensor. It has an error for each variable of ± 0.3 ° C, ± 3% and ± 3%, respectively. The station is located 20 m from the test boxes, where neighboring buildings or trees do not shade them. Type "T" thermocouples (error ± 0.5 ºC) to measure the temperature were placed inside the slab, substrate, vegetation, and inside the test boxes. Omega HFS-4 thin-film (± 5% error) sensors were placed on the interior surface of the roofs and walls to determine the heat flow through them. Two Decagon Devices EC-5 sensors were installed in the GR (error ± 3 %) to measure the volumetric water content of the soil. Sensor information was monitored in a Keysight 34972A data acquisition (6½ digit resolution) taking measurements every five minutes during the period of April 13 to 17, 2017. Figure 1b shows the placement and position of the sensors in the two test boxes.

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2.3 Validation results For validation, the simulation results were compared with the experimental data obtained from the test boxes. The experimental data selected for the validation was the temperature of the interior and exterior surface of the slab, the vegetation temperature and that of the indoor air of the test box. The average environment temperature during this experimental period was 24.3 °C with a maximum of 30.5 °C and a minimum of 18.4 °C. The maximum solar radiation was 945.7 W/m². The average relative humidity was 35% with a maximum 63%, and the average wind speed is lower than 2 m/s throughout the period. Sailor (2008), Silva et al. (2016) and Zeng et al. (2017) have used statistical parameters to measure the amount of error that exists between experimental and numerical data. For our validation, these errors are obtained with the root mean square error (RMSE) and the mean bias error (MBE): ( xi  yi ) 2 RMSE   n i 1 n

n

MSE   i 1

( xi  yi ) n

(1)

(2)

where xi represents the numerical values, yi represents the experimental values, and n is the data number.

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2.3.1 Validation for the traditional roof Figure 2 shows the comparative results for the case of the TR (Figure 2a, 2b and 2c). Figure 2a shows the qualitative comparison between simulation results and experimental results for the temperature of the outer surface of the TR. In general, the behavior is similar between both temperature values, obtaining an experimental maximum temperature value of 49.0°C and the corresponding numerical value of 49.3°C, therefore a 0.3°C deviation is obtained. Figure 2b shows the comparative results for the temperature of the interior surface of the roof. Figure indicates that there is a maximum difference of 2.7 ºC was found with respect to the experimental data, April 15th had the highest temperature, and April 17th had the lowest temperature. Another parameter compared in the validation was the air temperature inside the cavity. Figure 2c shows the behavior of the air temperature for five days. The average difference between the two types of data is 0.1ºC and a maximum of 0.9ºC; because April 15th is the day that presents higher temperatures on the surface of the slab, consequently in the interior air of the test box, this occurs approximately at 16:00 h. The highest temperatures are between 3:00 pm, and 4:00 pm and the lowest temperatures are at 7:00 am.

2.3.2 Validation for the green roof A green roof was installed on one of the test boxes, which is composed of four layers, the concrete slab is covered with an anti-root waterproofing, the substrate, and finally the vegetation, all this is contained with a sheet parapet of 15 cm in height (Figure 1). The energy balance of a GR requires the variables that involve the characteristics and properties of the vegetation and substrate. For vegetation, these variables are height, LAI, stomatal resistance (SR), reflectivity and emissivity of the leaves; for the soil are the depth, conductivity, density, specific heat, and maximum and minimum volumetric moisture content. The values of some of these variables is available in the literature [Sailor (2008); Gagliano et al. 14

(2015); Gagliano et al. (2016a); Gagliano et al. (2016b); Chagolla et al. (2017); He et al. (2017)] and others are unknown. In order to know this unknown values, several simulations were carried out and their guess values were varied in a determined interval established by the literature, so that the results were adjusted with the experimental data and thus having the parameters of a green roof suitable for simulation. From unknown variables that were varied (LAI, stomatal resistance, reflectivity, emissivity), the one with the greater impact in a green roof is the leaf area index. LAI range is between 0.001 and 5 within the structure of EnergyPlus. The simulations started with a value of 0.5, this value represents a scarce and not very dense vegetation, the results of the simulation showed that the exterior surface of the slab and the indoor air temperatures are very similar to the text box with a concrete roof, this despite that it had a green cover, but as the vegetation was very scarce, the highest percentage of radiation reached the substrate and by conduction it passed to the slab and into the test box, therefore this value was discarded. More simulations were performed by varying the LAI until the optimum value of 5 was found; this represents dense vegetation that covers 100% of the roof area. The second characteristic that was varied is the stomatal resistance (SR), which represents the resistance to the transfer of steam by stomata, which are small openings in the surface of the leaves that allow them to exchange water and gases with the environment. The SR depends on factors like the light intensity, the soil moisture content, and the vapor pressure difference between the inside of the leaf and the ambient air. The range of SR varies between 50 and 300 s/m, and the value that best fitted the experimental data was 180 s/m, this agrees with the values reported by Sailor 2008 and Gagliano et al. 2016a for the vegetation of Aeonium subplanum. The third free property is the reflectivity of the leaves; the range of variation available in the software is between 0.1 and 0.4. The values of leaves reflectivity reported by Chagolla et al. (2017) and Gagliano in 2015 and 2016b are in the order of 0.2 to 0.3 for the vegetation of the succulents according to the authors, so greater emphasis was placed on those values. The value of 0.22 for the reflectivity was

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the one that best adjusted to the measured values. Finally, the emissivity of the leaves was varied, the variation interval is between 0.8 and 1, the results of the simulation show that this characteristic does not present significant changes and according to the studies made by Gagliano in 2015 and 2016b was selected the value of 0.95 for the emissivity of the leaves of the succulents. For validation of the GR results, Figure 2 shows the comparative results for this case (Figure 2d, 2e, 2f and 2g). Figure 2d shows the qualitative comparison between the simulation results and the experimental results for the temperature of the outer surface of the GR. When comparing the results of Figure 2a versus Figure 2d, it can be seen that the temperature values obtained for the outer surface of the GR are lower than the corresponding values of the TR. The GR had an experimental maximum value of 41.0ºC and the TR had a maximum experimental value of 49.0ºC, therefore a temperature reduction on the outer surface of up to 8.0ºC is achieved by the use of GR. Comparison of simulation and experimental results of the GR show an RMSE value of 2.2. Figure 2e shows the behavior of the temperature on the interior surface of the slab is shown as a function of time, it is observed that the temperature does not it exceeds 25.5 ºC in none of the five days, this due to the layer of vegetation that functions as a solar barrier and prevents the radiation from reaching the slab. The maximum temperature difference between the experimental and simulated data was 0.9 ºC, and the minimum was 0.09 ºC. Figure 2f shows the indoor air temperature which indicates that the maximum temperature difference between the experimental and simulation data is 1 °C and on average 0.1 °C. Throughout the validation period, the temperature is lower than 26 ºC, the lowest being on April 17th at 7:00 h. Figure 2g shows the comparison of the temperature of the vegetation. The figure demonstrates that during the daytime hours the highest temperature values are reached, obtaining experimental values up

