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ScienceDirect Energy Procedia 101 (2016) 408 – 415

71st Conference of the Italian Thermal Machines Engineering Association, ATI2016, 14-16 September 2016, Turin, Italy

Carbon dioxide (CO2) sequestration and air temperature amelioration provided by urban parks in Rome Loretta Gratania*, Rosangela Catonia, Giacomo Pugliellia, Laura Varonea, Maria Fiore Crescentea, Silvia Sangiorgiob, Francesca Lucchettab b

a Department of Environmental Biology, Sapienza University of Rome, P.le Aldo Moro 5, Rome, 00185, Italy Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana 18 Rome, 00184, Italy

Abstract Urban landscapes are rapidly expanding globally and transforming the structure and function of urban areas, thereby influencing the quality of life. Cities account for more than 70% of the energy related to global greenhouse gases, which is expected to rise up to 76% by 2030. Taking into account that over 50% of the world’s population lives in cities and more than two thirds are expected by 2050, the problem of mitigating the atmospheric CO2 concentration is considerable. The urban areas covered by parks, gardens, tree-lined avenues, sport fields, and hedges are important sinks for carbon dioxide (CO2) by storing carbon through photosynthesis to form plant biomass. Despite plant CO2 sequestration is an important ecosystem service, the relationship between urban park vegetation and CO2 emission reduction is not completely clarified. In this context, the main objective of our research was to evaluate the role of urban park vegetation in improving air quality in Rome in terms of CO2 concentration and air temperature. In particular, we analyzed the relationship among the different vegetation types, size and position of an historical urban park in Rome. Moreover, since the presence of buildings within urban parks determines CO2 emissions closely related to their purpose of use, it is important to evaluate their impact in order to set instruments for their retrofit, considering the necessity of a compromise among the energy audit, the use of renewable energy systems and preservation of cultural heritage. 2016The TheAuthors. Authors. Published Elsevier © 2016 © Published by by Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the Scientific Committee of ATI 2016. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Scientific Committee of ATI 2016. Keywords: buildings, CO2 sequestration; energy consumption, urban parks; temperature, vegetation.

* Corresponding author. Tel.e fax: +39 06 49912358. E-mail address: [email protected]

1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Scientific Committee of ATI 2016. doi:10.1016/j.egypro.2016.11.052

Loretta Gratani et al. / Energy Procedia 101 (2016) 408 – 415

1. Introduction Carbon dioxide (CO2) concentration is one of the most abundant anthropogenic greenhouse gases which continues to increase globally reaching 400 ppm on a daily basis at Mauna Loa in 2013 [1]. The exchange of CO2 over cities is largely governed by anthropogenic emissions originating from road traffic and local heating with natural gas, oil or coal [2]. Cities account for more than 70% of the energy related to global greenhouse gases, which is expected to rise up to 76% by 2030, due to artificial surfaces, fossil fuel combustion and traffic volume [3]. Taking into account that over 50% of the world's population lives in urban areas and more than two thirds are expected to live in cities by 2050 (UN, United Nations, 2010), the problem of mitigating CO2 concentration in cities is considerable. The green areas (i.e. segments of urban areas covered by parks, gardens, sport fields, tree-lined avenues and hedges) have a significant role in the local carbon cycle of the city [4]. Vegetation in cities, particularly trees, can affect urban air quality [5]. Plants are a sink for CO2 by storing carbon throughout photosynthesis to form plant biomass [6]. In addition, the green areas have a recreational role which generate positive social and psychological effects improving the quality of the life [7, 8]. Thus, urban greening can generate significant ecosystem services (i.e. all those benefits related to ecosystems, which can have a direct or indirect impact on economic activities). In cities, parks and green spaces are of a strategic importance for the quality of life [9]. A park experience may reduce stress [10], enhance contemplativeness, rejuvenate the city dweller, and provide a sense of peacefulness and tranquility [9, 11]. This essential health-promoting function should be preserved [11]. Moreover, urban vegetation in parks can better contribute to reduce outdoor noise from road traffic, in comparison to plastic or other such man–made material barriers by the capacity of leaves to absorb acoustic energy [12]. Rome is characterized by a large volume of green areas (93000 ha, 72% of the entire municipal area) including natural protected areas, urban parks, public gardens, tree-lined streets and agricultural areas. A large part of the urban green areas is covered by historical residences with large parks. Moreover, since the presence of buildings within urban parks determines CO2 emissions closely related to their purpose of use, it is important that they are retrofitted. Moreover, these buildings have to be preserved in their valuable characteristics. It is necessary to achieve a compromise between an energy audit, use of renewable energy systems, and preservation of the cultural heritage [13]. Trees in the surrounding of buildings reduce air temperature and CO2 emissions associated with electric power production and consumption of natural gas by reducing the demand for heating and air conditioning in the buildings they shelter [14]. The main objective of this research was to analyze the positive effects of plants on buildings in a representative historical park in Rome, Villa Torlonia, (41°91’N; 12°30’E). The Limonaia and the surrounding vegetation was analyzed considering their characteristics: the microclimate variables in dependence of the different kind of trees and their extention, and the energy consumption of the restaurant, which were simulated with TRNSYS. Nomenclature COP CS EER LAI NP P heat PFD% TB TV T ext T int TPS

