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Article

Evaluation of Vegetation Configuration Models for Managing Particulate Matter along the Urban Street Environment

by
Na-Ra Jeong
*,
Seung-Won Han
and
Jeong-Hee Kim
Urban Agriculture Research Division, National Institute of Horticultural and Herbal Science, Wanju-gun 55365, Korea
*
Author to whom correspondence should be addressed.
Forests 2022, 13(1), 46; https://doi.org/10.3390/f13010046
Submission received: 26 October 2021 / Revised: 13 December 2021 / Accepted: 21 December 2021 / Published: 2 January 2022
(This article belongs to the Section Urban Forestry)
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Abstract

:
As a green infrastructure component, urban street vegetation is increasingly being utilized to mitigate air pollution, control microclimates, and provide aesthetic and ecological benefits. This study investigated the effect of vegetation configurations on particulate matter (PM) flows for pedestrians in road traffic environments via a computation fluid dynamics analysis based on the road width (four and eight-lane) and vegetation configuration (single-, multi-layer planting, and vegetation barrier). Airflow changes due to vegetation influenced PM inflow into the sidewalk. Vegetation between roadways and sidewalks were effective at reducing PM concentrations. Compared to single-layer planting (trees only), planting structures capable of separating sidewalk and roadway airflows, such as a multi-layer planting vegetation barrier (trees and shrubs), were more effective at minimizing PM on the sidewalk; for wider roads, a multi-layer structure was the most effective. Furthermore, along a four-lane road, the appropriate vegetation volume and width for reducing PM based on the breathing height (1.5 m) were 0.6 m3 and 0.4 m, respectively. The appropriate vegetation volume and width around eight-lane roads, were 1.2–1.4 m3 and 0.8–0.93 m, respectively. The results of this study can provide appropriate standards for street vegetation design to reduce PM concentrations along sidewalks.

1. Introduction

Road traffic emissions containing particulate matter (PM) and gaseous pollutants, such as carbon monoxide (CO) and nitrogen oxides (NOx), are a primary source of air pollution [1,2,3,4]. The amount of PM inhaled by pedestrians is greater than that inhaled by people that use public transportation or automobiles [2]. As pollutant exposure can have severe negative impacts on human health, taking appropriate mitigation measures is crucial [5].
Reducing emissions is a direct method of reducing exposure; however, managing air quality exclusively through emission control strategies is difficult. To adequately address air quality, McNabola et al. [6] proposed three methods: (i) managing pollutant quantities, (ii) controlling emissions intensity, and (iii) regulating source–receptor pathways. Vegetation on urban streets is a passive control mechanism that can extend the entire distance of pollutant sources and receptors, thereby enhancing dilution, increasing dispersion, and reducing the concentrations of pollutants [7,8,9,10]. Research has demonstrated that vegetation impacts local air quality through aerodynamic and deposition pathways [11]. For example, pollutants cannot freely move toward a sidewalk when vegetation blocks airflow; a portion of the pollutants passing through the vegetation is deposited and filtered [12,13,14]. While previous studies have primarily considered the aesthetic features of urban vegetation, recent studies have highlighted the role of vegetation in improving urban microclimates [15,16,17,18,19]. Some research has even revealed the potential for vegetation to cause localized increases in air pollutant concentrations [11,17,20]. The pollutant concentrations behind vegetation or physical barriers can be mitigated through deposition and vertical mixing; reducing convection and turbulence via the windbreak effect improves the air quality behind roadside vegetation significantly [12,14,21,22,23]. Urban vegetation can positively affect air quality, microclimate control, carbon fixation, rainwater drainage, and noise pollution [24,25,26,27,28], and promotes biodiversity by providing food sources, habitats, and landscape connectivity for urban fauna [29,30,31]. Green spaces in urban environments connect community dwellers, encourage physical activity, reduce stress, and are excellent recreational spaces [32,33].
Previous studies have examined the factors affecting air pollutant distributions along urban streets through wind tunnel tests, numerical simulations, or combinations of experimental methods. In particular, models based on computation fluid dynamics (CFD) have effectively explained the influence of the ratio of the street width to building height, vegetation characteristics, noise barriers or boundary walls, and individual buildings on the flow and diffusion of air pollutants [1,34]. Studies on the effects of vegetation on pollutant concentrations have revealed a relationship among the tree crown size, porosity, leaf area density (LAD), tree height, and planting interval [35,36,37,38,39,40,41,42,43,44]. Research on reducing airborne dust through the adsorption effect of vegetation has revealed that the deposition of PM on the surface of plants is influenced by various environmental factors, such as humidity and air velocity, as well as the size, shape, and physical properties of PM [45]. McNabola [46] reported that low boundary walls, noise barriers, street-parked cars, and vegetation could reduce the concentration of pollutants along urban streets by manipulating the natural airflow in the urban environment. The potential of noise barriers is affected by their size and shape, wind direction, and turbulence; in particular, the barrier height can influence pollutant concentrations at sensors near the barrier [47]. Studies have also identified that solid barriers and low boundary walls have similar effects to those exhibited by vegetation [48,49].
Slinn [50] presented a seminal theoretical equation for the rate of pollutant deposition on the surface of plants according to particle size. For particle diameters (d) > 10 µm, sedimentation by gravity is an important adsorption mechanism. For 1 µm < d < 10 µm, the effect of sedimentation decreases with size; for 0.1 µm < d < 1 µm, impaction and interception are important deposition mechanisms. Fowler et al. [51] and Gallagher et al. [52] verified the equation of Slinn [50] in the range of 0.1 µm < d < 1 µm, subsequently presenting a modified empirical equation. They explained that this revision is necessary because of the phoretic processes occurring along an electric potential gradient (electrophoresis) or a thermal gradient (thermophoresis). Based on the plant’s shape, the wind speed and airflow, also, significantly affect the deposition of PM on plants. For a wind speed range of 1–3 m·s–1, the deposition velocity is 1–10 cm·s–1 according to the leaf type; research has shown that whitebeam and maple are especially advantageous for deposition [53]. When deposition rates reach their maximum value (10 cm·s–1), ~10% of traffic emissions can be eliminated from urban arterial roads; however, these results vary significantly depending on the shapes of urban buildings and roads.
To improve the urban air environment, researchers have investigated the effect of near-street vegetation as a passive strategy to minimize the impact of pollutants on pedestrians. However, most of the resulting models have only addressed aerodynamic effects without considering deposition; as a result, the level of pollution can be overestimated [5,45]. In this study, numerical simulations of the road width and vegetation configuration were conducted to quantitatively analyze the flow characteristics of PM by considering both aerodynamic and deposition effects with the following objectives: (1) evaluate the movement characteristics of road pollutants under various street vegetation configurations and road widths; (2) compare PM concentrations at breathing heights on the sidewalk according to the vegetation configuration and road width; (3) identify the optimal composition and size of vegetation to maximize the PM reduction effect. This study provides insights for urban planners and landscape architects who manage street greenery to minimize the negative effects of PM on pedestrians. The remainder of this paper is organized as follows: Section 2 describes the materials and methods used, Section 3 presents and discusses the results, and Section 4 presents concluding remarks.