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to 43.3 ºC. From the comparison of the numerical results it is obtained a maximum difference of 6.8ºC with respect to the experimental results. Additionally, Figure 3 shows the volumetric water content (VWC) of the substrate during the period of the experiment. The figure shows that at the beginning of the measurements (April 13, 2017), the VWC was 0.41 m³/m³ and it was decreasing as the days went by because the substrate dries out and loses moisture. Moreover, it is observed that during the days of the experimental tests there was no irrigation and precipitation event. The decrease of the VWC had an almost linear behavior until the end of the experiment with a VWC of 0.12 m³/m³. Table 2 shows the minimum and maximum values of the temperature of the interior surface of the roof, the indoor air temperature, and the comparison errors of the simulated and experimental data for the TR and GR. For the TR, the maximum temperature difference between the simulated and experimental data is 2.7 °C, which leads to an RMSE error of 3.55 for the temperature of the interior surface. For the GR, the compared data were very close to each other, with an average maximum difference of 0.2 °C, which is reflected in the RMSE error of 2.17 and MBE of -0.20.

2.4 Case study In this section, the studied building is described, called here as the reference building, which was used for the modeling of a GR under different weather conditions in Mexico. The distribution of the thermal zones and their dimensions is presented. The weather data corresponding to the year 2017 and the modeling was done for the same period. The simulation parameters and the considerations that were taken are also mentioned.

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2.4.1 Reference building NOM-020-ENER-2011 standard is mandatory in Mexico and seeks to reduce heat gains in residential buildings through the building envelope. It provides a methodology to calculate the heat gains of the envelope and thus be able to compare the thermal loads of the proposed building against a reference one; the standard provides the characteristics (i.e., density and thermal conductivity) of the construction materials, in this way the user has the information easily accessible to perform the calculation. For the calculation of the heat gains through the envelope of the reference building, the standard NOM-020ENER-2011 does not take into account the heat gain through the floor, because it is supposed to be on the ground. However, if the building has one or more parking floors above the ground, the heat gain across the floor must be added. The heat gain through the building envelope is the sum of the heat gain by conduction through the opaque surfaces, plus the corresponding heat gains by solar radiation through the semi-transparent surfaces. The heat gain by conduction through each of the components, according to their orientation, roof, and type of surface, is obtained from the basic conduction equation, as shown below:

 pc   K j Aij (Tei  T ) n

(3)

j 1

where is  pc the heat gain by conduction through the component with orientation i; j are the different portions that form the part of the envelope; K j is the overall heat transfer coefficient of each portion;

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Aij is the area of the portion j with orientation i ; Tei is the value of the average equivalent temperature for orientation i; and T is the value of the interior temperature of the residential building. The solar radiation gain through each of the semi-transparent parts such as windows, doors and skylight

( ps ) ; ( Aij ) is obtained from a relationship that involves the area of the semi-transparent part; ( FGi ) is a glass shading coefficient of each semi-transparent portion, according to the manufacturer's specification (CS j ) ; the solar heat gain by orientation; ( SEij ) is the correction factor by outer shading for each semi-transparent portion, determined according to the element used. The model for the calculation is shown in equation (4). These values are provided in tables by standard NOM-020-ENER2011 for each city in Mexico.

 ps   Aij CS j FGi SEij  m

(4)

j 1

The reference building is a construction with physical characteristics and construction materials of a typical building in Mexico. For this reason, the construction of the standard can be taken to represent social housing. The reference building is a “virtual” construction that was used for the creation of the NOM-020-ENER-2011 standard and the calculation methodology for thermal gains.

The envelope of the reference building from the NOM-020-ENER-2011 standard is formed with exterior walls, roofs, windows, and doors. By improving the thermal characteristics of these components, the amount of heat entering the building can be reduced. This is achieved with the application of shading in 19

the windows, optical properties of window glasses, low emissivity materials and application of thermal insulation in the envelope. When implementing the strategies that reduce thermal loads, the evaluated building complies with the NOM-020-ENER-2011 standard, obtaining as a benefit the energy saving and reduction of expenses in cooling/heating of the building maintaining a comfort temperature. To establish the thermal properties of the materials of the building envelope, in the norm NOM-020-ENER2011 a reference building with constructive characteristics found in Mexico was used.

This building has two levels (lower level and upper level) with a total area of the envelope including doors and windows is 176.5 m²; the roof of 54.3 m²; the floors (including the upper and lower floors) with a total area of 86.8 m²; the glazed surface has an area of 12.8 m², the opaque surface of 160 m² and the area of the two exterior doors is 3.6 m². The height between the floors is of 2.5 m; the total volume for heating or cooling of 220 m³. The main facade is oriented to the North with the main access towards the West. In the East and West, there are other houses with the same characteristics that are taken into account for the modeling, and the façade oriented to the South is not in contact with another house because there is a service patio. Figure 4a shows the dimensions of the building. The walls of the building are made of brick covered with a mortar layer in the outside and plaster layer inside; the floors are made of a concrete slab covered with plaster and tile. The roof is a concrete slab covered with a layer of plaster in the inside face, but without protection or coating in the outside face. Table 3 shows the construction materials ordered from the interior to the exterior, the thicknesses and thermophysical properties of each of the elements of the enclosure. The building has windows of different sizes in most of its walls (Figure 4b), these have an aluminum frame and clear glass 4 mm thick single pane. Some walls have more than one window; the

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main façade has four, the rear façade has three, the right and left façades have one and the doors are made of wood. The glazing area represents 7.2% of the total of the walls, Table 4 presents the characteristics of the windows. (Heard, 1993). For the analysis, the residential building was divided into several thermal zones; in the first floor, the building has the kitchen (9.5 m²), dining room (15 m²), living room (9.5 m²), and additionally it has a small bathroom and the stairs to the second floor. The first floor in its entirety is considered a thermal zone because the spaces are open, there are no doors that divide them, and there are no internal thermal gains, so it can be considered that the temperature is uniform in all the first floor. The second floor has three spaces of interest, the main bedroom (12 m²) and two junior bedrooms (9.5 m²), the bathroom and hallway are very small to be considered in the analysis. The spaces of interest for our study are the three bedrooms: bedroom 1 (R1), bedroom 2 (R2) and bedroom 3 (R3). Then, the building is divided into thermal zones to identify the thermal behavior in each of the spaces of interest, with the TR and with the GR, these areas can be seen in Figure 4c (Heard, 1993).

