The impacts of roadside vegetation barriers on the dispersion of gaseous traffic pollution in urban street canyons

Urban Forestry & Urban Greening 17 (2016) 80–91

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The impacts of roadside vegetation barriers on the dispersion of gaseous traffic pollution in urban street canyons Xiao-Bing Li a , Qing-Chang Lu a,∗ , Si-Jia Lu a , Hong-Di He b , Zhong-Ren Peng a,c,∗ , Ya Gao a , Zhan-Yong Wang a a State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China b Logistics Research Center, Shanghai Maritime University, Shanghai 200135, China c Department of Urban and Regional Planning, University of Florida, PO Box 115706, Gainesville, FL 32611-5706, USA

a r t i c l e

i n f o

Article history: Received 24 November 2015 Received in revised form 22 March 2016 Accepted 24 March 2016 Available online 7 April 2016 Keywords: CFD simulation Roadside air quality Traffic emissions Vegetation barrier height

a b s t r a c t Vegetation barriers have been widely applied along urban streets to improve roadside air quality. For a deep investigation of their influences, field measurements and numerical simulations are performed in this study. Carbon monoxide (CO) is selected as a representative of gaseous traffic emissions for both field observations and numerical models. Computational Fluid Dynamics (CFD) models of the standard k-␧ turbulent model and Eulerian approach for species transport are solved by FLUENT solver. Results obtained from numerical simulations show a good agreement with field observations on the distribution of roadside CO. In perpendicular wind conditions, both field observations and numerical simulations present a prominent CO reduction over the slow lanes (footpath and bikeway) when vegetation barriers exist. To effectively mitigate roadside air pollution, numerical simulations also provide the optimal heights for roadside vegetation barriers in the given street canyons. For street canyons with an aspect ratio (the ratio of building height to street width) ranging from 0.3 to 1.67, 1.1 m can be used as an optimal height, and 2.0 m could serve as an alternative if tall vegetation barriers are considered. For street canyons with an aspect ratio of lower than 0.3, 0.9 m to 2.5 m can be considered as the optimal heights for roadside vegetation barriers. According to sensitivity analysis, the optimal heights for vegetation barriers are largely insensitive to wind velocities in the given street canyons. In the more complicated urban street canyons and complex meteorological conditions, the optimal heights can be determined by specific numerical simulations. These findings are expected to provide important insights into alleviation of gaseous mobile emissions in terms of vegetation barrier design in urban streets. © 2016 Elsevier GmbH. All rights reserved.

1. Introduction Air pollution is a severe environmental problem, especially traffic emissions, which are predominant sources of air pollutants in urban environment globally (Kumar et al., 2013). Due to poor ventilation conditions, urban streets face more serious air pollution. To mitigate traffic-induced air pollution, scholars and practitioners have proposed several alleviation strategies. McNabola et al. (2013) summarized three approaches to improve urban air quality: (1) controlling the emitted quantity of pollutants (g), (2) controlling the emission intensity (g km−1 ), and (3) controlling the source-

∗ Corresponding authors at: School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. E-mail addresses: [email protected] (Q.-C. Lu), [email protected] (Z.-R. Peng). http://dx.doi.org/10.1016/j.ufug.2016.03.006 1618-8667/© 2016 Elsevier GmbH. All rights reserved.

receptor pathways. This classification of strategies for mitigating air pollution is also suitable for street-scale level. In spite of these efforts, it is becoming more difficult to control the quantity and intensity of traffic emissions due to a rapid increase in vehicles in developing countries such as China. A relatively effective yet still largely unexplored way for improving roadside air quality is to alter the space between vehicles and pedestrians. For this purpose, barriers, otherwise known as a passive control method, can be used to improve roadside air quality. Gallagher et al. (2015) reviewed the specific effects of different types of barriers such as porous barriers (trees and vegetation barriers) and solid barriers (noise barriers, low boundary walls, and parked cars) on roadside personal exposure to traffic-emitted pollutants. Regardless of differences in physical characteristics, these two barrier types can, to some extent, improve roadside air quality in urban street canyons (Gallagher et al., 2011, 2012, 2013; Halim

