A rapid increase in urbanisation and rising populations living in urban areas lead to major problems including increased rate of air pollution and global warming. Assessing the impact of buildings on wind ﬂow, air temperature and pollution dispersion on people at the pedestrian level in the urban design is therefore of crucial importance. In this study, the eﬀect of diﬀerent forms of urban vegetation including green roofs, green walls and trees on velocity, air temperature and air quality is assessed using Computational Fluid Dy- namics (CFD) for a selected area of the East Village. This study indicates that adding a building increases air temperature, pollution concentration, and velocity at the pedestrian level. A parametric analysis is conducted to assess the impact of various key parameters on air temperature, pollution, and velocity at the pedestrian level. These variables include wind speed which ranges from 4-8 m/s at a reference height of 10m, and vegetation cool- ing intensity which varies from 250-500 Wm-3. Three scenarios are tested in which the streets have no bottom heating, 2 °C bottom heating, and 10 °C bottom heating. Pollution is simulated as a form of passive scalar with an emission rate of 100 ppb s-1, considering NO 2as the pollutant. In all cases, vegetation is found to reduce air velocity, pollutant concentration and ∗Corresponding author: E-mail address: A.keshmiri@manchester.ac.uk (Amir Kesh- miri). temperature. However, the presence of vegetation in various forms alter the pattern of pollution dispersion diﬀerently. More speciﬁcally, the results in- dicate that planting trees (e.g. birch trees) close to the edge of buildings can decrease the air temperature by up to 2-3 °C at the pedestrian level. Increasing the cooling intensity of the vegetation from 250 to 500 W m-3 results in signiﬁcantly lower air temperature. Results also show that lower wind speeds result in higher concentration of pollutants at pedestrian level. Furthermore, it was found that combining green walls and trees is the most eﬀective strategy to improve thermal environment and air quality. The re- sults of this paper provide useful clues for the design of green solutions for improving air quality, outdoor pedestrian comfort, and thermal environment. Keywords: CFD, building engineering, green roofs, green walls, vegetation, pollution, UHI, urban design. 1. Introduction Urbanisation and construction of high-rise buildings lead to temperature rise in cities, a phenomenon known as the Urban Heat Island (UHI) eﬀect, as well as an increase in the rate of air pollution (1, 2). According to Oke et al.(3), urban heat islands can raise city temperatures by 2 to 8 °C. According to Mohajeran et al. (4), the temperature rise will be between 5 and 15 °C. It has been found by Grimmond et al. (5) that by 2030, 61% of the world’s pop- ulation would be living in cities. This suggests that UHI will become more intense as a result of deforestation and global warming. Appropriate actions mustbetakenaheadoftimetopreventtheseissues. Anothercriticalenviron- mental issue aﬀecting urban environments is air pollution. Pollution comes from a variety of sources, including cars, houses and industry. There are two types of air pollutants: primary and secondary air pollutants. Nitrogen ox- ides (NO x) is the common primary air pollutants discharged directly into the atmosphere from emission sources (6, 7). NO 2in excessive concentration can have negative consequences for human health, such as cardiovascular disease, 2 lung problem, respiratory symptoms, and allergies. High levels of NO xmay have negative eﬀects on plants and habitats leading to loss of biodiversity (8). These are unavoidable changes, so steps must be taken to eliminate or at least lessen their consequences for the sake of people’s comfort. Various studies have been carried out in order to reduce the temperature of the air in urban areas. Planting vegetations, changing building design parameters (e.g. aspect ratio, roof shape, etc.), wind speed, wind direction, and the amount of heat source are some of the main methods and parameters that have an impact on UHI and air pollution. Computational Fluid Dynamics has been widely used in the literature to examine the impact of these parameters on the thermal environment and pollution dispersion. For instance, Lin et al. (9) and Chen et al. (10) implemented the green wall on the surface of the building and evaluated the eﬀect of vegetation on the thermal environment. Memon et al. (11) assessed the eﬀect of building aspect ratio and wind speed on air temperature. Some studies have looked at the impact of green roofs on the surrounding environment for outdoor thermal comfort (12). Huang et al. (13) found the average drop in ambient air temperature utilising produced by green roofs in