2.4.2 Cities for simulation Taking into account that Mexico has several types of weather predominating warm, arid, and temperate climates, the reference building was modeled for eight cities that include climatic conditions throughout the country (INEGI, 2018). At the North of the country is located the city of Monterrey, Nuevo León (25°40'20.8"N 100°18'34.1"W) with a warm semi-arid weather with an average annual temperature of 22.7 °C and little rain per year. La Paz, Baja California Sur (24°09'06.9"N 110°18'37.6"W) and Hermosillo, Sonora (29°04'29.4"N 110°57'30.3"W); both cities with arid warm weather, the first located on the coast and the second at 110 km away from it. In the center of the country is located Mexico City 21

(CDMX) (19°25'57.1"N 99°08'00.1"W) and Tlaxcala, Tlaxcala (19.317628, -98.238112). Both have temperate weather with an average annual temperature of 17.2 and 16.1 °C. Pachuca, Hidalgo (20.122505, -98.737053) lies in the temperate semi-arid weather and Colima, Colima (19°14'35.3"N 103°43'39.3"W) with a sub-humid warm weather that is influenced in large part by its mountainous relief. At the South of the country is located the coastal city of Chetumal, Quinta Roo (18°29'37.7"N 88°17'52.4"W) with sub-humid warm weather with summer rains and an average annual temperature of 26.6 °C. Figure 5 shows the location of the cities considered. In this work the reference building was simulated in the prevailing weathers of Mexico to know the behavior of the two types of roofs. Table 5 presents the main climatic variables used in the modeling of EnergyPlus: outdoor ambient temperature (T), relative humidity (RH), solar radiation (G) and wind speed (Wind), these are monthly averages for each city. The meteorological data were obtained for each hour corresponding to the year 2017 representative of each geographical area.

2.4.3 Building model for simulation Additionally, to the consideration of the reference building for different climates of Mexico, it is evaluated with two types of roofs (TR and GR) in free-floating conditions and with a set-point according to the weather of each city. In the simulations, internal loads from lighting, people, electrical equipment, or any other source of energy are not considered because our goal is to evaluate the roof. The windows have standard clear glass, they remain closed all the time and there are no shading systems in them. The infiltration rate is established as 0.7 air changes per hour. The neighboring buildings to the east and west are considered with the same thermal conditions as the reference building. Therefore, the walls of the simulated building that are next to the other houses are not exposed to the outdoor environment, thus are

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considered adiabatic. The tolerance of convergence of temperature and thermal loads is specified in 0.0004 °C and 0.0004 W, respectively. The time step of the simulation was 10 minutes, a weekly watering is established at 7:00 h with 1 m3/h of water, this based on the experimental information obtained from the irrigation of the GR of the test cell, the porosity of the substrate, and the moisture retention capacity of the green roof (Chagolla et al. 2017). Table 6 shows the components and properties of the GR for modeling; the green shaded values represent the parameters that were found in section 2.3.2. On the other hand, the human being performs a variety of activities throughout the day and to carry them out in a better way. Inhabitants look for a comfortable temperature in the workspaces, thus they usually use air conditioning systems to maintain comfort conditions in the indoor environment. The optimal temperature or comfort for the interior of buildings is obtained through a linear regression analysis that correlates the exterior temperature of the site and the answers given by the individuals when conducting surveys. The model to assess thermal comfort used in this work is the adaptive comfort model, proposed by Nicol (2004), which allows calculating the comfort temperature set point taking into account the local weather of each city, and was calculated as follows:

TC  0.534 TO  12.9

(5)

Where Tc is the comfort temperature and To is the average monthly outdoor ambient temperature. The air conditioning systems are modeled considering that they have an unlimited capacity, in such a way that they can satisfy any heating and cooling demand by always maintaining the established temperature.

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3. RESULTS For the different climates analyzed, the results are divided into three sections. The first section shows the thermal behavior of the building with the two types of roofs, the roof surface temperature, the indoor air temperature of each thermal zone is analyzed, and the energy demand. In the second section, an analysis is made for the environmental benefits that green roofs provide. Further, the third section presents the cost-benefit analysis that green roofs provide.

3.1. Thermal evaluation Figure 6a shows the monthly thermal behavior of the reference building in free-floating conditions for the city of Hermosillo, Sonora. The figure indicates that June is the month with the highest ambient temperature and this is reflected in the indoor air temperature of the building, especially in the upper level; in June there is a higher temperature difference between the levels, with 36.8 and 40.3 °C for the lower and upper levels, respectively. On an annual average, the air in the upper level of the building is 2.8 °C higher than the air in the lower level. The blue stripe represents the comfort temperature interval for each of the months; the same figure shows that the building indoor air temperature is above this temperature during the warm months of the year, while in winter, the building is within the comfort temperature. Figure 6b shows how the indoor air temperature of the building is reduced with the GR. The most significant temperature reduction of the indoor air occurs in May with 4.2 °C in the upper level, while in the lower level, the maximum reduction is 0.9 °C. These values indicate that the green roof affects the upper level of the building, and a more significant effect is observed in the warm season. On an annual average, the GR reduced 2.2 °C the indoor air temperature in the upper level. The building was divided into thermal zones for a better analysis of each of the spaces where inhabitants spend more time doing their activities. Thus, the house was divided into four main areas: the lower level (LowF), the 24

room 1 with the largest area (R1), a second room facing South (R2), and a third room facing North (R3). The lower level is an open space and has a lower temperature than the upper level due to the permanent shading it receives from the upper floor, reaching a maximum temperature of 36.8ºC. The zones R1 and R2 have a window that covers 28.5 and 26%, respectively of the walls. These two zones behave similarly throughout the year, with an average annual difference of 0.2 ºC. The zone R3 is oriented to the north, and it has a window in the front facade that covers 28.5% of the wall. This space has a lower temperature throughout the year compared to the other spaces in the upper level due to its location. This room reaches a maximum and minimum temperature of 40.2 and 19.9 ºC, respectively. The building behaves in the range of 19.2 and 40.5 ºC, as shown in Figure 6c. One of the benefits of green roofs is the reduction of the temperature due to the shading effect that it produces, since the vegetation absorbs most of the radiation, preventing it from reaching the roof of the building (Figure 6d). The zone LowF did not present significant changes with the GR, the maximum reduction occurred in May with 0.9 ºC, and on annual average it was reduced 0.5 ºC. This result agrees with the assumption that the green roof does not affect the lower levels. Of the thermal zones of the upper level, R2 is the zone with the most significant reduction of temperature; this reduction occurred in May with 4.7 °C. In the same month, the GR reduced 3.9 and 4.1 °C the air temperature in the zones R1 and R3, respectively. The air in the building with GR behaves between the interval of 19.1 and 36.8 °C. Table 7 shows the average monthly temperatures for each thermal zone and the temperature reduction that occurred with because of the GR. Figure 7a shows the comparison of temperature for the outer surface of the TR and GR for Hermosillo, Sonora. For this city, it can be seen that in general throughout the year, the temperature for the TR is higher than the corresponding GR, obtaining a maximum difference of 14.5 ºC for the month of June.