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et al., 2015; Tong et al., 2016). Acting as physical barriers between emission sources and nearby population, vegetation barriers have been evaluated as an effective strategy (Bowker et al., 2007; Hagler et al., 2011). Compared with solid barriers, vegetation barriers are more eco-friendly, and widely used to afforest urban arteries. Vegetation barriers mitigate roadside air pollution by affecting localized turbulence and changing natural dispersion patterns of trafficemitted pollutants (Gallagher et al., 2015; Janhäll, 2015). Steffens et al. (2012, 2013) summarized that two physical processes, dispersion and deposition, are mainly responsible for the mitigation function of vegetation barriers in roadside air pollution. The dispersion process is characterized by both deflection and recirculation of approaching air flows from the roadway. Accordingly, vegetation barriers affect the dispersion process of pollutants by altering in-canyon air flow structures. By contrast, the deposition process demonstrates Brownian diffusion, impaction, gravitation settling, and interception of particulate matters. In most situations, vegetation strategies used as passive controls are often characterized by trees and vegetation barriers. A number of studies have been conducted to investigate the impacts of trees and vegetation barriers on urban air quality at the macroscale level, that is, within hundreds of meters of a roadway (Brücher et al., 2000; Nowak et al., 2006; Tallis et al., 2011; Vos et al., 2013). However, the impacts of vegetation barriers at the micro-scale level (street-scale level, such as, at pedestrian level in the slow lanes) are seldom revealed. At the micro-scale level, the mitigation functions of vegetation barriers are limited by meteorological conditions, street configurations, vegetation barrier shapes, and so on. The literature revealed that, in most cases, perpendicular wind conditions are not favorable for ventilation in urban street canyons (Nazridoust and Ahmadi, 2006; Tominaga and Stathopoulos 2011, 2013). Especially in perpendicular wind conditions (such as 0 m s−1 to 20 m s−1 , and normal to vegetation barriers) with the presence of roadside trees, an obvious increase of pollutant concentrations on the leeward side (illustrated in Figs. 1 and 2) can be observed, and a moderate decrease of pollutant concentration on the windward side (illustrated in Fig. 2) is found at pedestrian level (Buccolieri et al., 2009; Gromke, 2011; Amorim et al., 2013; Abhijith and Gokhale, 2015). Vegetation barriers flanking roadways are able to act like low boundary walls to decrease roadside pollutant concentrations (Bowker et al., 2007; King et al., 2009; Gallagher et al., 2015). Tong et al. (2016) presented that an increase of vegetation barrier width is more favorable for mitigating roadside air pollution than vegetation barrier height. But this cannot determine if the influence of vegetation barrier height is negligible due to a limited spatial scale of this research, which is approximately 100 m to the roadway. Additionally, the impacts of vegetation barrier height on the dispersion of gaseous traffic-emitted pollutants over the slow lanes (footpath and bikeway) are seldom mentioned in previous studies. One objective in this study is to investigate the impacts of vegetation barriers on the dispersion of roadside air pollution. The other one is to attempt to find the optimal vegetation barrier heights to effectively reduce traffic-emitted pollutants over the slow lanes of urban streets to the greatest extent. Only dispersion processes of gaseous pollutants are investigated in this study. Any other processes that could lead to chemical transformations are not considered. Carbon monoxide (CO) is selected as a representative of gaseous traffic-emitted pollutants. Numerical models are configured based on the physical features and dimensions of the experimental site, and calibrated by field measurements. Computational Fluid Dynamic (CFD) code solver FLUENT 6.3.2 is used to perform numerical simulations. The findings presented in this study may not be generalizable in all types of street canyons and meteorological conditions, whereas provide urban planners

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with options when designing vegetation barriers in urban street canyons. 2. Methodology 2.1. Experiment site and street description Shanghai is one of the most densely populated metropolises in China with a population of approximately 24.25 million. Situated in the Yangtze River delta and adjacent to the East China Sea, Shanghai has a mild monsoon climate with warm humid summers and mild damp winters. With a large vehicle ownership, Shanghai is heavily polluted by traffic-induced pollutants. Therefore, traffic emissions have been a major contributor to urban air pollution, and account for approximately 21% of total carbon-related pollutants (Cao et al., 2012; Liu et al., 2015). Thus, effective measures are urgently needed to alleviate traffic-induced air pollution in urban environment. To investigate the potential effects of vegetation barriers in a real world urban street canyon, Dongchuan Road is selected as a case study. Dongchuan Road is a busy traffic corridor linking the Zizhu National High and New-Tech Development Zone to the Minhang Development Zone. As shown in Fig. 1(a), a section of street (121◦ 25 47.28 E, 31◦ 1 15.96 N) on Dongchuan Road located in the Minhang district is selected as the field monitoring site, and a prototype for numerical models. This road section is oriented northeast-southwest with four lanes in each direction, two of which are slow lanes (footpath and bikeway, shown in Fig. 1(b)), and the other two are motor lanes. As illustrated in Fig. 1(b) and Fig. 2, this road section is lined with camphor trees which have an average standing space of 5.5 m, on footpath. Two rows of vegetation barriers, which are densely foliated evergreen shrubs, flank the roadway. The vegetation barrier is 0.9 m in height and 1.5 m in width (see Fig. 1(b)). The total width of this road section, including footpaths and bikeways (slow lanes), is approximately 38 m. This street is flanked by buildings between 2 and 6 stories, and has an aspect ratio (AR, the ratio of building height to street width, H/W) of approximately 0.4. According to a classification of urban streets based on AR, the flow regime in this street canyon belongs to wake interference flow (WIF) (Li et al., 2009). Field observations were performed on the leeward, northwest-facing side of this road section. This road section is selected as the experimental site and numerical model prototype for several reasons. (1) This road section connects Shanghai Jiao Tong University and Auchan shopping mall. Additionally, several residential communities are also located nearby, generating a large pedestrian flow and making roadside air pollution a serious problem. (2) Except for Humin Road which intercepts Dongchuan Road approximately 100 m southwesterly and may be a potential CO contributor, no other major emission sources of CO are found in proximity to this monitoring site. Thus, CO emissions in this street canyon are predominantly emitted by vehicles. (3) Monitoring instruments can be located in Minhang campus of Shanghai Jiao Tong University to make continuous background records. (4) This road section includes two distinct parts, one is flanked by both trees and vegetation barriers, and the other is only flanked by trees. 2.2. Numerical model Two dimensional (2D) numerical models are developed to investigate the impacts of vegetation barriers on improvement of roadside air quality in urban street canyons. 2.2.1. Description of general flow and dispersion A cross section of street canyon model is outlined in Fig. 2. As indicated by previous studies, the standard k-␧ model is suitable for reproducing turbulence patterns in a simple street canyon

