# High resolution wind-tunnel investigation about the effect of street trees
on pollutant concentration and street canyon ventilation

Sofia Fellini<EMAIL_ADDRESS>Massimo Marro  Annika Vittoria Del
Ponte  Marilina Barulli  Lionel Soulhac  Luca Ridolfi  Pietro Salizzoni  Univ
Lyon, INSA Lyon, CNRS, Ecole Centrale de Lyon, Univ Claude Bernard Lyon 1,
LMFA, UMR5509, 69621, Villeurbanne France  Department of Environmental, Land,
and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli
Abruzzi 24, 10129 Turin, Italy

###### Abstract

Greening cities is a key solution to improve the urban microclimate and
mitigate the impact of climate change. However, the effect of tree planting on
pollutant dispersion in streets is still a debated topic. To shed light on
this issue, we present a wind-tunnel experiment aimed at investigating the
effect of trees on street canyon ventilation. An idealized urban district was
simulated by an array of blocks, and two rows of model trees were arranged at
the sides of a street canyon oriented perpendicularly with respect to the wind
direction. Reduced scale trees were chosen to mimic a realistic shape and
aerodynamic behaviour. Three different spacings between the trees were
considered. A passive scalar was injected from a line source placed at ground
level and concentration measurements were performed in the whole canyon.
Results show that the presence of trees alters the concentration pattern in
the street with a progressive shift from a nearly two-dimensional to a three-
dimensional field depending on tree density. Despite the significant change of
the concentration field induced by trees, the average level of pollution in
the street, and thus the overall ventilation efficiency, does not show a
specific trend with the density of trees.

††journal: Journal of LaTeX Templates

##  1 Introduction

Urban vegetation plays a key role for the livability of cities [1]. Beyond its
aesthetic role, the presence of vegetation brings numerous environmental
benefits in urban areas. Evapotranspiration and shading have cooling effects
that mitigate the urban heat island [2, 3]. The large surface area per unit
volume of vegetative structures facilitates particle deposition which acts as
a sink for pollutant particles [4]. Moreover, vegetation has a fundamental
role in the hydrological cycle: water infiltration in vegetated soils retains
stormwater from entering the drainage system, thus reducing the risk of flash
floods [5, 6].

While the social and environmental benefits of vegetation in cities are well
recognized, the role of vegetation on pollutant dispersion is still
controversial [7] and depends on the non-trivial interaction between the flow
field, the vegetative elements, and the surrounding built environment [8].
This is particularly true in street canyons, where the turbulent flow field is
strongly influenced by the geometry of the canyon, its orientation with
respect to the external wind [9], the presence of obstacles [10], and the
properties of the building walls, e.g., wall roughness and temperature [11,
12, 13, 14]. The presence of vegetated façades on the building walls, for
example, alters the near-wall velocity and may result in a reduction of the
turbulent air exchange [15]. Low-level hedges, instead, generally improve the
air quality at street level and thus help reducing the dose of pollutants
inhaled by pedestrians [16].

The interaction between turbulent flow and vegetation is even more complex in
tree-lined avenues, where trees occupy a significant volume of the canyon and
their effect on pollutant dispersion depends on the properties and shape of
the crowns, the height of the trunks, and the planting pattern [17, 18]. A
pioneering series of wind-tunnel experiments was performed by Gromke et al.
[19, 20, 21] to investigate this scenario. In their first studies, trees were
modelled as a row of small-scale trees with spherical, permeable crowns on
thin stems, placed in the middle of a street canyon of unit height to width
ratio $H/W$ [19]. The wind was perpendicular to the canyon axis. The flow
field within the canyon and the concentration at the canyon walls were
explored by varying different properties of the trees (crown diameter, tree
height, tree spacing). A relevant increase of concentration at the upwind wall
and a slight decrease of concentration at the downwind wall were observed.
These variations were more marked when the canyon was occupied by the greatest
volume of vegetation (large diameter of crowns and small distance between the
trees). To better investigate the effect of tree crown porosity, in a later
study, trees were replaced with a metallic cage filled with different amounts
of synthetic wadding material [20]. They found that the concentration at the
canyon walls is sensitive to crown porosity only for high porosity values
($>97\%$). Adopting the same experimental conditions, Buccolieri et al. [22,
23] simulated a large street canyon ($H/W=0.5$) with two rows of trees. They
also analysed the case of an approaching wind inclined by $45^{\circ}$ with
respect to the street axis. The aspect ratio of the canyon and the wind
direction turned out to be more influential with respect to vegetation density
and crown porosity. However, they evidenced that neglecting the presence of
vegetation in the streets would lead to significant errors in the predictions
of concentration levels.