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Figure 7b shows the temperature of the inner surface of the two types of roofs. The TR reaches 44 °C in June, which is the warmest month, on average the TR reaches 34 °C. On the other hand, the green roof reaches a maximum temperature of 35.5 °C and an annual average of 28.2 °C. The GR reduces the temperature of the interior surface throughout the year, especially in the warm months, presenting an annual average reduction of 5.6 °C. The maximum reduction occurred in May with 9.3 °C; this because of the shading and evapotranspiration of the vegetation, thus the heat fluxes are reduced and also the internal temperature. Figure 7c shows the energetic behavior of the roofs. The heat gains in the TR have a behavior similar to that of radiation; this shows that in a traditional roof, the heat gains are more significant than the heat losses. In the case of the GR, throughout the whole year the temperature on the inner surface is greater than the corresponding value on the outer surface, and therefore heat fluxes are negative, indicating that the heat flux is from inside the building to the outside environment (there are greater losses of energy than gains). Additionally, Figure 7d shows the behavior of the annual temperature for the configuration with GR in two positions, one of the positions corresponds to the temperature on the outer surface of the GR (outer roof) and the other position corresponds to the temperature obtained for vegetation. The figure shows that throughout the year the temperature of the vegetation is higher, obtaining a maximum difference of 13.7ºC with respect to the value of the temperature of the exterior surface. Additionally, Figure 8 shows the volumetric water content (VWC) for April for the city of Hermosillo; the irrigation of the GR was carried out once a week throughout the simulated year. This month was considered as an example to represent the behavior of moisture in the substrate during the year. The figure shows that during the days of irrigation, the VWC reaches 0.40 m³/m³, and as the days go by, the humidity decreases until it reaches 0.11 m³/m³ and stays that way until the irrigation day comes and the VWC increases again. Similarly, this behavior occurs with other months of the year. 26

Table 8 summarizes the normalized energy demand respect to the net floor area of building needs for cooling, heating, and the total annual energy required to condition the thermal zones. The annual energy use was obtained from the temperature set-point and the monthly temperatures of the Hermosillo, Sonora climate. This city has a warm arid weather that requires cooling in the warm season and heating in the cold season. A traditional roof requires more energy to condition the spaces, predominantly energy for cooling. The vegetation of the GR cools the zones and stabilizes the temperature because of both, its shading effect and the evapotranspiration of the vegetation. For this weather, the GR reduces the interior temperature throughout the year, thus more heating is required for the building with GR than the building with TR. Therefore, a GR in this city reduces cooling by 45% and increases heating by 25%. Although heating has increased the total annual consumption is reduced with the GR, so if savings in energy demand are generated, the negative values in Table 8 indicate that energy demand increased. From the eight considered cities, the city of Hermosillo, Sonora was previously presented in detail. The remaining cities are described along with their thermal and energetic behavior. The weather was classified into three main groups: warm, semi-arid, and temperate. Figure 9a shows the behavior of the average monthly indoor air temperature for the upper level during one year of the cities of La Paz, Chetumal, and Colima that are classified with the very warm and sub-humid warm climate; As the green roof affects only the upper level of the building, the results are presented only for this level. The highest temperature occurs for the building located in La Paz with a TR reaches a maximum air temperature of 36.2 °C in June and July, and with the green roof, the temperature is reduced to 33 °C for the same months, which represents a temperature reduction of 3.2 °C. The indoor temperature in this city behaves during the year between 24 and 36.2 °C, thus showing a difference of 12.2 °C between winter and summer. In Colima, the building had an average annual indoor temperature of 30.6 °C, which was reduced to 29.4 °C with the green roof. The greatest reduction in temperature occurred in April with 2.3 27

°C. Of the three cities in this category, the building located Colima is the one that exhibits a more stable indoor temperature behavior throughout the year, it only varies 4.4 °C. The behavior of the indoor air of the building located Chetumal presents a maximum average temperature of 33.7 °C in July and August with a TR. The building with a GR had an indoor air temperature 2.5 °C lower than the corresponding to the TR in both months; the lowest reduction occurred in the cold season. For the temperate climates weather data from the CDMX and Tlaxcala were used, which have average annual ambient temperatures of 17.2 and 16.1 °C, respectively. Figure 9b shows that the highest indoor temperature occurs in the CDMX with TR for April, reaching 24.6 °C. The two cities show similar behavior, because the green roof reduced the average annual indoor air temperature 1.7 and 1.5 °C for the CDMX and Tlaxcala, respectively. For the semi-arid warm and semi-arid temperate climates, the cities of Monterrey and Pachuca were considered (Figure 9c). The highest indoor air temperatures occurred in the building with traditional roof; in Monterrey the building had the highest air temperature of up to 34.2 °C in August. The maximum reduction of temperature provided by the green roof was 2.2 °C for the same month. In the months of beginning and end of year there is a smaller reduction to 1 °C. On the other hand, in Pachuca the green roof provided a maximum reduction of 2.7 °C in April, the indoor air temperature varied in the range of 15 to 23 °C. The building located in the different cities have benefits since in all the temperature was reduced with the GR, it can also be seen that the vegetation performance in the roofs varies in each climatic zone. Finally from the annual simulation results, the cities with the more significant reductions for the average indoor air temperature were Hermosillo with 4.7 °C and La Paz with 3.2 °C; On the other hand, Monterrey was the city with the smallest decrease, with 2.2 °C The thermal loads or energy consumption to condition the zones are function of the internal temperature, and this depends on the environmental conditions. Figure 10a shows that these cities are dominated by 28

cooling, and it is reduced with the application of the GR. The GR reduced 45, 31, and 42% the cooling energy demand for Chetumal, La Paz, and Colima, respectively, with La Paz being the city with the highest demand for cooling energy. Figure 10b presents the energy demand for the cities with temperate weather (CDMX and Tlaxcala). The buildings in these two cities show the same behavior; the GR reduced cooling by more than 90%. On the contrary, the heating demand is reduced by 11% for the CDMX, and it increased by 23% for Tlaxcala. These increments occurred because both cities have low average annual temperature, and with the application of the green roof, the temperature drops even more, requiring more energy to heat the thermal zones. Figure 10c shows the energy demand for the building located in semi-arid weather, it is observed that the green roofs in these two cities decreased the cooling energy demand. The green roof in Pachuca decreased a greater percentage with a 99% of the cooling energy demand. The GR reduced energy demand for heating in Pachuca by 10%, the opposite effect occurred in Monterrey, which increased heating by 25%; in this last city the climate is extreme, it presents important changes of temperature between day and night, this contributed to that the heating increased because the GR reduced the interior temperature. The greatest reduction in energy demand occurs in very arid and warm sub-humid warm climates. Therefore, green roofs reduce the energy needs compared to a traditional roofs, and its use is preferable in cities that are governed by cooling energy demand.