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Fig. 1. (a) Map of Dongchuan Road experiment site in Shanghai (green dot, inset map), urban background CO monitoring location in Minhang campus of Shanghai Jiao Tong University (blue dot), and the selected road section on Dongchuan Road (red line). (b) A view of slow lanes in the street canyon. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

Fig. 2. Outline of the street canyon model. Red dots (denoted by A, B, C, and D) represent field measurement positions on the leeward side in an urban street canyon. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

(So et al., 2005; Gallagher et al., 2011; Hong et al., 2012). Thus, the standard k-␧ turbulent model is used in numerical simulations, and the governing equations are as shown below.

The transport of traffic-emitted CO used Eulerian approach in numerical models and the corresponding partial differential equation is formulated as,

∂ /∂t + (Ui ) ∂ /∂xi = Sm

∂C␣ /∂t + Uj ∂C␣ /∂xj = ∂((D␣ + t /Sct )∂C␣ /∂txj )/∂txj



(1)

where C ˛ represents the concentration of pollutant species ˛ and D˛ means its diffusivity, Sct is turbulence Schmidt number.



∂Ui /∂t + Uj ∂Ui /∂t = −1/ ∂P/∂xi − ∂(u’i u’j )/∂xj 2

+∂ Ui /∂xj ∂xj

(2)

Sm is the specific source term, Ui represents the mean fluid velocity in direction i, P means pressure, u’i is the fluctuating velocity,  means fluid density,  represents kinematic viscosity, u’i u’j is the negative of turbulent stress tensor (Reynold stress tensor) divided by fluid density, which is ijT = −u’i u’j

(4)

(3)

2.2.2. Modelling of tree crowns and vegetation barriers Generally, vegetation barriers are filled with numerous small leaves and branches which can deflect and reflect approaching air flows. Thus, explicit numerical modeling of vegetation barriers is impossible at present because of complicated vegetation structures. To take the impacts of irregular leaves and branches on the dispersion of pollutants into account, vegetation is usually spatially averaged. Therefore, averaged flow speeds and turbulent statistics are usually used in numerical simulations (Wilson and Shaw, 1977; Steffens et al., 2012).

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In this study, the deposition process of traffic-induced particle pollutants is not considered and only deflection and recirculation processes of gaseous pollutants caused by vegetation barriers are investigated. Tong et al. (2016) noted that the impacts of solidvegetation barriers (solid barrier covered by vegetation on surfaces) on the dispersion of traffic emissions are similar to solid barriers. Many other studies have also indicated that densely foliated vegetation barriers can be modeled as solid barriers (Gromke et al., 2008; Gallagher et al., 2015). Considering vegetation barriers which are densely foliated on the selected road section, tree canopies and vegetation barriers are modeled as solid type in numerical models. Generally, tree canopy is developed as cuboid or sphere in numerical models (Mochida et al., 2008; Buccolieri et al., 2009; Salim et al., 2011; Balter et al., 2016). As shown in Fig. 2, tree canopies are modeled as spherical, and vegetation barriers are modeled as cuboidal based on their shapes in the real world. A series of heights which are, 0 m, 0.5 m, 0.9 m, 1.1 m, 1.5 m, 2.0 m, 2.5 m, 3.0 m, and 4.0 m for vegetation barriers are used in the CFD models to investigate the optimal heights. 2.2.3. Computational domain and mesh discretization One street in the real world usually consists of buildings with various heights on both sides. However, buildings are usually considered to be of uniform height in numerical simulations for simplification (Hang et al., 2012). Three heights for buildings are separately used in numerical models to investigate the effects of street configurations on the dispersion of traffic-emitted gaseous pollutants in the given street canyons. As noted in previous studies, emission sources of CO are always replaced by line sources located on a roadway (Buccolieri et al., 2009; Gallagher et al., 2012). Therefore, two CO emission sources, which have a same emission rate, are placed on the roadway in each direction (see Fig. 2). Bikeways 3.5 m in width are lined up along street, and footpaths 7 m in width are located 0.3 m above roadway surface. A computational domain in numerical models is set to twice the height of buildings to make sure that domain boundaries do not influence the accuracy of solutions. Outer walls of roadside buildings are used as the upstream and downstream boundaries, and a height of H (where, H is building height) above the building roof is set up as the low atmospheric boundary (see Fig. 2). Meshes in the computational domain are made by GAMBIT v2.3, which is a model development and meshing tool for FLUENT solver. The computational domain is discretized using unstructured triangular grids with a resolution of 0.5 m, which is fine enough to ensure the recommended minimal amount of 10 × 10 cells in the canyon cross section (Franke et al., 2007). Mesh size decreases towards domain boundaries (wind inlet, outlet, atmosphere boundary, and walls) with a compression factor of less than 1.1. Studies for grid independency have been performed to ensure that the simulation results are independent of domain size and mesh resolution. 2.2.4. Boundary conditions Wind inlet (see Fig. 2) of the computational domain uses velocity-inlet boundary condition and the pressure-outlet boundary condition is specified for outlet. Based on field observations, hourly mean wind speed (2 m s−1 ) is used as a baseline velocity for inlet wind. As illustrated in Fig. 2, the direction of inlet wind is normal to vegetation barriers. Another three velocities, which are 1 m s−1 , 3 m s−1 , and 20 m s−1 , are also used in numerical simulations to perform a sensitivity analysis. Emission sources of CO are defined as the velocity-inlet boundary condition, and an emission rate is estimated based on related traffic flows during the monitoring periods (see Nagpure et al., 2016 for detailed description). Order of magnitude of the estimated CO emission rate (which is 1e−5 m s−1 ) is used as the velocity of two CO inlets. Upper boundary of the computational domain is set up as a wall condition allow-