Tokyo is 0.3 °C whereas Chen et al. (14) gave an estimate of 0.1 °C. Baskaran et al. (15) conducted an experimental investigation to determine the eﬀect of a garden roof on air temperature reduction. Accord- ing to their ﬁndings, garden roofs are more eﬀective in reducing heat gain in the summer than heat loss in the winter. The eﬀect of a dom-shaped roof on air quality has been investigated by Hosseini et al. (16). The relative performance of green walls and green roofs for lowering air pollution was investigated by Qin et al. (17). Rafael et al. (18) assessed the impacts of green infrastructure on aerodynamic ﬂow and air quality. Gromke et al. (19) evaluated diﬀerent arrangement of trees on pollution dispersion. The majority of studies in this ﬁeld have focused on the impact of diﬀerent characteristics solely on air temperature or pollution. None of the works cited above considered the impact of various types of vegetation (e.g., green roofs, green walls, and trees) on air temperature, pollution, and velocity at 3 the same time. In this study, the eﬀects of green roofs, green walls and trees on air quality, air temperature and velocity are evaluated for a range of pa- rameter values using CFD. A selected area of the East Village of London Olympic Park is used as a case study. The CFD model is validated using wind tunnel data by Uehara et al. (20) and simulation results of Baik et al. (21). 2. Problem Deﬁnition and Methods 2.1. Test Case 1: Street canyon with bottom heating 2.1.1. Introduction In this section, a CFD model is used to predict the air temperature in ide- alised street canyons. The results of the present simulation are compared against wind tunnel data from Uehara et al. (20) and the simulation results of Baik et al. (22). It is worth mentioning that this case study has also been validated by other studies (11, 23, 24). 2.1.2. Computational domain and meshh In this case study, there are seven street canyons. The building’s height, H, and width, W, are both 1 m, as illustrated in Figure 1.For the discretization of this 2D model, a structured (trimmed cell) mesh was used. Using a 0.02m cell size, with a stretching ratio of 1.2, approximately 420,000 rectangular elements were generated. The target street canyon with bottom heating is the middle canyon in Figure 1. Bottom heating is deﬁned as the temperature diﬀerence between the ground’s surface and the ambient air temperature and represents the eﬀect of the sun warming the earth. The estimated normalised potential temperature and horizontal velocity are acquired at the centerline of the target street canyon. 4 Figure 1: 7 street canyons with bottom heating in the middle street. 2.1.3. Governing equations and boundary conditions In this case study, the temperature and velocity variations are computed using coupled ﬂow and energy equations. The ﬂow model is based on the RANS equations with the standard k- εturbulence model, as described in (25–27). The segregated ﬂuid temperature energy model is employed in this simulation. The energy equation is solved using the Segregated Fluid model in STARCCM+, with temperature as the solution variable. The equa- tion of state is then used to calculate enthalpy from temperature (28). The wind speed and temperature at the boundaries are given based on the bulk Richardson number (Rb) which is deﬁned by equation 1. Rb=gH(Ta−Tg)/(Ta+ 273) u2 h (1) where g is the gravitational acceleration, H denotes building height, Taand Tgstand for the ambient air and the ground temperature respectively. The velocity at the building height is denoted by uh. The simulated case has a bulk Richardson number of -0.27 and a ground heating of 2 °C. Wind speed at the inlet is set 0.5 m/s. The ambient air temperature at the inlet and outlet set as 20 °C. The outﬂow boundary has zero static pressure and the top of domain is symmetric. Table 1 represents a summary of the boundary conditions for Test case 4. 5 Table 1: Boundary conditions for Test case 1 Boundary conditions Value/Deﬁnition Inﬂow wind speed [m/s] 0.5 Width of street canyons [m] 1 Height of buildings [m] 1 Air temperature [ °C] 20 Ground temperature [ °C] 22 Richardson number -0.27 Outﬂow Zero static pressure Top of the domain Symmetry 2.2. Test cases 2 and 3: Selected areas of East Village of London Olympic park 2.2.1. Introduction In this section, some areas of East Village are selected and presented in Figure 2a and b. Test case 2 consists of 7 buildings, while Test case 3 is the same as Test case 2 but with one additional building. The focus of this section is on the impact of vegetations (trees, green roofs and green walls) on wind speed, air temperature and pollution dispersion in urban areas. The eﬃcacy of diﬀerent mitigation measures is quantiﬁed and compared in terms of area-weighted average of temperature, pollutant concentration and velocity magnitude. 