Coefficient of Performance CO2 carbon sequestration capacity [kg CO2 day-1] Energy Efficiency Ratio leaf area index [m2LA m2PC] net photosynthetic rate [µmol m-2s-1] heating demand [kW] photon flux density extinction [%] air temperature of the building wall [°C] air temperature under the canopy of the tree group [°C] external temperature [°C] internal temperature [°C] total photosynthetic leaf surface area [m2]

2. The study area Villa Torlonia is one of the historical villas in Rome built at the beginning of the 19th century. His area covers approximately 132000 square meters, and is characterized by different buildings and landscapes, as a result of the

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various settings conferred by architects and landscape architects who worked on it in subsequent periods. Among the several buildings inside the park, our study focuses on the one with the highest consumption and continuity of use: Limonaia, built in 1840, is now a cafè/restaurant. 2.1. The building: geometry and construction aspect The building is single storey, walls are not insulated and the roof is double pitched. While walls consist of one single layer, as in Table 1, roof and floor are composed by three layers each. Transparent surfaces are composed by: a metallic frame (15% of the surface) and single layer glazings (85% of the total surface). Basing on these characteristics, it is expected a low class of efficiency for the building [15, 16, 17]. Further details on geometry, percentage of transparent surfaces and materials are in Table 1, 2 and 3. As regards natural solar radiation, Limonaia doesn’t take a big advantage of it, because the southern side adjoins a warm space, and has a wide green area beside. Anyway, the effect of natural solar radiation is taken into account by the simulation tool. N

Fig. 1. (a) solar path and shadows; (b) layout of Limonaia. Table 1. Building Geometry Side N E-W S (internal) roof(a) floor

Length [m] 15 34 15

External Surface [m2] 105 204

Height min-max [m] 6 -8 6 6-8

Internal Surface [m2]

Volume [m3]

105 (b) 530 510 total 1553

105

3080

Table 2. Transparent surfaces SIDE N E W

Gates NG 1 3 3

SG [m2] 10 30 30

Windows NW SW [m2] 1 4,52 7 19,60 5 13,58

Transparent surface (Gates + Windows) Stot,Glazing [m2] Stot,Frames [m2] Stot,Transparent [m2] = Stot,F + Stot, G 12,342 2,178 14,52 42,16 7,44 49,6 37,01 6,57 43,58

Table 3. Building Materials Layer Thickness[m]

Walls stone 0,63

Roof stone 0,025

insulation 0,076

Concrete 0,102

Floor wood 0,03

Insulation 0,03

claytile 0,05

2.2. Climatic Data Referring to UNI 9019: 1987 norm, Italy is divided in climate zones, in order to set the period of use of heating systems (while cooling period is not determined). Rome is in the climate zone “D”, so heating systems use is allowed from November, 1st to April, 15th. As the surface of the city is very wide, the temperature may vary among the different zones. Generally, the reference temperature is taken in the small town of Ciampino, 13 km far from the center of the city, but several measure stations are placed in other areas, under control of agencies or research centers like ARSIAL – Regione Lazio. In particular, the Meteorological Station is located in Via Rodolfo Lanciani,