The modification of the airflow and concentration field within streets due to
tree planting has been widely studied also by means of numerical simulations.
Reynolds-averaged Navier-Stokes (RANS) models proved to be able to
qualitatively reproduce the experimental results [e.g. 24, 22, 23, 25, 26] but
Large Eddy Simulations provide a better agreement as they solve the
intermittent and unsteady fluctuations of the turbulent flow which plays a
major role in ventilation dynamics [e.g., 27, 28]. However, the advantages of
LES involve higher computational costs compared to RANS [27]. Merlier et al.
[29] showed that, thanks to its computational efficiency, LES with the lattice
Boltzmann method (LBM) is a promising technique to predict dispersion in
street canyons with tree plantings.

Despite remarkable advances in numerical models, simulating complex and porous
geometries such as trees and their effect on pollutant dispersion still
represents a challenge. Wind-tunnel experiments are thus highly recommended to
improve and validate existing models. For these reasons, we present in this
work experimental results aimed at evaluating how tree planting influences the
concentration field within a street canyon. To this aim, we reproduce a street
canyon oriented perpendicular with respect to the wind direction, with two
lateral rows of trees and a linear source of gas to mimic vehicular emissions.
To provide a detailed characterization of the scalar field in the three-
dimensional street canyon, the concentration is measured on a high-refined
measurement grid with around 1000 sampling points for each configuration. The
reference geometry is that of a two-dimensional canyon closed laterally, i.e.
it does not communicate with side streets. Although this is an uncommon
configuration, it allows us to accurately estimate the exchange rate of
pollutants between the canyon and the atmosphere above.

In Section 2, the experimental setup and the adopted measurement techniques
are presented. In Section 3, we discuss the similarity criteria for the
aerodynamic modelling of trees and for the boundary layer in the wind tunnel.
The characterization of the concentration field in the street canyon is
reported in Section 4, together with the estimate of the ventilation
efficiency. Moreover, in Section 5, we investigate the effect of trees on
pollutant concentration in streets with different geometrical boundaries
(street intersections, open or closed canyon). Finally, the conclusions and
perspectives of the work are presented in Section 6.

##  2 Experimental setup and measurement techniques

###  2.1 Wind-tunnel setup

The experiments were performed in the atmospheric wind tunnel at the
Laboratoire de Mécanique des Fluides et d’Acoustique (LMFA) at the École
Centrale de Lyon. The aerodynamic circuit is composed by an axial fan which
induces wind velocities between 0.5 and 6 m/s, flow diverging and converging
systems, and an upwind grid for the generation of homogeneous turbulence. A
heat exchanger system regulates the air temperature with a precision of $0.5$
K. The test section of the wind tunnel is 12 m long, 3.5 m wide, and 2 m high.

To simulate an idealized urban district (Fig. 1) and the turbulent flow within
and above it, the floor of the entire test section was overlaid with an array
of square blocks (panel a in Fig. 2). The blocks were 50 cm wide and 10 cm
high and made of wood and polystyrene. The spacing between the obstacles was
10 cm in the spanwise direction and 20 cm in the streamwise direction. In this
way, we obtained a street network composed of square canyons (height to width
ratio $H/W=$1) aligned with the wind direction intersecting larger
perpendicular streets ($H/W=0.5$). The two different proportions were selected
to avoid channeling effects along the wind direction and to recreate tree-
lined boulevards in the direction perpendicular to the wind. The blockage
ratio of the model to the cross-section of the wind tunnel was $5\%$.