2.2.2. Computational domain and grid The computational domain for all test cases in this section is the same as in (25). Building heights for Test case 2 range from 30 to 80 m, while those for Test case 3 range from 30 to 102 m. In this part of East Village, the additional building in Test Case 3 is the tallest building. In Test Case 2, there is no vegetation, however in Test Case 3, there are numerous types of vegetation that are deﬁned in Table 2. 6 Table 2: Test case 3 with various forms of vegetation. Case number Description Test case 3-a Without vegetation Test case 3-b With green roofs Test case 3-c With green walls Test case 3-d With trees Test case 3-e With combination of green roofs and green wall Test case 3-f With combination of green roofs and trees Test case 3-g With combination of green roofs and more trees Test case 3-hWith combination of green roofs and more trees: trees 2 m closer to the ground Test case 3-i With targeted combination of green walls and trees Test case 3-j With targeted combination of green walls and trees The size, type and location of trees are chosen based on the optimal layout of trees in (25). The thickness of green walls and roofs is 1 m. Green roofs and green walls are represented in this study by a block with a thickness of 1 m, as opposed to the real case where green roofs contain numerous layers (29).The trees are elevated 6 m above the ground, thus the green walls are similarly lifted 6 m. In Test Cases 3a and 3b, green walls and roofs are found on all of the building surfaces which are depicted by numbers 2 and 3 in in Figure 5b. Apart from the inﬂuence of individual vegetations, the eﬃcacy of combination strategies was also assessed. Figure 3 from cases e-h shows the combination of greenery. Cases ’i’ and ’j’ are targeted mitigation choices, and the reason for this conﬁguration will be detailed in section 3.5. Polyhedral mesh is used for discretisation of the domain with a a thin mesh with two layers on green roofs and green walls (See Figure 2). 7 (a) Test case 2 (b) Test case 3 (c) Test case 3-a (d) Test case 3-b (e) Test case 3-c (f) Test case 3-d Figure 2: Computational domain: (a) Test case 2, (b) Test case 3. Computational mesh for Test case 3: (c) without vegetation, (d) with green roofs, (e) with green walls, (f) with trees. 8 Test case 3-e Test case 3-f Test case 3-g Test case 3-h Test case 3-i Test case 3-j Figure 3: Geometry for Test case 3 with mitigation options. 2.2.3. Governing equations and boundary conditions The ﬂow and energy equations included in CFD simulations of this test case are the same as those for Test case 1. Heat conduction is neglected, and the walls are considered thin. The simulations are all run in steady state. The ﬂow and surface roughness boundary conditions are the same as in Ref. (25). For all test situations, the air temperature is set to 20 °C. Diﬀerent ground temperatures were used to generate various heating intensities, which repre- sent bottom heating, the diﬀerence between air and ground temperatures. To evaluate the eﬀect of vegetations (green roofs, green walls and trees) the source terms are added to the equations of momentum, turbulent kinetic en- ergy and energy dissipation. These equations and parameters are described in depth in refs. (25, 30–32). To account for the impact of vegetation on air temperature, a volumetric 9 cooling power is assigned to vegetation per unit volume which is a function of the leaf area density (LAD). Based on the work of Gromke et al. (30), volumetric cooling power can be estimated as 250 W m−3per LAD unity. To model traﬃc emissions at the bottom of the street canyon, additional equations are required. A passive scalar model is used to model pollution. The passive scalar model, which comprises of convection and diﬀusion terms, is chosen with an arbitrary passive scalar model to build up passive scalar tracking (here pollutant concentration). The diﬀusion is modelled in this study using a linear eddy-diﬀusivity model to calculate turbulent viscosity and the turbulent schmidt number which are essential variables for diﬀusion transport. The reader is referred to (28) to learn more about passive scalar’s transport and diﬀusion equations. In the simulation of contaminants in urban environments, the turbulent schmidt number plays a key role and its eﬀect on computed concentration was investigated in diﬀerent studies (31, 33–35). Jiang et al. (36) investi- gated the eﬀect of Schmidt number on the projected concentration ﬁeld in this study. He demonstrated that using the standard k- εmodel, a schmidt number of 0.4 produces the best results, and that a larger value leads to an overestimation of the concentration ﬁeld. Since in this study, the standard k-εis chosen for the simulations and based on the