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near to Villa Torlonia. The collected data were used in the following analysis and compared with the punctual measurements taken in Villa Torlonia in reference days (February, 4th and March, 11th). 2.3. Energy consumption x Occupation schedule and metabolism The interior space is equipped to host about 80 people seated. In the following analysis, the whole day is considered, with 14 working hours, from 9 am to 11 pm. People turnout is estimated as in table 4. The activity level set in the simulation tool to evaluate the metabolic gain is 03 (ISO 7730) – seated, eating. Table 4. People turnout during a working day Time slot Number of Guests

00:00 – 09:00 am 0

09:00-12:00 am 25

12:00- 03.00 pm 70

03:00 – 08:00 pm 35

08:00-11.00 pm 70

11:00-12:00 pm 0

x Lighting, office devices, and kitchen equipment The artificial lighting system consists of 9 halogen lamps - 20W each - in the kitchen and other service areas, and 20 LED lamps - 35W each - in the bar and restaurant area. The total installed power is 250 W, and the brightness is 235 lm. The calculation of consumption and heat gain is set on the basis of the working hours and the natural enlightenment of the building. It is considered also a PC – 50W – at the cashier desk. The kitchen equipment (cooking tools and appliances) will be considered in terms of internal heat gain. In particular, 4 burners and 4 freezers are considered and set with default values in the simulation tool. x Heating and cooling strategies. Ventilation The heating and cooling system consists of an air-cooled air to water heat pump with a water chiller, (thermal power: 18.8 kW, COP = 2.7; cooling power: 16.7 kw, with EER = 2.6), supported by a monobloc refrigeration unit (cooling capacity: 20.3 kW, EER= 2). As regards ventilation, air change rate is set on 2 complete renewals per hour with external air, during the working hours. 3. Data collection and analysis 3.1. Analysis of the effect of vegetation In order to analyze the effects of vegetation on the building microclimate, three groups of trees around the Limonaia were considered. In particular, tree groups were selected at a distance of 7 m from the building (B): the first group (L1) was selected at west side of the building and was characterized by the presence of four Quercus ilex L. trees and two Tilia cordata L. trees. The second group (L2), at the south side, was characterized by a 20 T. cordata trees. The third area, at north side was characterized by the absence of trees (L3). The leaf area index (LAI, m2LA m2PC) was measured by the “LAI 2000 Plant Canopy Analyzer” (LICOR Inc., Lincoln, USA). The photon flux density extinction (PFD%) by each considered vegetation group was determined by a quantum radiometer photometer (LI-189 LI-COR, USA) with a quantum sensor LI-190SA. The PFD% was calculated as: (PFD% = PFDb/ PFDo) x 100 where PFDb was photon flux density below the group of trees and PFD o was the photon flux density measured in the open. The following microclimate variables were monitored between the end of April and the beginning of May 2016 on the building wall facing each selected group of trees (L1 and L2), in the area without trees (L3) and below the crown of L1 and L2. In particular, air temperature of the building wall (T B, °C), air temperature and air humidity (RH, %) under the canopy of the tree group (T V) were monitored simultaneously by HOBO data loggers (H08-00302, Onset HOBO Data Loggers, Cape Cod, MA) from 9.00 am to 13.00 pm at 0.10s intervals. The CO2 concentration around the building wall was measured at a distance of 1 m from the building and below the trees in L1 and L2 by a CO2 analyzer CP11 (Rotronic, UK). The CO2 sequestration capability of trees (CS, kg CO 2 day-1) was calculated by multiplying the total photosynthetic leaf surface area (TPS, m2) of L1 and L2 and the mean net photosynthesis and the total photosynthetic activity time (in hours) for the study period, according to Gratani and Varone [6]. TPS of L1 and L2