3.2. Environmental evaluation Green roofs present environmental benefits such as the reduction of the urban heat island (UHI) and air pollutants. The European Union (EU) has proposed that green roofs can contribute to decarbonization. The EU adopted an environmental plan that establishes a 20% reduction in polluting emissions, a 20%

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reduction in energy consumption, and the use of at least 20% of renewable energies in 2020 (Ferrante et al., 2016). Other countries within its official regulations stipulate that public buildings have green roofs, such as Shanghai, which ruled that as of 2015, an area higher than 30% of the available roof of public buildings must be covered with a green roof (He et al. 2017). Other important cities such as Basel (Switzerland), Toronto (Canada), Stuttgart (Germany), Portland (United States), Tokyo (Japan) and Singapore have demanded the use of green roofs for every building (Francis and Jensen, 2017; Zeng et al. 2017). In Mexico, there is still no law that stipulates the use of green roofs or information on its use and care. The indirect emissions of CO2 of the building located in the eight different cities were determined. The reduction of the CO2e emissions due to electricity consumption was calculated using the National Electric System Emission Factor of the year 2017 (1 MWh = 0.582 tons of CO2) (Comisión Reguladora de Energía 2019). The city that presented the highest reduction in emissions was Chetumal with 45.7%; this percentage represents 2.5 tonCO2/year avoided. The building located in Chetumal, which is a city with sub-humid warm weather, is dominated by cooling, and with the green roof, the demand for energy and CO2 was reduced by almost half. The building located in Colima considered with a similar weather reduces emissions by 42%; both cities have similar energy consumption. The building that consumes more energy is Hermosillo, and at the same time, it is the building that emits more CO2 into the atmosphere, 9.7 tonCO2/year when it has a TR, and 6.2 tonCO2/year when it has a GR, which represents a reduction of emissions of 36.4%. Another building with a good performance is the one located in La Paz, the green roof reduced the CO2 emissions by 33%, and in the worst scenario is Monterrey, this is a very warm semi-arid weather, and the use of a green roof increased the heating but, even so, the total energy consumption and the emissions were reduced by 7.3%. In general, all the cities presented environmental benefits due to the reduction in energy that was obtained by installing a GR.

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The most significant decreases in pollutants occurred in very arid sub-humid and warm locations. Table 9 shows the energy demand and CO2 emissions for each city with the two types of roofs.

3.3. Economic evaluation An important part to consider when installing a green roof is the economic cost involved, as well as its payback period. In this section, the cost of a green roof for Mexico is presented. The materials were selected in online stores, in this way it is ensured that the price is the same for the whole country and at the same time, the materials can be obtained in all the considered cities. The price of electricity was taken from the Federal Electricity Commission and corresponds to domestic users, rates vary depending on the geographical area, the time of year, and the season of the year (CFE, 2019). The price of water also varies depending on the area; the fees were obtained from the Mexican Water Information System (SITAP, 2019). With all these values, it was calculated a cost per m² of green roof of $1,416.00 MXN, and the payback period was obtained with this amount and the annual energy savings energy. Table 10 shows the fees for water and electricity, the annual savings produced by the use of the green roof, and the payback period. The same table indicates that Hermosillo had the highest annual savings ($7,884.7), so it takes 8.8 years to recover the investment. The cities of Chetumal, La Paz, Colima, CDMX and Pachuca have smaller payback periods than 20 years; this indicates that the project is profitable because the approximate useful life of a green roof is 20 years (Bianchini & Hewage, 2012). Thus, in these cities, the investment is fully recovered, and the green roof would continue working and generating profits for more years. The unfavorable cases were Tlaxcala and Monterrey because they had a payback period greater than 20 years. This means that economically, a green roof is not recommended in these locations because of the small energy saving that it provided annually. But when green roofs are analyzed from an

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ecological point of view, they are recommended because this technology helps to mitigate the CO2 emissions by reducing the energy consumption for cooling and heating in residential areas.

4. CONCLUSIONS A theoretical-experimental study was carried out to evaluate the thermal, environmental, and economic performance of a residential building with a traditional and green roof under the weather conditions of eight cities in Mexico. According to the results, it is concluded that: • From the experimental study conducted in Cuernavaca, Morelos, the traditional and green roof had a maximum interior surface temperature of 38.9 and 25.5 °C, respectively. The previous values indicate that the vegetation layer functions as a solar barrier and prevents radiation from reaching the roof slab exterior surface. • From the validation with the experimental data, it was found a maximum error of 3.55 and 2.17% for the temperature of the interior surface of the traditional and green roof, respectively. • From the annual simulation results, the cities with the more significant reductions for the average indoor air temperature were Hermosillo with 4.7 °C and La Paz with 3.2 °C; On the other hand, Monterrey was the city with the smallest indoor air temperature reduction, with 2.2 °C. • The cities with the highest percentage in the reduction of energy to heat the building are Chetumal, Colima and Hermosillo with 45.7, 42 and 36.4%, respectively.

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• The emissions are related to the consumption of electricity to condition the residential building located in different cities. The cities with the more significant reductions of the CO2 emissions were Chetumal, Colima, and Hermosillo with 45.7, 42, and 36.4%, respectively. • Finally, the economic study revealed that a minimum of 8.8 years is needed to recover the green roof investment cost in Hermosillo, which was the city with the greatest annual savings. The useful life of the green roof is approximately 20 years, and the cities of Tlaxcala and Monterrey exceed this period, so there is no economic benefit; in this case, the benefit is only for the environment. In general, the simulation results of the green roof showed that the warm sub-humid and very arid warm weather present the best performance with a decrease in the energy required for air conditioning up to 45.0%. Consequently, the annual savings are more significant, and the payback period is lower compared to the other cities; therefore, the use of green roofs is recommended for hot sub-humid and warm very arid climates.