2. Materials and Methods

2.1. Vegetation Configuration

The types of vegetation configurations were set according to tree–shrub arrangement, tree rows, and roadway widths. The roadway width was set to four and eight two-way lanes, where the widths of one, four, and eight lanes were 3.5, 14, and 28 m, respectively. Vegetation configurations were categorized as single-layer planting with only trees, multi-layer planting with both trees and shrubs, and vegetation barriers. Tree arrangement consisted of one and two rows of planting, with the latter applied only to the eight-lane roadway (Table 1). For the four-lane roadway, four scenarios were planned: a single-layer planting (F1), two multi-layer configurations with shrub heights of 50 cm and 1 m, (F2 and F3, respectively), and a vegetation barrier (F4). Six scenarios were configured for the eight-lane roadway: single-layer planting (E1), multi-layer planting with a shrub height of 1 m (E2), two-row single-layer planting (E3), two-row multi-layer planting (E4), multi-layer planting with a stepped shrub height (E5), and a vegetation barrier (E6).

2.2. Computational Fluid Dynamics

2.2.1. Mathematical Formulations in a Numerical Model

To calculate the flow of PM generated along the road, the airflow was modeled using ANSYS Fluent v. 19.2. The finite volume method was employed, and the standard default model was used to calculate turbulence. A discrete phase model was used to calculate particle motion using the Lagrangian method [54] to track airborne particulates or those accumulated along the ground. Airflow was regarded as a continuum for which the Navier–Stokes equations were deciphered, while the discrete phases were solved by tracking particulates, droplets, or bubbles [55]. The exchange of momentum, mass, and energy occurred between the continuum and discrete phases, where the interaction among particles could be included or excluded. As PM contains little mass and its influence on airflow is limited [54], we assumed that the interaction force between the particles was negligible. Under this assumption, a one-way calculation was performed where only flow impacted the particulates, greatly simplifying the approach and reducing the computation time.
In the discrete phase model, the trajectory of a particle can be predicted by integrating the acting force in the Lagrangian reference frame. This is the same as the inertial force acting on the particle, which can be expressed according to Equations (1)–(3):
∂ u p ∂ t = F D ( u − u p ) + g x ( ρ p − ρ ) ρ p + F x ,
F D ( u − u p ) = 18 μ ρ p   d p 2   C D R e 24 ,
R e ≡ ρ d p | u p − u | μ
where g x is the gravity acceleration, F x ′ is an additional acceleration term (force·unit–1 particle mass), F D ( u − u p ) is the drag force per unit mass of the particle, u ,   u p ,   μ ,   ρ ,   ρ p ,   and   d p are the fluid phase velocity, particle velocity, fluid dynamic viscosity, fluid density, particle density, and particle diameter, respectively, and R e and C D are the relative Reynolds number and drag coefficient, respectively [56,57]. The behavior of submicron particles was mainly influenced by intra-system flow; hence, particle rotation and Brownian forces had relatively little influence and were, thus, excluded from the analysis.