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ing slip velocity, which means advection flow may appear near the upper boundary to reproduce characteristics of the atmosphere aloft. All the remaining surfaces are defined as walls with no-slip velocity boundary conditions. Key parameters, which are investigated and used in numerical models and field measurements, are summarized in Table 1. 2.3. Measurements and field campaigns Field measurements for CO concentrations, meteorology, and traffic data took place on 21st of June, 19th of July, 2015, and 15th of January, 2016. All the environment-related instruments were professionally calibrated by the Shanghai Environmental Monitoring Center (SEMC) before and after the experiments. Monitoring periods lasted from 1:00 p.m. to 3:00 p.m., during which traffic flow had a relatively steady composition and operation pattern. The traffic flow pattern during the monitoring periods was steady-flow type, and the dispersion processes of gaseous traffic emissions were less affected by vehicle-induced turbulence (Thaker and Gokhale, 2016). 2.3.1. Traffic data Traffic volume and vehicle categories were manually recorded during the monitoring periods. 100 samples of passenger car speeds were recorded using a portable radar speed detector (J2358-RADAR GUN). The traffic related data were collected to estimate a mean emission rate for CO inlets in numerical models. 2.3.2. CO sampling Electrochemical monitors (Langan Model T15) were used to measure CO concentrations, and were mounted on tripods at 1.4 m and 1.6 m above road surface. According to the results of previous studies focusing on similar street canyons (WIF, 0.3 < AR < 0.67) with the presence of trees, concentrations of gaseous trafficemitted pollutants on the leeward side are apparently larger than the windward side (Buccolieri et al., 2009; Abhijith and Gokhale (2015)). Thus, CO monitors were located on the southeast side (leeward) of this road section considering local wind directions. As illustrated in Fig. 2, four positions denoted by A, B, C, and D (12.8 m, 15.6 m, 19.7 m, and 20.45 m on the horizontal axis, respectively) were selected to monitor roadside CO concentrations over the slow lanes. Positions A and B were located on the leeward footpath, C was located on the leeward bikeway, and D was located above the top surface of leeward vegetation barrier. Additional observations were conducted on 15th of January, 2016, to eliminate the potential impacts of Humin road on the CO concentrations measured on Dongchuan Road. As shown in Fig. 1(a), five locations marked by a, b, c, d, and e (representing 70 m, 80 m, 90 m, 100 m, and 110 m to Humin road, respectively, and locating at position A shown in Fig. 2) were selected to monitor variations of CO concentrations along the experimental road section at a measurement height of 1.6 m. Locations a and b were measured for a vegetation-free case (no vegetation barriers exist), and the remaining three locations were measured for a vegetation-presence case. Urban background CO concentrations during the monitoring periods were measured in Minhang campus of Shanghai Jiao Tong University, where CO concentrations were less affected by urban traffics. 2.3.3. Meteorological data Wind anemometers (Model FS-04) were used to record wind directions and velocities in Minhang campus of Shanghai Jiao Tong University at a similar height of 15 m, atop the 5-story building at the experimental site. Mean wind velocity was about 2 m s−1 , and wind directions were approximately normal to vegetation barriers

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Table 1 Summary of key parameters used in field measurements and numerical simulations. Type

Name

Value

Street canyon

Configuration

Aspect ratio

Meteorology

Wind

Velocity

Vegetation

Temperature Tree

Direction

Vegetation barrier

Pollutant(CO)

Emission source Background

a

Height Canopy diameter Height Width Velocity Mass Concentration

0.39 0.18, 0.78 2 1, 3, 20 90 35 6 5 0, 0.5, 1.1, 1.5, 2.0, 2.5, 3,4 0.9 1.5 1e−5 (12.5) 0.84

Unit

m s−1 ◦ ◦

C m m m m m m s−1 (mg s−1 ) ppm

NSa or FMa FM+NS NS FM+NS NS NS+FM NS+FM NS+FM NS+FM NS FM+NS NS+FM NS NS FM

NS is short for numerical simulation, and FM is short for field measurement.