Conflict of Interest None. Acknowledgement A. Ávila-Hernández acknowledges the support provided by the Consejo Nacional de Ciencia y Tecnología (CONACYT) given through its doctorate scholarship program.

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L.F. Cabeza, A. De Gracia, A.L. Pisello, Integration of renewable technologies in historical and heritage buildings: A review, Energy and Buildings 177 (2018) 96–111. L.F.M. Francis, M.B. Jensen, Benefits of green roofs: A systematic review of the evidence for three ecosystem services, Urban Forestry and Urban Greening 28 (2017) 167–176. M. Foustalieraki, M.N. Assimakopoulos, M. Santamouris, H. Pangalou, Energy performance of a medium scale green roof system installed on a commercial building using numerical and experimental data recorded during the cold period of the year, Energy and Buildings 135 (2017) 33–38. M. Musy, L. Malys, C. Inard Assessment of direct and indirect impacts of vegetation on building comfort: a comparative study of lawns, green walls and green roofs, Procedia Environmental Sciences 38 (2017) 603–610. M.A. Chagolla-Aranda, E. Simá, J. Xamán, G. Álvarez, I. Hernández-Pérez, E. Téllez-Velázquez, Effect of irrigation on the experimental thermal performance of a green roof in a semi-warm climate in México, Energy and Buildings 154 (2017) 232–243. P. Ferrante, M. La Gennusa, G. Peri, G. Rizzo, G. Scaccianoce, Vegetation growth parameters and leaf temperature: Experimental results from a six plots green roofs’ system, Energy 115 (2016) 1723–1732. P. Ferrante, M. La Gennusa, G. Peri, G. Scaccianoce, G. Sorrentino, Comparison between conventional and vegetated roof by means of a dynamic simulation, Energy Procedia 78 (2015) 2917–2922. R. Djedjig, S.E. Ouldboukhitine, R. Belarbi, E. Bozonnet, Development and validation of a coupled heat and mass transfer model for green roofs, International Communications in Heat and Mass Transfer, 39 (2012) 752–761. S. Frankenstein, G. Koenig, FASST Vegetation models, Cold Regions Research and Engineering Laboratory (2004) 56. S. Guzmán-Sánchez, D. Jato-Espino, I. Lombillo, J.M. Diaz-Sarachaga, Assessment of the contributions of different flat roof types to achieving sustainable development, Building and Environment 141 (2018) 182–192. 37

S. Hodo-Abalo, M. Banna, B. Zeghmati, Performance analysis of a planted roof as a passive cooling technique in hot-humid tropics, Renewable Energy 39 (2012) 140–148. S. Quezada-García, G. Espinosa-Paredes, A. Vázquez-Rodríguez, J.J. Ambriz García, A.M. EscobedoIzquierdo, Sensitivity analysis of green roof, International Journal of Green Energy (2014) 1-33. S. Quezada-García, G. Espinosa-Paredes, M.A. Escobedo-Izquierdo, A. Vázquez-Rodríguez, R. Vázquez-Rodríguez, J.J. Ambriz-García, Heterogeneous model for heat transfer in green roof systems, Energy and Buildings 139 (2017) 205–213. S.G. Talat, S.S. Reynolds, Who should own a renewable technology? Ownership theory and an application, International Journal of Industrial Organization 63 (2019) 213–238. Secretaría de Energía (SENER), Balance Nacional de Energía: Consumo final de energía por sector, México,

(2017).

Available

at:

https://www.gob.mx/cms/uploads/attachment/file/414843/Balance_Nacional_de_Energ_a_2017.pdf (Accessed February 17, 2019). Sistema

de

Información

de

Tarifas

de

Agua

Potable

(SITAP),

(2018).

Available

at:

http://187.189.183.90/usodomestico.php (Accessed August 8, 2019). Y. Duan, X. Jiang, Visualizing the change of embodied CO2 emissions along global production chains, Cleaner Production 194 (2018) 499–514. Y. He, H. Yu, A. Ozaki, N. Dong, S. Zheng, Influence of plant and soil layer on energy balance and thermal performance of green roof system, Energy 141 (2017) 1285–1299. Y. Qin, M. Zhang, J.E. Hiller, Theoretical and experimental studies on the daily accumulative heat gain from cool roofs, Energy 129 (2017) 138–147.

38

FIGURE CAPTION

Figure 1. Test cells with green roof and traditional roof, (a) real image and dimensions of each component, (b) location of sensors. Figure 2. TR: (a) Outer roof temperature, (b) Ceiling roof temperature, (c) Indoor temperature; and GR: (d) Outer roof temperature, (e) Ceiling roof temperature, (f) Indoor temperature and (g) Vegetation temperature. Figure 3. Experimental result for the volumetric water content (VWC). Figure 4. Reference Building, (a) Plant view and dimensions, (b) Front and right view, rear, rear and left side view, (c) Thermal zones of the building. Figure 5. Cities considered in this work. Figure 6. Hermosillo city: (a) and (b) Air temperatures by level for TR and GR; (c)and (d) Air temperatures for the different zones for TR and GR. Figure 7. Hermosillo city: (a) outer roof temperature for TR and GR, (b) ceiling roof temperature for TR and GR, (c) Heat gain of the roof for TR and GR and (d) vegetation and outer temperature for GR. Figure 8. Volumetric water content for the GR in Hermosillo city. Figure 9. Thermal behavior of the reference building at the upper level for TR and GR for each city. Figure 10. Energy demand of the reference building for TR and GR for each city.

39

(a)

(b)

40

FIGURE 1 Traditional roof

(a)

(b)

(c) Green roof

41

(d) (e)

(f) (g)

Figure 2.

42

FIGURE 3

43

(a)

(b)

(c)

44

FIGURE 4

FIGURE 5

45

(b)

(a)

(d)

(c)

FIGURE 6

46

(a)

(b)

(c)

47

(d) FIGURE 7

Figure 8

48

(a) Arid warm climate and sub-humid warm weather

(b) Temperate weather

(c) Semi-arid warm climate and semi-arid temperate climate 49

FIGURE 9

(a) Arid warm climate and sub-humid warm weather

50

(b) Temperate weather

(c) Semi-arid warm climate and semi-arid temperate

climate

FIGURE 10

Table 1. Optics and thermo-physical properties of the vegetation and concrete roof.