A neutrally stratified boundary layer approximately 1.1 m depth was generated
by combining the effect of a row of 0.95 m high Irwin spires [30], placed at
the beginning of the test section, and the building-like obstacles on the
floor. Moreover, the obstacles were covered by 5 mm high nuts to generate
further roughness and accelerate the full development of the boundary layer.
The free stream velocity at the top of the boundary layer ($U_{\infty}$) was
kept constant at 5 m/s. More details about the boundary layer above the
obstacles is given in Section 3.2.

The reference street canyon (see photo reported in Fig. 2.c) was placed
perpendicular to the wind direction, at a distance of approximately 9 meters
from the beginning of the test section. Its length (L), width (W), and height
(H) measured 1.0 m, 0.2 m, and 0.1 m, respectively (Fig. 2.b and d), thus
providing the following aspect ratios: $H/W=0.5$, $L/H=10$, and $L/W=5$. In a
1:200 scale, the street canyon matches fairly well a typical tree-lined
boulevard, 40 m wide and flanked by 20 m high buildings, as in typical
European city centres (e.g., Barcelona, Turin, Lyon) [31]. Note that, in the
experiment, the length of the canyon does not cover the whole width of the
test section as it is closed laterally by polystyrene blocks. This makes the
canyon similar to the one investigated numerically by Moonen et al. [32]. This
geometry was chosen in order to define a control volume to compute the mass
balance for an accurate estimate of the ventilation efficiency (see Section
4.2). We are aware that other boundary conditions are possible, for instance
street intersections or an indefinitely long canyon. The effects of different
lateral boundaries will be discussed in Section 5.

To simulate urban vegetation in the street canyon, model trees were aligned
along two lateral rows 14 cm apart. Three different configurations for the
tree density were analysed: in configuration Zero (panel d), the street canyon
was empty. In configuration Half (panel e), seven trees equally spaced by 14
cm (9.5 cm distance between the crowns) were arranged along each lateral row.
In configuration Full (panel f), the lateral rows were composed of fourteen
trees equally spaced by 7 cm (2.5 cm distance between the crowns). The
aerodynamic characterization of model trees is reported in Section 3.1.

Figure 1: (a) Aerial view of the city of Barcelona (Spain). Source: Barcelona
From Above by Ian Harper. (b) The modelled urban canopy in the wind tunnel.
Figure 2: (a) Sketch of the urban canopy in the test section of the tunnel.
The blocks delimiting the reference street canyon are coloured grey. Sketch
(b) and photo (c) of the front view of the street. Top view of the street
canyon model for the different configurations of tree density: (d) Zero, (e)
Half, (f) Full. The yellow line represents the pollutant source

To simulate vehicular emissions, a tracer was emitted by a linear source at
the centre of the reference street canyon. The source is the same adopted by
Marro et al. [33], to which we refer for more details. It consists of a
stainless steel tube pierced with needles emitting a gas mixture in a
homogenization chamber, located in a slot cut in the tunnel floor. From this
chamber, the gas is released at street level from a 65 cm long and 1 cm wide
metallic grid. The small diameter of the needles produces substantial pressure
drop thus rendering the gas release insensitive to local pressure fluctuations
in the street canyon. This design aims at minimizing the vertical momentum and
maximizing the lateral homogeneity of the emission, following the indications
of Meroney et al. [34]. The source released a mixture of air and ethane. The
latter is used as a passive tracer, since it is detectable by the Flame
Ionisation Detector (FID) (see Section 2.2) and has approximately the same
density of air (the density ratio between ethane and air being about 1.03).
The total air-ethane flow rate was 4.0 l/min, with a percentage of ethane of
$5\%$ in volume (0.2 l/min). The injection velocity at street level (estimated
as the ratio between the total flow rate and the release surface at street
level) was around 0.01 m/s. The supply of the two gases was regulated by two
digital mass-flow controllers (Alicat Scientific MC-Series) working in a range
between 0.1 and 20 Nl/min (for air) and 0.01 and 2 Nl/min (for ethane), with
an accuracy of 0.5%.