2.2.2. Geometry and Boundary Conditions

The cross-section of the model was simulated with sidewalks and buildings facing sidewalks on both sides of the roadway. Actual building heights vary along any given street; however, in the numerical simulations here, uniform heights were considered for simplicity [58]. The building height was 15 m [3], and the surface of the building was set to the no-slip wall boundary condition; in this approach, the no-slip boundary condition assumes that the speed of the fluid layer in direct contact with the boundary is identical to the velocity of this boundary [59]. The flow of air develops as it passes buildings; to account for this effect, the distance between the inlet boundary and road was set to 30 m and that between the outlet boundary and road was 10 m. The inlet velocity of 3 m·s–1 was set as the inlet boundary condition. The pressure of the outlet boundary was set to zero to provide a better convergence rate (Figure 1a). The lane width was set to 3.5 m, and the sidewalk height and width were 0.3 and 7 m, respectively. The passage of vehicles was assumed to generate PM, and particle inlets were configured in the road center. There were two symmetrical particle inlets on the road, with a height × width of 0.3 × 0.3 m (Figure 1b).
Roadside plants (trees and shrubs) consist of irregular small leaves and branches that interfere with airflow. As such, a complex plant canopy is difficult to emulate in explicit numerical modeling; therefore, the average canopy flow speed and turbulence statistics were spatially averaged [60]. Canopies were represented as regions of fluid without branches and leaves [56]; however, the branches, leaves, and stems of trees play an important role in removing pollutants and airborne particulates [61]. Accordingly, the adsorption of PM on the surface of plant leaves and stems was considered [3], assuming that a certain percentage of particulates in contact with the surface was deposited according to Equation (4):
W = ( C 0 − C x ) C 0 × 100 ,
where W is the depletion rate of PM, C 0 is the mean concentration of PM in the control, and C x is the mean concentration of PM on the plant. Previous studies [3] have suggested that W = 0.1–0.3 is based on the location of the sidewalk, street trees, vegetation configurations, and planting heights. Adopting similar values to those used in previous research, W was calculated as 0.1 using the employed model; in other words, this study assumed that 10% of the PM particles in contact with the vegetation surface were deposited.
In general, tree canopies are portrayed by cubes or spherical shapes in CFD models [5,16,20,62,63,64], while tree trunks are given a small volume with a negligible effect on airflow [65]; thus, only canopies were modeled [5]. Additionally, the crowns of deciduous broadleaf trees, primarily used as street trees, are spherical. Therefore, only spherical crowns were modeled in this study. The general size of the Zelkova serrata, commonly planted as a street tree in Korea, was assumed. The spherically modeled trees had a diameter and height of 5 and 7 m, respectively; the height from the ground to the first branch was 2 m and the width of the crown was 5 m. Shrubs were modeled as squares, such as pruned Buxus microphylla var. koreana NAKAI, with a width of 1 m and height of either 0.5 or 1 m, depending on the simulated vegetation configuration.

2.2.3. Grid Model and Configuration

A surface and spatial grid were constructed to investigate the behavior of PM (Figure 2). A denser grid size was set in the region of interest to enhance the analysis accuracy and minimize the time and computational resources required. The particle inlet and walls formed a dense grid of 0.15 m. All regions were applied as a tetrahedral grid model and then converted to polyhedrons for better analysis outcome. The discrete phase model (DPM) for tracking particulates first calculated the continuum (air), followed by the particle behavior based on this value. Accordingly, the continuum period value must be defined. In this study, the continuum flow was calculated until convergence, after which the results of five repetitions per time interval were applied to the DPM.

2.2.4. PM Model Configuration

PM with diameters between 1 and 100 μm was analyzed. PM from the road is generated by tire friction and fuel combustion; therefore, its primary component was assumed to be carbon-based; the PM density was set to 1650 kg·m–3 based on Jeong [54] (Table 2). Distribution data were applied to the Rosin–Rammer equation to easily define the particle size distribution.
In this approach, the complete range of particle sizes was divided into a set of discrete size ranges, where each was defined by a single stream that was part of the group. The Rosin–Rammer distribution function assumed an exponential relationship between particle diameters according to Equation (5) [66]:
F ( d ) = { 1 2 π ∫ 0 d 1 σ d ′ e x p ( − l n ( d ′ / d m ) 2 2 σ 2 )   d d ′ ( d ≤ d m ) 1 − e x p ( − 0.693 ( d / d m ) γ ) ( d m < d ) ,  
where d is the particle diameter, σ is the geometric standard deviation, γ is the spread parameter of the distribution, and d m is the median diameter [67].

3. Results

3.1. Particulate Matter Flow Analysis

3.1.1. Comparing Vegetation Configurations

In this study, the entire flow was in contact with the windward wall and entered the roadway through the vegetation at the lower part of the roadside. The flow entering the roadway was significantly affected by the vegetation type; when blocked, the PM generated in the roadway was less likely to move into the sidewalk.
In the F1 model, where only trees were planted on a four-lane road, the flow encountering the leeward (right) wall passed between the crown and ground, thereby increasing the velocity (Figure 3a). A clockwise vortex was formed, which moved the PM from the discharge area (close to the ground) to the windward (left) side. Hence, the PM concentration along the leeward sidewalk may have differed from the overall concentration of the entire atmosphere. In the F4 model, where the sidewalk and roadway were separated by a barrier, a primary vortex formed on the windward sidewalk, a secondary vortex formed in the center of the road, and the wind maintained a weak influence on the leeward sidewalk (Figure 3d).
In the E1 model, particles generated in the road moved along the main vortex and were divided into rising and falling particles at the leeward wall (Figure 4a). The rising particles moved in the exit direction from the external flow on the building, among which some moved to the windward sidewalk, causing PM particles to accumulate in both directions. In the E2 model (multi-layer planting), although the PM inflow into the windward sidewalk was observed, the same was not true for the leeward sidewalk (Figure 4b). In the E3 model (two-row single-layer planting), a primary vortex formed at the top of the windward wall and a secondary vortex formed in the center of the roadway, directing the flow in the leeward sidewalk direction (Figure 4c). This flow could also lead to the introduction of pollutants into the atmosphere. In the E4 model, unlike the other vegetation configurations, the flow formed from the roadway toward the leeward sidewalk; however, as the flow velocity passing between the shrubs and trees (0.03 m·s–1) was smaller than that around the tree canopy (0.1 m·s–1), the PM was affected by the rising flow passing the tree crown and did not flow into the sidewalk (Figure 4d). The E5 model showed a similar flow to the E2 model, where there was no inflow of PM through the leeward sidewalk; however, PM particles moved outside of the roadway and entered through the windward sidewalk (Figure 4e). In the E6 model, where the air and particulates from the roadway could not flow onto the sidewalk, only a small vortex formed on the sidewalk, thereby reducing the impact of pollutants in the atmosphere (Figure 4f).