during the monitoring periods. This allows for less complex simulations in numerical models. Temperature and relative humidity (RH) were recorded by a HOBO data logger U12-011 which was mounted on a tripod located on the leeward side. Mean temperature was 35 ◦ C, and mean RH was 60% during the monitoring periods. 3. Results and discussion 3.1. Field measurements As described above, five locations (as illustrated in Fig. 1(a) and described in Section 2.3.2) were selected to measure variations of CO concentrations along the experimental road section. As shown in Fig. 3, CO concentrations do not have a large variation between locations a and b, and an absolute difference of hourly mean CO concentrations between these two locations is 0.01 ppm which accounts for approximately 0.7% of the hourly mean CO concentration. The standard deviation of hourly mean CO concentrations among locations c, d, and e is 0.005 ppm which accounts for approximately 0.4% of the hourly mean CO concentration. Thus, the amount of CO originating from Humin Road is relatively minor, and a contribution of CO from Humin Road to the experimental site can be considered negligible in this study. Additionally, to obtain pure traffic-emitted CO concentrations on the experimental site, mean background CO concentration of 0.84 ppm is removed from direct roadside measurements. Fig. 4(a) displays distributions of the measured CO concentrations at a measurement height of 1.4 m on the leeward side, and it clearly demonstrates that mean CO concentrations of vegetationfree case over the slow lanes (except for position D, which is not over the slow lanes, see Fig. 2) are obviously higher than vegetation-presence case. However, a sharp decline of the curve occurs between positions C and D in the vegetation-free case, which is different from vegetation-presence case. It would be caused by the presence of vegetation barriers which elevates on-road CO concentrations in the vegetation-free case, and this result is consistent with previous studies (Hagler et al., 2011; Tong et al., 2016). Fig. 4(b) presents distributions of CO concentrations measured at a height of 1.6 m over the leeward slow lanes, and the curves of mean CO concentration are similar to 1.4 m in both cases. That is, mean CO concentrations of vegetation-free case measured at 1.6 m are also obviously higher than vegetation-presence case. Consequently, it can be concluded that CO concentrations over the leeward slow lanes are generally decreased due to the presence of vegetation barriers, compared with vegetation-free case illustrated in Fig. 4. In the vegetation-presence case, reduction percentages of mean CO concentrations at each monitoring position are calculated in comparison with the vegetation-free case (see Fig. 5). Mean CO con-

centrations (at positions A, B, and C) are reduced by approximately 53%, 32%, and 27% at 1.4 m, and reduced by approximately 36%, 23%, and 24% at 1.6m. These results indicate that a positive impact of vegetation barriers on the alleviation of roadside air pollution becomes more evident when staying further from the roadway in the given street canyon. Therefore, field observations confirm a conclusion that vegetation barriers flanking a roadway have a potential to improve roadside air quality over the leeward slow lanes in an urban street canyon. 3.2. Evaluation of the numerical model with field data Results obtained from numerical simulations are evaluated by field observations for effectiveness of parameters used in numerical models. An agreement of distribution patterns of roadside CO concentrations between numerical simulations and field observations is highlighted. Comparisons of specific CO concentrations at measurement positions between numerical simulations and field observations are not performed due to a rough estimation of CO emission rate in numerical models. As shown in Fig. 6, distribution curves of roadside CO concentrations (at positions A, B, and C) obtained from numerical simulations shows a relatively good agreement with field observations. In numerical models with the presence of vegetation barriers, roadside CO concentrations demonstrate an increasing trend when approaching the roadway, and are always lower than that of the vegetation-free case. These simulation results are also consistent with field observations. Generally, results obtained from numerical simulations have a relatively good agreement with field observations in terms of dispersion patterns. 3.3. Numerical results analysis The numerical models, which are verified by field observations, can be used to explore the reasons that lead to a concentration decline of gaseous traffic-emitted pollutants over the slow lanes of urban street canyons. The optimal heights for vegetation barriers can also be investigated in different types of street canyons and wind conditions. What should be clearly stated here is that the optimal heights will vary depending upon specific site factors, but modeling based on these specific conditions could help to identify the optimum configuration for roadside vegetation barriers. 3.3.1. Effects of vegetation barrier As discussed above, CO concentrations of vegetation-free case over the leeward slow lanes are generally larger than vegetationpresence case (see Fig. 7(a)). Potential factors which make this happen are very complicated in the real world, and we mainly focus

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Fig. 3. Distributions of CO concentrations measured at each location on 15th of January, 2016. Hollow circles represent hourly mean CO concentrations.

Fig. 4. Distributions of CO concentrations measured at each position on 19th of July, 2015. (a) At 1.4 m; (b) At 1.6 m. Hollow circles represent mean CO concentrations.

Fig. 5. Reduction percentages of mean CO concentrations with the presence of vegetation barriers, MH means measurement height.

on alterations of flow structures and related parameters resulted from the presence of vegetation barriers in this study. Vegetation barriers can reflect and deflect approaching air flows, which lead to occurrences of more complicated vortices in urban street canyons. As concluded by previous studies, these vortices can enhance the recirculation of pollutants inside a street canyon (Nazridoust and Ahmadi, 2006; Wang and McNamara, 2007). In the vegetation-free case illustrated in Fig. 7-1(c), vortex 1 is compressed and limited to the leeward side over slow lanes. What’s more, vortex 1 (anticlockwise) and vortex 2 (clockwise) have different recirculation directions and intersect in the leeward side, which makes vortex 1 has a potential to trap gaseous traffic pollutants over the leeward

slow lanes. That is, CO emissions are transported to the leeward side by vortex 2, and then some of them are trapped by vortex 1 near ground surface. While in the vegetation-presence case, vortex 1 is further extended to the roadway, and a small vortex (vortex 2) is formed over the leeward slow lanes. In this condition, part of CO emissions transported by vortex 1 are recirculated to the roadway, and then removed from the street canyon by vortex 3 (Fig. 7-2(c)). This leads to a decline of CO concentrations over the leeward slow lanes. Therefore, it can be concluded that scales, positions, and structures of vortices have essential impacts on roadside distribution of traffic-emitted CO in urban street canyons.