Properties

Optics

Thickness

Thermo-physical

(m)

Roof type component

Absorbance

Green roof

Concrete

Emissivity

Thermal conductivity

Density

(W/mK)

(kg/m³)

Specific heat (J/ kgK)

Vegetation

0.72

0.90

-

-

-

0.08

Substrate

0.85

0.95

0.25

1370

800

0.06

Slab

-

-

1.74

2300

840

0.1

Slab

0.6

0.82

1.74

2300

840

0.1

51

Table 2. Minimums and maximums of temperature and error of the modeling of the two types of roofs.

Traditional Roof Variable

Data

Maximum

Minimum

Average

Outer roof

Experimental

49.0

15.5

28.1

temperature

Simulated

49.3

17.3

29.4

Ceiling

Experimental

38.9

16.9

25.9

temperature

Simulated

36.2

17.7

25.6

Indoor

Experimental

27.4

18.0

23.1

temperature

Simulated

27.0

18.9

23.2

RMSE

MBE

14.0

1.31

3.55

-0.32

1.15

0.11

Green Roof Variable

Data

Maximum

Minimum

Average

Outer roof

Experimental

41.0

15.0

23.5

temperature

Simulated

38.3

14.7

23.7

Ceiling

Experimental

25.5

20.9

23.4

temperature

Simulated

25.4

20.0

23.2

Experimental

25.5

19.8

23.6

Simulated

25.7

20.8

23.7

Vegetation

Experimental

42.3

14.7

23.9

temperature

Simulated

40.0

15.4

24.2

Indoor temperature

RMSE

MBE

2.20

0.20

2.17

-0.20

0.59

0.05

1.30

0.12

52

Table 3. Construction materials and thermophysical properties.

Element

Material Tile

Thickness

Cp

λ

ρ

U

(cm)

(J/kgK)

(W/mK)

(kg/m3)

(W/m²K)

1

795

1.136

2600

Ground floor level

Upper floor

Walls

15.09 Concrete

10

840

1.74

2300

Tile

1

795

1.136

2600

Concrete

10

840

1.74

2300

Plaster

1.5

1000

0.372

800

Plaster

1.5

1000

0.372

800

Brick

14

800

0.81

1600

Mortar

1.5

837

0.72

1890

Plaster

1.5

1000

0.372

800

Roof

9.38

4.27

10.23 Concrete

10

840

1.74

2300

53

Table 4. Area, materials and properties of the windows and doors.

Facade

Description

Material

Area

U

(m²)

(W/m²K)

Window 1

Glass

1.54

5.95

Window 2

Glass

1.85

5.95

Window 3

Glass

0.90

5.95

Window 4

Glass

0.92

5.95

Main door

Wood

1.78

1.78

Window 5

Glass

1.62

5.95

Back door

Wood

1.78

1.78

Window 6

Glass

1.68

5.95

Window 7

Glass

1.68

5.95

Window 8

Glass

1.80

5.95

Window 9

Glass

0.75

5.95

Front

Right side

Rear

Left side

54

Table 5. Average monthly weather for the eight cities.

Monterrey,

La Paz,

Hermosillo, CDMX

Nuevo León

Month T (ºC)

HR

G

Baja California Sur Wind

T

HR

G

Wind

Sonora T

HR

G

Wind

T

(%) (W/m²) (m/s) (ºC) (%) (W/m²) (m/s) (ºC) (%) (W/m²) (m/s) (ºC)

HR (%)

G

Wind

(W/m²) (m/s)

Jan.

14.3

55

143.4

2.0

20.6

54

172.7

2.0

19.0

30

153.9

1.9

13.9

47

182.3

2.0

Feb.

16.9

67

153.5

2.5

22.1

51

201.4

2.2

20.7

33

194.6

1.9

17.8

36

225.8

2.0

Mar.

19.2

63

181.7

2.8

23.5

50

246.7

2.6

22.9

31

252.8

2.3

18.9

39

247.1

2.2

Apr.

24.6

54

227.6

3.2

25.2

46

291.5

2.8

25.2

25

286.6

2.3

19.5

40

249.2

2.2

May.

25.8

59

235.9

3.4

28.6

38

313.8

2.8

29.0

20

325.3

2.5

18.1

56

210.7

1.8

Jun.

28.6

64

235.9

3.8

30.8

50

311.4

3.2

34.6

34

329.3

2.6

18.0

69

195.7

1.7

Jul.

29.4

58

248.4

3.4

31.5

63

262.2

2.6

32.7

52

280.9

2.3

17.2

68

215.3

1.8

Aug.

29.9

58

248.3

3.0

31.3

67

244.2

2.3

31.8

57

259.8

2.1

17.7

65

229.5

1.7

Sep.

25.9

74

167.5

2.8

29.9

72

213.3

2.7

30.7

60

221.8

2.1

17.4

71

175.3

1.6

Oct.

24.6

68

171.2

2.3

28.4

65

215.8

2.1

27.5

50

205.4

1.9

16.4

68

180.6

1.8

Nov.

16.6

66

127.0

2.1

24.6

60

182.6

2.3

21.6

40

172.7

2.0

16.2

56

184.7

1.8

Dec.

16.2

79

85.1

2.1

21.2

68

152.9

2.0

17.6

47

136.6

1.8

15.1

55

168.4

2.0

Average

22.7

64

185.5

2.8

26.5

57

234.0

2.5

26.1

40

235.0

2.1

17.2

56

205.4

1.9

Month T (ºC)

Tlaxcala,

Pachuca,

Colima,

Chetumal,

Tlaxcala

Hidalgo

Colima

Quintana Roo

HR

G

Wind

T

HR

G

Wind

T

HR

G

Wind

T

(%) (W/m²) (m/s) (ºC) (%) (W/m²) (m/s) (ºC) (%) (W/m²) (m/s) (ºC)

HR (%)

G

Wind

(W/m²) (m/s)

Jan.

12.3

57

219.7

1.8

10.9

39

200.2

1.5

23.0

70

200.9

1.3

22.9

62

168.5

1.7

Feb.

15.4

47

250.6

1.6

15.1

34

239.4

1.5

23.9

68

236.9

1.5

25.8

57

241.9

2.8

Mar.

17.1

50

277.8

1.9

16.9

37

269.2

1.9

24.7

55

284.8

1.6

26.6

57

267.9

3.4

55

Apr.

18.3

48

282.1

2.0

18.1

45

273.6

1.9

26.0

61

285.3

1.7

27.1

60

279.4

4.0

May.

17.5

61

249.3

1.8

17.0

58

255.2

1.9

27.3

67

259.8

1.6

27.4

57

261.0

3.8

Jun.

17.4

77

221.5

1.6

17.3

70

211.6

1.8

26.5

81

234.2

1.4

28.1

57

241.6

4.0

Jul.