3.1.2. Comparison by Tree-Shrub Configuration

According to an analysis of the effects of single- and multi-layer planting on flow, E1 maintained a faster velocity than that of E2 in Zone A (Figure 5) because the flow was pushed upward by a second vortex located below while a large second vortex was created with an increase in the flow over the sidewalk. An analysis of Zone B showed that as the gap between the bottom of the tree crown and the top of the shrub or sidewalk widened, the flow that entered the roadway from the sidewalk increased.

3.2. Analysis of PM Concentrations

3.2.1. Vegetation Configuration and Planting Arrangement

According to our analysis of PM at breathing height (1.5 m) along a four-lane road, the PM concentration (PM2.5, PM10) decreases from the center of the roadway toward the leeward sidewalk. Furthermore, the PM2.5 concentration at the center of the leeward sidewalk (Figure 6a, Point 9) decreased by 73.8–85.8% compared to that at the center of the roadway (Point 0). The concentration of PM2.5 in the middle of the sidewalk was lowest in F4, followed by that in F3, F2, and F1. At Point 8, the PM2.5 concentration was 17% and 37.4% lower in F2 and F3, respectively, than that in F1. The PM10 concentrations were similar to those of PM2.5; in the multi-layer planting configuration of F3 and F4, the concentrations were lower along the sidewalk (Figure 6b). While all four model types showed different PM2.5 concentration distributions, PM10 showed similar trends under F1, F2, F3, and F4. The concentrations in F1 and F2 were nearly identical up to Points 6, 7, where street vegetation occurred; however, they differed at Point 9, the center of the sidewalk.
In the eight-lane road, the PM concentrations at the center of the sidewalk could be largely categorized into two groups for both PM2.5 and PM10. Here, E1 and E3 (only trees in one or two rows) were categorized as one group, with all other model types forming the other group. The mean PM2.5 and PM10 concentrations of the E1 and E3 group were 71.0 and 140.3 μg·m–3, respectively; those of the other group were 28.5 and 64.5 μg·m–3, respectively (Figure 7). Thus, there was a >2-fold difference in the PM concentrations between the groups. According to the tree–shrub configuration, the PM concentration at the center of the sidewalk with multi-layer planting (E2) decreased by 68.2% compared to that in single-layer planting (E1). Within the multi-layer planting, the PM concentration did not significantly change between cross-sectional shrub heights (E2 and E5).

3.2.2. Roadway Width and Vegetation Configuration

The present study analyzed the differences according to the roadway width for the same planting type. For single-layer planting (F1 and E1), the PM2.5 concentrations at the center of the sidewalk decreased by ~73.1% and 40.2% in the four- and eight-lane roadways, respectively, compared to those at the center of the road; however, the PM10 concentrations decreased by 71.9% and 48.2%, respectively. A linear regression equation was established according to these recorded changes in the PM2.5 concentration; the resulting slope and adjusted R2 values were –11.6 and 0.74 for the four-lane road (F1) and –3.2 and 0.69 for the eight-lane road (E1), respectively (Figure 8a). The PM10 showed a similar trend, although the trendline slope varied. This can partially explain the fact that the reduction in the PM concentration was positively related to the absolute value of the slope.
For the multi-layer planting strategies (F3 and E2), the PM2.5 concentration in the center of the sidewalk decreased by 76.5% and 75.9% along the four- and eight-lane roads, respectively, compared to that at the center of the roadway; however, the PM10 concentrations decreased by 73.8% and 70.6%, respectively. For the calculated PM2.5 concentration trend line, the slope and adjusted R2 were –9.53 and 0.86 for the four-lane road, respectively; the corresponding values for the eight-lane road were –7.38 and 0.81, respectively. For the trend line of the PM10 concentrations, the slope and adjusted R2 were –18.96 and 0.83 for the four-lane road, respectively; the corresponding values for the eight-lane road were –15.01 and 0.81, respectively (Figure 8b).
For the vegetation barrier, the PM2.5 concentrations from the center of the roadway to the center of the sidewalk decreased by 83.4% and 68.8% on the four- and eight-lane roadways, respectively; however, the PM10 concentrations decreased by 80.7% and 72.7%, respectively. The slopes of the PM2.5 concentration trend lines were –13.14 and –6.89 for the four- and eight-lane roadways, respectively; the adjusted R2 values were 0.78 and 0.91, respectively. Hence, the explanatory power of the barrier along the eight-lane roadway was high. The slopes of the PM10 concentration trend lines were 26.3 and 14.6 for the four- and eight-lane roadways, respectively; the adjusted R2 values were 0.75 and 0.91, respectively (Figure 8c).
To comprehensively evaluate the effect of the vegetation configuration, the rate of PM reduction at the pedestrian breathing height was analyzed according to the vegetation volume along the street. For a four-lane road, 0.6 m3 was the optimal street vegetation volume for reducing both PM2.5 and PM10. In this case, the minimum vegetation width was 0.4 m based on the 1.5 m breathing height. For wider eight-lane roads, at a vegetation width of 0.8–0.93 m, planting volumes of 1.4 and 1.2 m3 were appropriate for reducing PM2.5 and PM10, respectively (Figure 9).