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Fig. 6. Variations of mean roadside CO concentrations obtained from field observations and numerical simulations at 1.6 m. Solid lines represent field observations and dashed lines represent numerical simulations. MNV represents the vegetation-free case, MV represents the vegetation-presence case, and vh is short for vegetation height.

Fig. 7(b) displays vertical velocities of mixed air flows in the given street canyon. In the vegetation-free case, vertical flow velocities near the leeward tree crown are evidently larger than vegetation-presence case. This finding indicates that most of the CO emissions are vertically transported in the leeward side, which increases the possibility for vortex 1 to recirculate CO emissions in the given street canyon. By contrast, an area characterized by upward velocity of larger than 0.1 (m s−1 ) is observed over the roadway in the vegetation-presence case, and this indicates more CO emissions have been removed from the street canyon before arriving the leeward slow lanes. In combination with vertical velocity and flow structure, it can be primarily explained that the presence of vegetation barriers decreases CO concentrations on the leeward side of urban street canyons. Fig. 7(d) shows spatial distributions of Turbulent Kinetic Energy (TKE) inside the street canyon. In the vegetation-free case, an area characterized by TKE of less than 0.05 (m−2 s−2 ) on the leeward side is smaller than vegetation-presence case. This indicates that the presence of vegetation barriers has a potential to decrease TKE on the leeward side, which is unfavorable for the dilution of in-canyon pollutants. Therefore, the presence of vegetation barriers deteriorates in-canyon ventilation conditions from a perspective of TKE. In both cases, lower TKE is observed in the leeward side, which may elevate CO concentrations over the slow lanes compared with the windward side (see Fig. 8). This result is also consistent with previous studies (Hagler et al., 2011; Tong et al., 2016). Nevertheless, vegetation barriers are favorable for improving roadside air quality when the combined effects on vertical velocity and flow structure in urban street canyons are considered. 3.3.2. Effects of vegetation barrier height Based on validated numerical models, the impacts of vegetation barrier height on roadside gaseous pollutant concentrations are explored in this subsection. In perpendicular wind conditions, vegetation barrier heights ranging from 0 m to 4 m are used in numerical simulations. Fig. 9 presents variations of CO concentrations along the road cross section in the cases with vegetation barrier heights ranging from 0 m to 4.0 m in the given street canyon. It illustrates that CO concentrations of vegetation-presence case on both sides of the street are generally lower than vegetation-free case. In the cases with vegetation barrier heights of 0 m to 1.5 m, CO concentrations over the leeward slow lanes demonstrate an increasing trend when approaching the roadway (see Fig. 9(a)). By contrast, CO concentrations on the windward side demonstrate a

different distribution pattern from vegetation-free case. CO concentration increases to a peak value approximately at × = 44 m, and then demonstrates a declining trend when approaching the roadway. While in the cases with vegetation barrier heights exceeding 1.6 m, CO curves on the leeward side are different from those cases with vegetation barrier heights of less than 1.6 m (see Fig. 9(b)). That is, CO concentration primarily declines to a minimum value approximately at × = 15 m, and then demonstrates an increasing trend when approaching the roadway. For CO curves on the windward side, they are consistent with those cases which have vegetation barrier heights of less than 1.6 m. Thus, it can be concluded that not only roadside CO concentrations but also CO distribution patterns are affected by an alteration of vegetation barrier height. As discussed in Section 3.3.1, different distribution patterns of CO in the cases, with vegetation barrier heights exceeding 1.6 m (tall) and less than 1.6 m (low), resulted from alterations of in-canyon air flow structures (see Fig. 10). As illustrated in Fig. 10(b), the lower boundary of vortex 2 is not in contact with the roadway when vegetation barrier height is 1.1 m. The area occupied by large upward velocities (red color) is extended closer to the windward side. This means that more CO can be vented out of the street canyon before being trapped on the leeward side. In the cases with vegetation barriers exceeding 1.6 m, another anti-clockwise vortex (see Fig. 10(c) and (d)) is developed between vortex 2 and roadway surface. As illustrated in Fig. 9, in the cases with tall vegetation barriers, CO concentrations (lower than 0.14 ppm) over the leeward slow lanes are generally lower than that of the cases (higher than 0.14 ppm) with low vegetation barriers. Thus, it can be interpreted that the presence of this anti-clockwise vortex can enhance dilution processes of incanyon pollutants, during which CO mass is removed from the street canyon. Additionally, the direction of air flows at the leeward side gradually switches from downward to upward in the cases with an increased vegetation barrier height, which also help to dilute in-canyon air pollutants. What’s more, tall vegetation barriers can prohibit CO from reaching the slow lanes directly. However, roadside CO concentrations are not consistently reduced in relation to an increased vegetation barrier height. As evident in Fig. 9(a), CO concentrations over the leeward slow lanes, with a vegetation barrier height of 0.5 m, are approximately equal to those of the case with a vegetation barrier height of 0.9 m. But large concentration differences are observed over the windward slow lanes. A same phenomenon can also be observed in the cases with vegetation barrier heights of 1.1 m and 1.5 m. In the cases with vegetation barrier heights of 2.5 m and 3.0 m, CO con-