###  2.2 Measurement techniques

The concentration field within the reference street canyon was measured using
a Flame Ionisation Detector (HFR400 Fast FID) [35], which is commonly used for
measurements in urban-like geometries [36, 37, 14]. A straight 30 cm long
sampling capillary tube, with radius $1.27\times 10^{-4}$ m, was mounted on
the Flame Ionisation Detector head, which was positioned above the test
section so as not to affect the flow field. The imposed pressure drop along
the capillary tube was 33330.6 Pa. The sampling frequency of the FID signal
was fixed at 1000 Hz [38]. The instrument works in the range 0-10 Volt and it
can detect concentration values between 0 and 5000 ppm with an accuracy of
about 1-2 ppm. A more detailed description of the FID is provided by Marro et
al. [33].

The measurements were performed in statistically steady conditions: a constant
flow rate of ethane was injected from the ground level source and the
concentration within the cavity was measured at around 1000 sampling points
for each configuration of tree density. The measurement grid was defined to
characterize the entire three-dimensional volume in detail (see Fig. S2 in the
Supplementary Material). For each point, we fixed a sampling time of 2 minutes
which provides a reliable estimate of the mean concentration. Moreover, before
and after each acquisition, the background concentration was recorded by
stopping the emission for 15 seconds (and leaving time for the transients to
settle). The background concentration, which was assumed to evolve linearly
with time from its initial to its final value, was then subtracted from the
signals [33, 39].

The velocity field above the obstacles was characterized (Section 3.2) by
means of a Hot-Wire Anemometer (HWA) at constant temperature, using an X-wire
probe with acceptance angle of $45^{\circ}$. In this way, two velocity
components of the velocity field were measured simultaneously. The platinum
probe wire was 1 mm long and with a diameter of 5 $\mu$m. The small size of
the hot-wire element enables good spatial resolution of the velocity field
while the low thermal inertia of the material ensures fast response, allowing
the detection of high-frequency fluctuations of the turbulent flow [40]. An
acquisition time of 1 minute at a frequency of 4000 Hz was adopted for each
sampling point. Calibration of the HWA was carried out using a Pitot tube to
measure a reference velocity.

For the aerodynamic characterization of the model trees (presented in Section
3.1), their drag coefficient was measured in a small closed-circuit wind
tunnel with a 30 cm $\times$ 30 cm test section being able to generate
velocities up to 25 m/s. The tunnel was equipped with an external load cell
with a precision of 0.01 N. Different layouts of trees were attached to a
removable plate connected with the load cell. The drag coefficient was
estimated for a varying wind velocity inside the tunnel. Moreover, the
aerodynamic porosity of the model trees was evaluated by performing velocity
measurements upwind and downwind a single tree on a regular grid of points.
Since in this case we needed to measure only the average velocity, we used a
Pitot tube.

##  3 Similarity criteria

###  3.1 Aerodynamic characterization of model trees

To investigate the effect of trees in urban areas by means of wind-tunnel
experiments, buildings and vegetative structures need to be modelled in small
scale. Similarity criteria are then necessary to transfer small-scale findings
in the wind tunnel to full-scale applications. For impermeable and rigid
structures, like buildings, dynamical similarity between the experiment and
the real application exists if the model and the full-scale object are
geometrically similar and the value of the Reynolds number is the same [41].
In case of fully turbulent flows around bluff bodies with sharp edges, this
condition is weakened and flow similarity is assumed as far as the Reynolds
number is sufficiently large. On the other hand, less knowledge is available
about the appropriate similarity criteria for vegetative structures. From a
fluid dynamical point of view, vegetation is a complex porous medium made of
branches and leaves giving rise to the development of boundary layers, wakes,
and recirculation zones [42]. Moreover, due to their flexibility, trees can
sway with the wind and induce fluid-structure interactions.