4. Discussion

4.1. Changes in Flow According to Vegetation Configuration

When the wind blows perpendicularly in a street canyon, two dominating flow phenomena occur in the central part and corners at either end [14,34]. In the simulated results of this study [34], a flow was observed down the windward wall, which traversed up to the top of the leeward wall that was parallel to the wind direction regardless of the road width. This was influenced by the external flow at the top, which led to the formation of a clockwise main vortex driven by the sheer force through the street [68]. Previous wind tunnel and CFD studies documented an increase in air pollution near the windward side, which slightly decreased near the leeward side under perpendicular wind [48]. When vegetation was increased, the aerodynamic effect overwhelmed the effect of deposition, thereby increasing the concentration near the windward wall and decreasing it near the leeward wall [69]. Similar results were observed in this study. The flow velocity was higher on the windward side than on the leeward side; the leeward velocity decreased the gap between the ground and crown. When the roadway width increased, the difference between the windward and leeward velocities also increased while the flow velocity of the leeward sidewalk decreased. The decrease in the velocity influenced the pollutant concentration by accelerating deposition and reducing dispersion. The edge where the sidewalk and building façade met influenced the effect of small vortices and turbulence diffusion because the level of enclosure increased according to the planting configuration. Vegetation acted as an obstacle and reduced the flow velocity. The airflow of the main vortex decreased with the flow velocity [44]; thus, the pollutants became less diluted [70].
In the multi-layer planting, the wider the gap below the tree, the greater the flow from the sidewalk to the roadway, which in turn led to increased PM flow into the opposite sidewalk. This is because the clockwise vortex decreased, and the air exchange rate increased as the gap between the ground and lower part of the crown increased [63].

4.2. Relationship between Vegetation Configuration and PM Concentration at the Breathing Height

4.2.1. Changes in the Concentration of PM According to the Vegetation Configuration

Microparticles can readily enter pulmonary alveoli via respiration [71], which can cause cardiovascular and respiratory diseases [72], thereby rendering the PM concentration at breathing height an important concern for pedestrian health.
On four-lane roads, the PM2.5 in the middle of the sidewalk was the lowest in vegetation barrier, followed by multi- and single-layers planting. Compared to single-layer planting (only trees), the multi-layer planting (trees + shrubs) showed lower PM2.5 levels, implying a decreased impact on pedestrians. Hence, multi-layer planting with a taller shrub height dispersed the flow of PM entering the sidewalk owing to the decreased distance between the lower branches of trees, as well as shrubs; accordingly, there was less of an impact from pollution on the sidewalk. Zhang et al. [14] conducted a CFD analysis of the changes in the PM concentration due to varying vegetation configurations at the pedestrian breathing height; the authors reported a reduction of 16.5–20.6% in PM for a tree–shrub planting configuration. This pattern can be explained using shrubs that limit lateral dispersion along the bottom part of the street [73]; consequently, a larger proportion of pollutants pass through the vegetation, where many particles are deposited. The PM10 concentrations tends to be similar to those of PM2.5, but if the height shrub is low in the multi-layer structure, the effect is similar to that of the single-layer structure. Gao and Niu [74] calculated the dispersion and deposition rates of different particle sizes through simulations, showing that gravitational sedimentation becomes a more dominant factor with an increase in particle size. This is because when a particle is small and turbulent, Brownian diffusion overwhelms the gravitational forces. As larger PM accumulates faster than it disperses, it is deposited on plant leaves while passing through shrubs, thereby reducing the PM concentration.
On eight-lane roads, there is a difference in the concentration of fine dust on the sidewalk according to the type of planting in single- and multi-layered structures. Although the results of this study were obtained via simulation, similar results were obtained in a field survey by Abhijith and Kumar [62], where PM concentrations on roads were monitored: no changes were observed along roads where only trees were planted. This can be attributed to the fact that there is only one main tree trunk (i.e., stem) between the canopy base and ground level, implying that the barrier effect is minimal because essentially no surface is available for deposition within the breathing zone. In multi-layer structures, shrub height that prevents PM inflow at the pedestrian breathing height is the most important factor. The effect of multi-layer planting was similar to the results obtained by Chen et al. [75], who analyzed the vegetation and PM10 concentration around a highway; they reported that an arbor–shrub vegetation configuration had a high PM removal rate on roads with heavy traffic. Maher et al. [76] found that leaf particulate lead and iron concentrations were highest at 0–0.3 m and 1.5–2.0 m above ground level, the latter of which is the average breathing height of adult pedestrians. Thus, the ability to more efficiently block particulate pollutants across this height range will decrease the impact of PM on pedestrians.
Although the vegetation barrier was effective at minimizing the impact of PM on pedestrians on a four-lane road, its effects were minimal for eight-lane roads. With a wider roadway, multi-layer planting was more effective than the vegetation barrier; when comparing the tree-planting arrangements, the PM concentration was relatively lower in the two-row arrangement compared to its one-row counterpart.
The monitoring results reported by Abhijith and Kumar [62] showed that trees with similar LAD had lower PM concentrations in multiple tree rows than those in a single row; thus, their field monitoring results were similar to the simulations in this study. The factor that most influenced the reduction in the PM concentration was the height of shrubs beneath the trees, not the number of trees themselves. Furthermore, the sidewalk must be sufficiently wide to plant two tree rows; therefore, if the sidewalk width is limited, planting vegetation in a multi-layer pattern is effective at reducing the PM concentration along the sidewalk. However, abundant street vegetation not only improves air quality, but can also provide other positive effects by enhancing other amenities, such as the moderation of the urban microclimate, temperature reduction, and noise abatement. As such, when sufficient space for street greenery can be secured, expanding the green space and applying two-row trees with multi-layered planting is advantageous.
As the PM size increased, its effects on the sidewalk in the multi-layer planting and vegetation barrier models diminished. Based on the tree–shrub configuration field monitoring results, coarse PM (PM2.5–10) significantly decreased, attributable to microparticulate removal via deposition and barrier effects [62]. In particular, identical results can be expected for not only vegetation barriers, such as green walls and hedges, but also for solid barriers, such as noise barriers and boundary walls. Similar to trees, studies have described that the aerodynamic effect is stronger than the pollutant removal capacity for barriers [77]. In one CFD study, the level of sidewalk pollution was 26–41% lower than that on the roadside when separated by a barrier [1,3]. Furthermore, field observations have confirmed that concentrations behind barriers decreased by 27–52% [78,79]. In a previous study, the sidewalk concentration behind the vegetation barrier was 19.8–33.8% lower than that on the road. These results indicate that a barrier can potentially have a positive effect on the air quality along a sidewalk; however, low porosity barriers should be prioritized [80], as the sidewalk pollutant concentration increases with the vegetation barrier [77,81]. Considering the effects of green infrastructure, which includes improvements to the atmosphere, façade greening can be used in solid barriers and for the development of roadway green spaces with multi-layered plants.