16.6

73

244.5

1.7

16.4

70

254.1

1.9

26.5

78

258.8

1.4

28.8

53

279.1

3.2

Aug.

17.1

69

260.5

1.7

16.6

68

263.0

2.0

26.3

79

237.6

1.4

28.9

54

263.8

2.8

Sep.

16.9

76

216.7

1.7

16.5

73

211.7

1.9

25.2

84

201.2

1.2

27.6

58

202.6

2.7

Oct.

15.9

74

218.2

1.6

14.7

72

197.1

1.7

26.1

82

204.1

1.3

27.9

55

200.4

2.3

Nov.

15.1

66

214.5

1.6

13.7

66

193.3

1.5

24.1

76

181.0

1.2

24.2

58

182.4

1.9

Dec.

13.5

66

193.7

1.6

12.3

68

168.8

1.6

23.0

73

177.3

1.2

24.0

60

170.5

1.5

Average

16.1

63

237.4

1.7

15.5

58

228.1

1.7

25.2

73

230.2

1.4

26.6

57

229.9

2.8

Table 6. Parameters of the green roof for the simulations.

Green roof parameters

Vegetation

Soil

Height of Plants

0.08

(m)

Leaf Area Index

5

Leaf Reflectivity

0.28

Leaf Emissivity

0.9

Minimum Stomatal Resistance

180

(s/m)

Thickness

0.06

(m)

Conductivity

0.25

W/mK

Density

1370

kg/m3

56

Specific Heat

800

Saturation Volumetric Moisture

J/kgK

0.5

Content Residual Volumetric Moisture

0.01

Content Initial Volumetric Moisture Content

0.5

Table 7. Temperatures in each of the thermal zones with traditional and green roof, and the air temperature reduction.

Temperature reduction Traditional roof Month

Green roof

T amb

TopF LowF LowF

R1

R2

R3

LowF

R1

R2

R3

R1

R2

R3

Jan.

19.0

20.6

23.2

23.2

21.3

20.3

22.3

22.4

20.2

0.2

1.0

0.8

1.1

Feb.

20.7

22.8

26.0

25.9

24.1

22.5

24.7

24.7

22.6

0.3

1.3

1.3

1.5

Mar.

22.9

25.2

28.7

28.6

27.2

24.7

26.6

26.2

24.8

0.5

2.1

2.4

2.4

Apr.

25.2

27.4

30.8

30.6

29.9

26.7

27.9

27.3

26.8

0.7

2.9

3.3

3.1

May.

29.0

31.2

34.8

34.5

34.3

30.3

30.9

29.9

30.2

0.9

3.9

4.7

4.1

Jun.

34.6

36.8

40.5

40.2

40.2

36.0

36.8

35.8

36.3

0.9

3.6

4.4

3.9

Jul.

32.7

34.8

38.2

38.0

37.9

34.1

35.4

34.5

34.8

0.7

2.8

3.5

3.0

Aug.

31.8

33.9

37.3

37.1

36.7

33.3

34.8

34.2

34.0

0.6

2.5

2.9

2.7

Sep.

30.7

32.8

36.1

35.9

35.0

32.3

34.3

33.8

33.0

0.4

1.8

2.1

2.0

Oct.

27.5

29.9

33.4

33.3

31.7

29.5

31.9

31.6

29.9

0.4

1.6

1.7

1.8

57

Nov.

21.6

23.7

26.9

26.8

24.8

23.5

26.0

26.1

23.7

0.2

0.9

0.7

1.0

Dec.

17.6

19.2

21.8

21.8

19.9

19.1

21.5

21.7

19.5

0.1

0.3

0.2

0.5

Aver.

26.1

28.2

31.5

31.3

30.2

27.7

29.4

29.0

28.0

0.5

2.1

2.3

2.3

Min

17.6

19.2

21.8

21.8

19.9

19.1

21.5

21.7

19.5

0.1

0.3

0.2

0.5

Max

34.6

36.8

40.5

40.2

40.2

36.0

36.8

35.8

36.3

0.9

3.9

4.7

4.1

58

Table 8. Normalized energy demand for cooling and heating for traditional and green roofs.

Month

Traditional roof

Green roof

Energy saving

(kWh/m²)

(kWh/m²)

(%)

Cooling

Heating

Cooling

Heating

Cooling

Heating

Jan.

1.57

7.39

0.31

8.99

80.6

-21.7

Feb.

3.96

3.10

1.37

4.04

65.3

-30.3

Mar.

8.05

0.22

3.09

0.95

61.6

-332.6

Apr.

12.77

0.11

5.98

0.28

53.1

-143.5

May.

20.36

0

11.03

0

45.8

0

Jun.

27.15

0

14.45

0

46.8

0

Jul.

26.83

0

15.18

0

43.4

0

Aug.

24.65

0

14.40

0

41.6

0

Sep.

22.06

0

13.33

0

39.6

0

Oct.

16.75

0

10.30

0

38.5

0

Nov.

4.12

1.88

1.81

2.57

56.1

-37.0

Dec.

0.74

10.50

1.92

12.22

-158.9

-16.4

Sum

169.00

23.20

93.17

29.05

Total

192.20

122.22

59

Table 9. CO2 emissions

Total City

Roof

charges (kWh/year)

TR

9,238.3

CO2 emitted (ton CO2/year)

CO2 reduction (%)

5.4 45.7

Chetumal GR

5,013.0

2.9

TR

8,404.8

4.9 42.0

Colima GR

4,877.0

2.8

TR

16,683.3

9.7 36.4

Hermosillo GR

10,609.0

6.2

TR

12,142.9

7.1 33.0

La Paz GR

8,137.0

4.7

TR

11,736.8

6.8 28.1

Pachuca GR

8,440.0

4.9

TR

12,494.4

7.3 27.5

CDMX GR

9,053.0

5.3

TR

9,913.0

5.8 13.5

Tlaxcala

Monterrey

Charges and

GR

8,570.0

5.0

TR

10,446.4

6.1

7.3

60

GR

9,679.0

5.6

Table 10. Electricity and water fees and payback period.

Recovery Water

Electricity

Annual savings

period

($/0-10 m3)

($/kWh)

($)

(Years)

Hermosillo

71.90

1.44

7,884.7

8.8

Chetumal

98.10

1.49

5,118.1

13.6

La Paz

104.78

1.55

4,951.6

14.1

Colima

64.68

1.50

4,515.0

15.4

CDMX

126.70

1.55

3,813.5

18.3

Pachuca

106.44

1.50

3,667.2

19.0

Tlaxcala

80.60

1.50

1,046.8

66.5

Monterrey

43.73

1.43

572.3

121.7

City

61