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Fig. 7. View of flow characteristics in the given street canyon reproduced by numerical simulations with the presence of vegetation barriers (the left column, 1), and the absence of vegetation barriers (the right column, 2). (a) CO concentration, (b) vertical wind velocity, negative values mean downward velocity and positive values mean upward velocity, (c) streamlines, (d) Turbulent Kinetic Energy (TKE), vh is short for vegetation height, vh=0 m represents vegetation-free case, and vh=0.9 m represents vegetation-presence case.

centrations on both sides of the street are always larger than those with a vegetation barrier height of 2.0 m. These results reveal that effectiveness of vegetation barriers on the mitigation of roadside air pollution is not always proportional to an increased vegetation barrier height. That is, the higher vegetation barriers do not always work better than the lower ones. Therefore, the optimal vegetation barrier heights should be found out to effectively improve roadside air quality in urban street canyons. To comprehensively evaluate the effectiveness of vegetation barrier height, an evaluation indicator denoted by am is proposed in this study. am is the mean of averaged CO concentrations on both sides of slow lanes in urban street canyons.

⎡ ⎤ n1 n2   am = ⎣ Ci /n1 + Cj /n2 ⎦ /2 i=1

j=1

(5)

where Ci is CO concentration at the ith sampling position and n1 is number of samples on the leeward side. Cj is CO concentration at the jth sampling point, and n2 is number of samples on the windward side. Fig. 11 displays a variation of am in the cases with different vegetation barrier heights in the given street canyon (AR is 0.4) and wind condition (2 m s−1 ). In the cases with low vegetation barriers (less than 1.6 m) illustrated in Fig. 11, values of am suggest that 1.1 m can be considered an optimal height. With this vegetation barrier height, mean CO concentration over the slow lanes is reduced by approximately 26% in contrast to the vegetation-free case. Values of am also indicate that 2.0 m can also be an optimal height if tall vegetation barriers are necessary. The optimal heights for vegetation barriers are obtained with a fixed wind velocity and AR. Therefore, the optimal heights should also be evaluated in conditions with different wind velocities and ARs.

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Fig. 8. Variations of CO concentrations along the road cross section at 1.6 m, and vh is short for vegetation height.

Fig. 9. Variations of CO concentrations along the road cross section in the cases with different vegetation barrier heights: (a) 0 m to 1.5 m, (b) 2.0 m to 4.0 m, and vh is short for vegetation height.

3.3.3. Effects of wind velocity Given the prescribed urban street canyon, the optimal heights for vegetation barriers should not be susceptible to wind velocities within a certain range. Therefore, we attempt to change incident wind velocities to investigate the susceptibility of optimal heights to wind velocities (as presented in Table 1). Moreover, 1 m s−1 and

3 m s−1 (low wind velocities) are used based on field observations, and 20 m s−1 (wind scale 8) is used as a large wind velocity which occurs in summertime. The evaluation indicator am is used to evaluate the effects of wind velocities on roadside CO concentrations, as shown in Fig. 12.

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Fig. 10. Views of flow structure in given street canyons, reproduced by numerical simulations, with different vegetation barrier heights. Dark lines represent streamlines, and colors denote magnitude of vertical flow velocities (m s−1 ). Negative values represent downwind velocities, positive values represent upward velocities, and vh is short for vegetation height.

Fig. 11. The variation of evaluation indicator ␣m in the cases with different vegetation barrier heights when wind is 2 m s−1 , vh is short for vegetation height, and the measurement height is 1.6 m.

Fig. 12. The variation of evaluation indicator ␣m in the cases with different vegetation barrier heights in different wind conditions, and vh is short for vegetation height.

Values of am in the cases with different vegetation barrier heights have relatively minor differences when the incident wind

velocity is low. The distribution curve of am for the case with an incident wind velocity of 20 m s−1 is similar to the cases with low

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Fig. 13. The variation of evaluation indicator ␣m for streets with different vegetation barrier heights and ARs in different wind conditions, (a) AR is 0.18, (b) AR is 0.78, and vh is short for vegetation height.

incident wind velocities. It reveals that the optimal heights for vegetation barriers are also not sensitive to incident wind velocities within a certain range. Consequently, 1.1 m can be considered as an optimal height for low vegetation barriers, and 2.0 m can be considered as an optimal height for tall vegetation barriers in the given street canyon.

3.3.4. Effects of street configuration According to the classification of urban street canyons by Li et al. (2009), ARs of 0.18 and 0.78 are used to further investigate the effects of street configuration on determination of the optimal heights. An AR of 0.18 is used to represent a street canyon characterized by the isolated roughness flow regime (IRF, AR < 0.3), and an AR of 0.78 is used to represent a street canyon characterized by the skimming flow regime (SF, 0.67 < AR < 1.67). Fig. 13 shows variations of am for these two types of streets in different wind conditions. Streets with ARs of larger than 1.67 are not considered in this study. As illustrated in Fig. 13, curves of indicator am are similar in different wind conditions for these two types of streets. It reveals that the optimal heights of vegetation barriers are also not sensitive to incident wind velocities in urban street canyons with an AR of less than 1.67. For a street canyon with an AR of 0.18, values of am have minor differences in those cases with vegetation barrier

height ranging from 0.9 m to 2.5 m. Therefore, a range of 0.9 m to 1.6 m can be used as the optimal heights for low vegetation barriers. Additionally, a range of 2.0 m to 2.5 m can be used as the optimal heights for tall vegetation barriers, if necessary. For street canyons with an AR of 0.78, curves of indicator am are similar to the street with an AR of 0.4. Therefore, 1.1 m and 2.0 m can also be used as the optimal vegetation barrier heights in these types of street canyons. As these results indicated, 1.1 m can be used as an optimal height for low vegetation barriers, and 2.0 m for tall vegetation barriers in all of these three street types. Consequently, it can be concluded that the optimal vegetation barrier heights are also largely insensitive to street configuration.