4.2.2. Changes in the Concentration of PM According to the Road Width

Research has shown that the air pollution purification effect of plants varies with the aspect ratio of the street width to building height (W/H) [16]. In terms of the air flow along roads, W/H is an important factor that affects the flow area of road canyons and characterizes various flow patterns [82]. These W/H-dependent flow patterns in urban street canyons occur because of various vortex structures formed [83]. Analyzing the difference according to the width of the road for the same planting structure suggested that this reduction effect was higher along narrower roadways. This result is consistent with the findings of Buccolieri et al. [16] who reported that as W/H increased, deeper and narrower street canyons formed, with a corresponding increase in the influence of trees. All multi-layer planting types had high explanatory power regardless of the PM size; irrespective of the lane number and roadway width, multi-layer planting was effective at minimizing pedestrian sidewalk-exposure to PM by controlling the inflow of airborne PM generated by passing vehicles. The effect of the PM concentration on pedestrians was the lowest in the barriered scenario, where the road and sidewalk were completely separated. Wania et al. [43] showed that as hedges are closer to the pollutant source, the PM removal rate is higher than that obtained with tree canopies owing to the aerodynamics offered by the vegetation. Trees diminish airflow and enable pollutants to accumulate along the road, allowing high concentrations to directly reach pedestrians [77]. In contrast, research has demonstrated the potential of hedges and shrubs as planting options capable of reducing pollutant concentrations at the breathing height [75].
Moreover, due to the large absolute value of the change in the slope of the PM10 concentration, street vegetation is more effective at reducing higher PM concentrations. Hinds [84] explained that particle size has a significant effect on deposition; in other words, ultrafine particles (diameters of ~0.1 μm) behave more like gas molecules and are deposited by diffusion, whereas sub-micrometer particles (1–10 μm) influence the surface that bends the air stream, which affects the deposition velocity; particles with diameters ≥ 10 μm fall to the ground owing to gravitational sedimentation. As small PM are relatively light, changes in their concentration due to dispersion by plants are negligible; however, heavier PM are deposited on the leaves and stems of trees and shrubs, thereby causing more significant changes in their concentrations.
The street width is directly proportional to the vegetation volume required to minimize the impact of PM along the sidewalk. Compared to single-layer planting, multi-layer planting with trees and shrubs or a shrub-based barrier provided the most efficient structure for securing the necessary volume of street greenery. Furthermore, the aerodynamic effects were important for reducing PM [5,77,85]. While it is important to individually select species with a strong deposition effect to maximize PM reduction, we must first consider the vegetation configuration and volume that can enhance the dispersion effect. Consequently, tree canopy porosity is a significant factor influencing aerodynamic effects, whereas the LAD and deposition velocity are the dominant mechanisms controlling deposition [5].