In previous wind-tunnel experiments, trees, windbreaks, and canopies have been
modelled by using different materials, e.g., brushes, cotton balls, metal
screens, and plastic stripes. Aerodynamic validation of the adopted structures
was done by analysing different fundamental features of the interaction
between the trees and the flow field, as the drag coefficient, the
characteristics of the wakes behind the trees [43], the ratio between tree
height and roughness length [44], the leaf area density [45], or the sway
frequency [46]. Gromke and Ruck [20, 21] used the pore volume fraction and the
pressure loss coefficient to characterize the porous media that mimicked
avenue trees. Gromke [47] also established a similarity criterion based on the
pressure loss coefficient. More systematically, Gromke and Ruck [42] analysed
the aerodynamic characteristics of 12 small-scale modelled trees made of
different materials and porosity. Measurements of the drag coefficient and of
the flow field around the crowns evidenced the drag coefficient as a key scale
parameter for the modelling of trees. Manickathan et al. [48] compared the
aerodynamic behaviour of model and natural trees in a wind tunnel. They found
that, together with the drag coefficient, the aerodynamic porosity of the tree
crown is another key parameter to compare natural and model trees.

In accordance with these studies, we mimicked natural trees with plastic trees
for railway modelling and we characterized their aerodynamic behaviour by
estimating their aerodynamic porosity and their drag coefficient. We also
investigated their optical porosity. The trees were 8.5 cm high and 4.5 cm
wide, with crowns in plastic porous material on plastic trunks. Under the
conditions of the experiment, tree models behaved like rigid bodies and thus
deformations and fluid-structure interactions could be neglected.

Aerodynamic porosity ($\alpha_{p}$) is defined [49] as the ratio of the time
average wind speed behind the obstacle ($U_{b}$) and the average speed of the
approaching wind ($U_{ref}$):

$\alpha_{p}=\frac{\int_{A_{c}}U_{b}(x,y)dA_{c}}{\int_{A_{c}}U_{ref}(x,y)dA_{c}},$
(1)

where $A_{c}$ is the projected frontal area of the obstacle. In other words,
aerodynamic porosity determines the portion of the flow that passes through
the porous material with respect to the flow that diverges from the obstacle.
We note that this aerodynamic parameter was initially introduced to
characterize windbreaks. However, its application has been more recently
extended to individual trees [50, 51, 48]. To estimate $\alpha_{p}$, we
performed velocity measurements upstream and downstream a single tree that was
placed in a homogeneous flow where (i.e. $U_{ref}\approx$const), so that the
integral at the denominator in Eq. 1 becomes $U_{ref}\cdot A_{c}$. Following
the indication of Guan et al. [49], the velocity behind the tree was measured
in the first plane not occupied by the tree branches. Velocities were measured
on a regular and dense grid and a two-dimensional velocity field was obtained
through spatial interpolation. The spatial average velocity was then estimated
by integrating the velocity field over the tree silhouette (Fig. 3.b). The
mean speed upstream ($U_{ref}$) and downstream ($U_{b}$) the tree were 4.95
m/s and 1.48 m/s, respectively. By means of Eq. 1, we obtained
$\alpha_{p}=0.3$, a value in line with that of common natural trees, as
hollies and cypresses (see square markers in Fig. 3.d from the study of
Manickathan et al. [48]).