4. Conclusions The impacts of vegetation barriers on the mitigation of roadside air pollution in urban street canyons are investigated in this study, and the numerical simulations are validated with field observations. Based on the validation, three key conclusions can be reached. First, results obtained from numerical simulations show a good agreement with field observations in perpendicular wind conditions. Therefore, the standard k-␧ turbulent model and solidification processing for vegetation barriers in numerical models are suiTable Second, we validate the effectiveness of vegetation barri-

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ers on the improvement of roadside air quality, with a significant decline of gaseous traffic-emitted pollutants over the slow lanes of given street canyons. Finally, the effects of vegetation barrier height, wind velocity, and street configuration on determination of the optimal vegetation barrier heights are also evaluated. Results reveal that 1.1 m can be used as an optimal height for low vegetation barriers, and 2.0 m for tall barriers (if needed) on urban streets with ARs of less than 1.67. In addition to 1.1 m, a range of 0.9 m to 2.5 m can be considered as the optimal heights for vegetation barriers in a street canyon characterized by the IRF. These results also demonstrate that the optimal heights for vegetation barriers are largely insensitive to perpendicular wind velocities within an acceptable range. Although we have highlighted some key findings, this study still has its limitations. For instance, traffic-induced turbulence is not clearly considered in numerical models due to a complicated composition of traffic flow. Consequently, results obtained from numerical simulations may not be generalizable for the more complicated street canyons. Nevertheless, the key findings presented in this study can provide recommendations and insights for urban planners when designing vegetation barriers for urban streets. Acknowledgements This study is supported by the Shanghai Environmental Protection Bureau (No. 2014-8) and the State Key Laboratory of Ocean Engineering of China (GKZD010059) at Shanghai Jiao Tong University. We wish to acknowledge the supports of the Shanghai Environmental Monitoring Center for assistance in instrumental calibration processes. We also appreciate two anonymous reviewers’ insightful suggestions on our work. Finally, we would like to thank graduate students Qiuyue Miao, Bai Li, and Yizhe Huang from the center for ITS and UAV Application Research at Shanghai Jiao Tong University for their help in data collection. References Abhijith, K.V., Gokhale, S., 2015. Passive control potentials of trees and on-street parked cars in reduction of air pollution exposure in urban street canyons. Environ. Pollut. 204, 99–108. Amorim, J.H., Rodrigues, V., Tavares, R., Valente, J., Borrego, C., 2013. CFD modelling of the aerodynamic effect of trees on urban air pollution dispersion. Sci. Total Environ. 461, 541–551. Balter, J., Ganem, C., Discoli, C., 2016. On high-rise residential buildings in an oasis-city: thermal and energy assessment of different envelope materiality above and below tree canopy. Energy Build. 113, 61–73. Bowker, G.E., Baldauf, R., Isakov, V., Khlystov, A., Petersen, W., 2007. The effects of roadside structures on the transport and dispersion of ultrafine particles from highways. Atmos. Environ. 41 (37), 8128–8139. Brücher, W., Kessler, C., Kerschgens, M.J., Ebel, A., 2000. Simulation of traffic-induced air pollution on regional to local scales. Atmos. Environ. 34 (27), 4675–4681. Buccolieri, R., Gromke, C., Di Sabatino, S., Ruck, B., 2009. Aerodynamic effects of trees on pollutant concentration in street canyons. Sci. Total Environ. 407 (19), 5247–5256. Cao, J.J., Zhu, C.S., Tie, X.X., Geng, F.H., Xu, H.M., Ho, S.S.H., Ho, 2012. Characteristics and sources of carbonaceous aerosols from Shanghai, China. Atmos. Chem. Phys. Discuss. 12, 16811–16849. Franke, J., Hellsten, A., Schlünzen, H., Carissimo, B., 2007. Best Practice Guideline for the Cfd Simulation of Flows in the Urban Environment. Cost Action 732. Quality Assurance and Improvement of Microscale Meteorological Models, Hamburg, Germany. Gallagher, J., Gill, L.W., McNabola, A., 2011. Optimizing the use of on-street car parking system as a passive control of air pollution exposure in street canyons by large eddy simulation. Atmos. Environ. 45 (9), 1684–1694. Gallagher, J., Gill, L.W., McNabola, A., 2012. Numerical modelling of the passive control of air pollution in asymmetrical urban street canyons using refined mesh discretization schemes. Build. Environ. 56, 232–240. Gallagher, J., Gill, L.W., McNabola, A., 2013. The passive control of air pollution exposure in Dublin, Ireland: a combined measurement and modelling case study. Sci. Total Environ. 458, 331–343.

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