5. Conclusions

In this study, a CFD analysis was conducted on the changes in the PM concentrations generated from traffic emissions according to the roadside vegetation configuration, focusing on pollution at the pedestrian breathing height. The following conclusions were drawn based on simulations of varying road widths and planting types. First, roadside vegetation created a flow along the wind direction: if the flow from the windward sidewalk to the roadway was blocked by vegetation, there was a decrease in the possibility of road PM flowing into the leeward sidewalk. Second, compared to a single-layer planting strategy of only trees, a multi-layer planting strategy of both trees and shrubs was more effective at preventing PM inflow from the roadway to the sidewalk; however, if the road was narrow (four lanes), a PM reduction effect could be expected even with a single layer. Third, using a vegetation barrier in regions where the road and sidewalk are completely blocked yielded low PM concentrations, thereby decreasing adverse effects on pedestrians; however, multi-layer planting was more effective than a barrier when the road was wider (eight lanes). Thus, to reduce the impact of PM, securing a greater vegetation height, rather than crown width, is more important for preventing PM inflow from the roadway to the sidewalk. Fourth, street vegetation was effective at reducing the concentration of larger PM. Finally, based on a breathing height of 1.5 m, the optimal vegetation volume for reducing PM was 0.6 m3 along a four-lane road and 1.2–1.4 m3 along an eight-lane road.
The findings of this study were derived in terms of the PM reduction effect based on the road width and vegetation configuration; however, there are limitations in generalizing these simulation results for more complex roads. For example, turbulence from vehicular traffic was not clearly considered in the numerical model, while the wind direction was limited to only the perpendicular direction. A further limitation stems from performing simulations that simplify the vegetation into homogenous, spherical, and rectangular shapes without considering the porosity of the canopy. Nevertheless, the vegetation configuration presented in this study can provide insights for landscape architects and urban designers to reduce airborne particulates when managing street vegetation.

Author Contributions

Conceptualization, N.-R.J.; Methodology, N.-R.J. and S.-W.H.; Validation, N.-R.J., J.-H.K. and S.-W.H.; Formal analysis, N.-R.J. and S.-W.H.; Investigation, N.-R.J.; Visualization, N.-R.J.; Writing—original draft, N.-R.J.; Writing—review & editing, N.-R.J., J.-H.K. and S.-W.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