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was determined multiplying each LAI value by the projected tree crown area to the soil [6]. The net photosynthetic rate (NP, µmol m-2s-1) was measured by an open infrared CO2 gas analyzer (ADC LCA4, UK), equipped with a leaf chamber (PLC, Parkinson Leaf Chamber). Measurements were made on cloud-free days (PAR > 1000 µmol m-2s-1), in the morning to ensure that near-maximum daily photosynthetic rates were measured. On each sampling occasion, fully expanded leaves were used. Measurements were carried out on two representative trees per species in each area (three leaves per plants). The coefficient of CO2/C of 3.67 was used to convert the amount of sequestered CO2 in equivalent C amount, according to Evrendilek et al. [18]. 3.2. Temperatures The data set of temperatures, detected near the outer walls of Limonaia was analyzed considering some characteristics: x Orientation of the wall: eastern wall, being irradiated, shows a higher temperature in the morning; the western one has lower temperature during the early hours of the day, as it is shaded both by the building, both by the vegetation; x Presence of vegetation: the southern wall, overlooking the small forest, is not directly exposed to radiation during the day, and for this reason has the lowest temperature. Eastern and western walls are moderately exposed to vegetation, while the northern one has a large free space in the vicinity; x Presence of internal heat sources: the northern wall, whilst not enjoying direct irradiation, is in contact, towards the inside, with the equipment of the kitchen, and then heat sources which determine a high temperature, an effect which is added to the presence already said heating equipment. The mean value of the 4 temperatures of the walls was used to set the building’s behavior simulation, both with heating system on and off. The mean value was compared with the contemporary data from ARSIAL meteo station, which is located on the roof of a building, about 25 meters high; for this reason, all the data set has been brought to the ground level, adding 2°C to the values, in order to consider the Urban Heat Island effect [19, 20, 21]. Particularly, considering the time slot between 8 and 10 pm, the heat release by the buildings is more evident, so it’s reliable to obtain a higher temperature at ground level. Comparing the temperature, it could be supposed, for the winter months, a general similarity between the temperature inside and outside the villa, with a slight difference in heating consumption in the two settings. Otherwise, the first measurements in May show a lower temperature inside the park, which likely will lead to a lower demand of energy for air conditioning: this will be verified in the next phase of study, as soon as the complete summer reliefs will be available.

a)

b) Fig. 2 (left). Relationship between air temperature of the building wall (TB, °C) and leaf area index (LAI, m2LA m2PC), and between air humidity (RH, %) and air temperature (T, °C). Regression analysis equation and determination coefficient (R2) are shown.


Fig. 3 (right). Temperature trend inside the villa and outside; (a) in February and March and (b) in May.

3.3. Building and energy consumption The energy behavior of the building was analyzed using dynamic thermal simulation software (TRNSYS), considering both the structural characteristics of the building and the air conditioning system, as well as internal heat gains from kitchen equipment (freezers, burners…) and from artificial lighting. As regards air conditioning systems, consumption has been estimated by setting a comfort room temperature (20°C for winter) [22, 23]. The variation of temperature has been estimated considering the effects of external temperature, the solar radiation, the number of customers and employees inside the building, and the thermal effect of lighting and equipment. Lighting consumption has been determined on the basis of work hours. For food service, electricity consumption depends very lightly on the number of customers, so it has been determined basing only on work hours, and using a correction factor for every kind of appliance. Lighting consumption has been determined on the basis of work hours. Energy bills was kept into account in order to compare and verify the predictions. Gas consumption is related only to the kitchen use. It will be taken into account to calculate local CO2 emissions. 4. Results and discussion 4.1. Vegetation LAI values resulted significantly different among the three considered areas. In particular, it was 3.48 ± 0.18, 1.43 ± 0.28 and 0 in L1, L2 and L3, respectively. A significant difference was found in T V, which resulted 10% lower in L2 than in L1. PFD% was 72% in L1 and 98% in L2. The relationship between TB and LAI was fit by an exponential regression (TB = 26.938 e-0.024LAI). RH showed the same trend as TB attested by the significant (p < 0.001) negative linear relationship between the variables (R2 = 0.98) (see fig. 2), TB at L0 was 27.2 ± 0.2 °C decreasing by 6% and 8% in L1 and L2, respectively. The CO2 concentration in L1 and L2 was 460 ± 9 ppm and 452 ± 11 ppm, respectively and 430 ± 6 ppm in L3. The CO2 sequestration capacity was 5.80 kg CO2 day-1 and 37.5 kg CO2 day-1 in L1 and L2, respectively corresponding to a C amount of 1.6 and 10.2 per day.

Fig. 4. Different position of the groups of trees in respect to the building (B), L1 = side 1 with the presence of Quercus. ilex and Tilia cordata; L2 = side 2 with Tulia cordata; L3 = side 3 without trees.

4.2. Energy Consumption and CO2 emissions Analyzing the graphs relating to the two reference days, as shown in figure, it is confirmed that the value of the temperatures of the walls tends to differ in dependence of the exposure, the presence of vegetation and, for the northern part, the presence of the kitchen and all its equipment in this area of the building, which creates a sort of heat island. In particular, the role of vegetation in improving air quality is highlighted by the 7% (mean value) TB decrease in the site characterized by the presence of vegetation compared with the northern part without vegetation. Moreover, through the photosynthetic activity the vegetation in the surrounding of the building removes from the atmosphere an amount of CO2 equal to 21.7 kg CO2 (mean value) per day in the period April – May when the

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photosynthetic rates shown their maximum. Based on these results and considering the import role played by the vegetation in the air temperature amelioration we might suggest to planting trees also in the northern part of the Limonaia in order to reduce the heat island due to presence of the kitchen in this part. Using the average temperature of the walls as reference, the resultant value is very similar to ARSIAL data, detected by the temperature control unit. From this first set of data it can therefore be assumed that the winter heating consumption does not differ greatly from those calculated for the same building in a different position outside the park, while there is a benefit in terms of concentration of CO2. From a first view of the measured temperatures in the month of May (see fig. 3(b)), however, it is likely the scene of milder temperature than outside, with consequent lower consumptions in terms of cooling during the summer.

a)

b)

c)

Fig. 5. (a) Mean value of air temperature aside the walls. (b) and (c) Temperature trend inside the Building, with heating system ON and OFF, in February and March.

a)

b)

Fig. 6. Building temperature trend with ARSIAL data set in: a) February and b) March.

As regards CO2 emissions due to electrical energy consumption, the average consumption from electricity bills between 2006 and 2015 (86000 kWh/y) was used, and converted with the equivalent CO2 emission factor related to national electric power production in 2013 given by [24] that is equal to 337.43 gCO2/kWh. The average total CO2 emissions are about 29000 kg/y. Besides, natural gas consumption was taken into account: it is related only to cooking and food preparation. In 2015, the consumption of natural gas for the restaurant activity amounted to 1958 Sm3. CO2 emissions are evaluated as: CO2 emissions = gas volume * emission factor * oxidation factor. Using data from [25] for natural gas, CO2 emissions are about 3800 kg/y. CO2 emissions due to natural gas consumption are localized within the building and in its immediate proximity. 5. Conclusions Considering the benefits achieved by trees on the selected building, our results highlight the importance of vegetation mainly characterized by trees with larger size in the surrounding area. In fact, when properly placed and scaled around the building, during the summer period, trees can block unwanted solar radiation from striking the building and reducing its cooling-energy use. Another aspect that should be considered is the effects of different tree species (deciduous and evergreen species) during the year. In particular, while both evergreen and deciduous tree species decrease the cooling-energy use in summer, the deciduous species allow solar gain in buildings during winter than the evergreens. The analysis carried out on the relationship between the park and the building reveals

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that in the winter months (February and March) there are no considerable differences for a building located inside or outside the park. This is because any gains in terms of heat producted by internal appliances and cooking tools are mitigated by the shading effect due to vegetation. In view of this a thorough evaluation of the building without or with different heat gains on the north wall in accordance with some possible different intended uses for the building is needed. As regards the summer months (May) the data derived from the first analysis show that vegetation is a positive influence on the average temperature, by mitigating the internal heat sources on the north wall. In first approximation, it is possible to evaluate the optimal arrangement of the cooking tools and the internal appliances on the different walls of the building in order to benefit from internal heat gains in winter and the mitigating effects of the plants in summer. Results are presented in Table 5; referring only to the examined elements, further research and analysis are needed. As regards the appliances within the building, possible solutions for reducing consumption shall take into account the restrictions and the regulations for historic buildings. Therefore, feasible measures are very limited. Table 5. Optimal position of equipment inside the building with reference to the outside vegetation Vegetation Internal heat sources

winter

NORTH EAST SOUTH WEST

r o

NORTH summer r o

EAST winter

summer

winter

r r o

r r o

r r -

SOUTH summer r r -

WEST winter

summer

r o r

r o r

r: recommended solution, o: optional solution

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