This study was carried out with the support of the “Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01427002)” of the Rural Development Administration, The Republic of Korea.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 13 00046 g001 550
Figure 1. Model geometry example for F3: (a) boundary conditions and (b) model layout and conditions. The red arrows indicate PM generation locations (Particle inlet).
Figure 1. Model geometry example for F3: (a) boundary conditions and (b) model layout and conditions. The red arrows indicate PM generation locations (Particle inlet).
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Figure 2. Grid example for modeling F3. (a) The model was constructed with surface and spatial grids of different sizes. The surface grid-particle inlet and walls formed a dense grid of 0.15 m; the spatial grids were applied as a tetrahedral grid model and then converted to polyhedrons. (b) Black-and-white reversal of the cross-section of the grid model. The white area is the region of interest with a dense grid.
Figure 2. Grid example for modeling F3. (a) The model was constructed with surface and spatial grids of different sizes. The surface grid-particle inlet and walls formed a dense grid of 0.15 m; the spatial grids were applied as a tetrahedral grid model and then converted to polyhedrons. (b) Black-and-white reversal of the cross-section of the grid model. The white area is the region of interest with a dense grid.
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Figure 3. Velocity contours and vectors in the four-lane road, reproduced by numerical simulations with different vegetation configurations; scenario: (a) a single-layer planting (F1), (b) multi-layer configurations with shrub heights of 50 cm (F2), (c) multi-layer configurations with shrub heights of 1 m (F3), and (d) vegetation barrier (F4).
Figure 3. Velocity contours and vectors in the four-lane road, reproduced by numerical simulations with different vegetation configurations; scenario: (a) a single-layer planting (F1), (b) multi-layer configurations with shrub heights of 50 cm (F2), (c) multi-layer configurations with shrub heights of 1 m (F3), and (d) vegetation barrier (F4).
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Figure 4. Velocity contours and in the eight-lane road, reproduced by numerical simulations with different vegetation configurations; scenario: (a) single-layer planting (E1), (b) multi-layer planting with a shrub height of 1 m (E2), (c) two-row single-layer planting (E3), (d) two-row multi-layer planting (E4), (e) multi-layer planting with a stepped shrub height (E5), and (f) vegetation barrier (E6).
Figure 4. Velocity contours and in the eight-lane road, reproduced by numerical simulations with different vegetation configurations; scenario: (a) single-layer planting (E1), (b) multi-layer planting with a shrub height of 1 m (E2), (c) two-row single-layer planting (E3), (d) two-row multi-layer planting (E4), (e) multi-layer planting with a stepped shrub height (E5), and (f) vegetation barrier (E6).
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Figure 5. Comparative velocity analysis for single- and multi-layer vegetation structures: (a) E1 and (b) E2. (a) describes the flow in the horizontal direction due to the generation of a second vortex below zone A and the large flow in zone B. (b) shows that a second vortex was not generated in zone A, resulting in vertical flow and reduced flow in zone B.
Figure 5. Comparative velocity analysis for single- and multi-layer vegetation structures: (a) E1 and (b) E2. (a) describes the flow in the horizontal direction due to the generation of a second vortex below zone A and the large flow in zone B. (b) shows that a second vortex was not generated in zone A, resulting in vertical flow and reduced flow in zone B.
Forests 13 00046 g005
Forests 13 00046 g006 550
Figure 6. Changes in the PM concentration at the breathing height according to the type of vegetation configuration along a four-lane road: (a) PM2.5 and (b) PM10. The measurement point was set up to the leeward sidewalk based on the central point of the road. Point 0 is the center of the road, Points 6 and 7 are the starting and ending points of the vegetation area, respectively, and Point 9 is the center of the sidewalk. Points 0–6, 6–7, and 7–10 are the road, vegetation area, and sidewalk, respectively.
Figure 6. Changes in the PM concentration at the breathing height according to the type of vegetation configuration along a four-lane road: (a) PM2.5 and (b) PM10. The measurement point was set up to the leeward sidewalk based on the central point of the road. Point 0 is the center of the road, Points 6 and 7 are the starting and ending points of the vegetation area, respectively, and Point 9 is the center of the sidewalk. Points 0–6, 6–7, and 7–10 are the road, vegetation area, and sidewalk, respectively.
Forests 13 00046 g006
Forests 13 00046 g007 550
Figure 7. Changes in the PM concentration at the breathing height according to the type of vegetation configuration along an eight-lane road: (a) PM2.5 and (b) PM10. The measurement point was set up at the leeward sidewalk based on the central point of the road. Point 0 is the center of the road, Points 6 and 7 are the starting and ending points of the vegetation area, respectively, and Point 9 is the center of the sidewalk. Points 0–6, 6–7, and 7–10 are the road, vegetation area, and sidewalk, respectively.
Figure 7. Changes in the PM concentration at the breathing height according to the type of vegetation configuration along an eight-lane road: (a) PM2.5 and (b) PM10. The measurement point was set up at the leeward sidewalk based on the central point of the road. Point 0 is the center of the road, Points 6 and 7 are the starting and ending points of the vegetation area, respectively, and Point 9 is the center of the sidewalk. Points 0–6, 6–7, and 7–10 are the road, vegetation area, and sidewalk, respectively.
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Forests 13 00046 g008 550
Figure 8. Changes in the PM density (dots) and linear regression predictions (lines) of the PM concentrations at the pedestrian breathing height (1.5 m) according to the distance from the source center of the roadway for three different vegetation models: (a-1,b-1,c-1): PM2.5; (a-2,b-2,c-2): PM10. (a) single-layer vegetation, (b) multi-layer vegetation, and (c) vegetation barrier.
Figure 8. Changes in the PM density (dots) and linear regression predictions (lines) of the PM concentrations at the pedestrian breathing height (1.5 m) according to the distance from the source center of the roadway for three different vegetation models: (a-1,b-1,c-1): PM2.5; (a-2,b-2,c-2): PM10. (a) single-layer vegetation, (b) multi-layer vegetation, and (c) vegetation barrier.
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Forests 13 00046 g009 550
Figure 9. The predictive model of the PM reduction rates for different vegetation volumes at the pedestrian breathing height of 1.5 m: (a-1) PM2.5 along the four-lane road, (a-2) PM10 along the four-lane road, (b-1) PM2.5 along the eight-lane road, and (b-2) PM10 along the eight-lane road.
Figure 9. The predictive model of the PM reduction rates for different vegetation volumes at the pedestrian breathing height of 1.5 m: (a-1) PM2.5 along the four-lane road, (a-2) PM10 along the four-lane road, (b-1) PM2.5 along the eight-lane road, and (b-2) PM10 along the eight-lane road.
Forests 13 00046 g009
Table
Table 1. Modeled vegetation configurations categorized by roadway width.
Table 1. Modeled vegetation configurations categorized by roadway width.
Model
Name		Lane #TreeShrubBarrierCross-Section of Vegetation
Configuration
Row #Height (m)Width (m)Height (m)Height (m)
F1417--- Forests 13 00046 i001
F2 170.5 - Forests 13 00046 i002
F3 171.0 - Forests 13 00046 i003
F4----3.0 Forests 13 00046 i004
E1817- - Forests 13 00046 i005
E2171.0 - Forests 13 00046 i006
E327- - Forests 13 00046 i007
E4271.0 - Forests 13 00046 i008
E5171.00.5- Forests 13 00046 i009
0.51.0
E6----3.0 Forests 13 00046 i010
Table
Table 2. Summary of key simulation parameters.
Table 2. Summary of key simulation parameters.
TypeNameValueUnits
Road StructureRoad Width4 (14)
8 (28)		Lanes (m)
MeteorologyWindVelocity3m·s–1
DirectionPerpendicular---
VegetationTreeHeight7m
Diameter5m
ShrubHeight0.5, 1m
Width1m
Vegetation barrier
(hedge)		Height3m
Width0.5m
PollutantParticulate matterMaterialCarbon-
Based		---
Density1650kg·m–3
Inlet300mg·m–2·day–1
Inlet velocity0.1m·s–1
Size1–100mm
Mean   ( D v 0.5 ) size10mm
Distribution coefficient3.5---
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Jeong, N.-R.; Han, S.-W.; Kim, J.-H. Evaluation of Vegetation Configuration Models for Managing Particulate Matter along the Urban Street Environment. Forests 2022, 13, 46. https://doi.org/10.3390/f13010046

AMA Style

Jeong N-R, Han S-W, Kim J-H. Evaluation of Vegetation Configuration Models for Managing Particulate Matter along the Urban Street Environment. Forests. 2022; 13(1):46. https://doi.org/10.3390/f13010046

Chicago/Turabian Style

Jeong, Na-Ra, Seung-Won Han, and Jeong-Hee Kim. 2022. "Evaluation of Vegetation Configuration Models for Managing Particulate Matter along the Urban Street Environment" Forests 13, no. 1: 46. https://doi.org/10.3390/f13010046

APA Style

Jeong, N.-R., Han, S.-W., & Kim, J.-H. (2022). Evaluation of Vegetation Configuration Models for Managing Particulate Matter along the Urban Street Environment. Forests, 13(1), 46. https://doi.org/10.3390/f13010046

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