The optical porosity $\beta_{p}$ is another commonly used parameter to
characterize the vegetation and it can be easily estimated by elaborating
digital photos [52]. It is defined as the ratio between the open surface of a
porous material and its total surface. Through a digital elaboration of the
photo capturing the frontal view of the model tree (Fig. 3.a), we delimited
the silhouette of the tree and obtained its cross-section, $A_{c}\approx
3.5\times 10^{-3}$ m2. We then estimated the optical porosity,
$\beta_{p}\approx 0.05$, as the ratio of the number of white pixels to the
total number of pixels within the silhouette of the tree. According to the
empirical relationship found experimentally by Guan et al. [49], the optical
porosity ($\beta_{p}$) is related to the aerodynamic porosity as:

$\alpha_{p}\simeq\beta_{p}^{n},$ (2)

where the exponent $n$ was estimated by Guan et al. [49] equal to 0.4 for
realistic windbreak. Introducing our estimated values for $\alpha_{p}$ and
$\beta_{p}$ in Eq. 2, we find $n\approx$ 0.402 that is consistent with the
cited study. The relation in Eq. 2 can then be conveniently used for deriving
the aerodynamic porosity when velocity measurements cannot be directly
performed.

Figure 3: a) Front photo of the model tree. b) 2D velocity field downstream
the tree obtained by spatial interpolation of point velocity measurements for
the estimate of $\alpha_{p}$. c) Drag coefficient as a function of the
Reynolds number and the wind velocity for four different alignments (reported
by different colours) of a single model tree, obtained by rotating the
vertical axis of the tree at $90^{\circ}$ intervals. d) Drag coefficient and
aerodynamic porosity for various model trees and natural trees. From the study
of Manickathan et al. [48].

The drag coefficient is defined as:

$c_{d}=\frac{2F}{\rho_{a}U_{ref}^{2}A_{c}},$ (3)

where $F$ is the drag force [N], $\rho_{a}$ is air density ($\approx 1.18$
kg/m3 at $25^{\circ}$C), $U_{ref}$ is the reference velocity [m/s] for the
approaching wind, and $A_{c}$ is the projected frontal area of the tree [m2].
The drag force $F$ was measured by means of a load cell, while the velocity
$U_{ref}$ was measured with a Pitot tube, as explained in Section 2.2.

In Fig. 3.c, we report the drag coefficient as a function of the wind velocity
$U_{ref}$ and Reynolds number $Re$ for four different faces of a single model
tree, obtained by rotating the vertical axis of the tree at intervals of
$90^{\circ}$. The Reynolds number was calculated as $Re=U_{ref}H_{T}/\nu$,
where $H_{T}$ is the tree height and $\nu$ is the air kinematic viscosity
($\nu\approx 1.55\times 10^{-5}$ m2/s at a temperature of 25∘C). Except for
the values at low speed (where the experimental uncertainty of the measurement
is large), the drag coefficient rapidly converges to a constant value around
0.75. As shown in Fig. 3.d, this value is in line with the drag coefficient of
natural trees and confirms that the model trees adopted in this study present
realistic aerodynamic properties.

We note that natural trees undergo foliage reconfiguration and their drag
coefficient decays with increasing wind speed [48]. This is not found in our
model trees that do not deform. However, since in this study we focus on
moderate velocities in a street canyon, we are not interested in reproducing
the flexibility of natural trees.

###  3.2 Characterization of the boundary layer

The turbulent boundary layer developing above the idealised urban district was
characterized by measuring and focusing on the statistical properties of the
turbulent flow. To this aim, we measured vertical velocity profiles in
different positions of the wind tunnel.

The evolution of the boundary layer along the central axis of the wind tunnel
(from $x=-1.27$ m to $x=1.27$ m) is shown in the right part of Fig. 4.a (blue
top $x$-axis). The good overlapping between the curves reveals that the flow
is fully developed when it approaches the reference canyon ($x=0$), i.e. its
development in the stream-wise direction is so slow that changes over the
fetch can be neglected. As already introduced in Section 2.1, the free-stream
velocity ($U_{\infty}$) is around 5 m/s while the height of the boundary layer
($\delta$) is around 1.1 m. The ratio $\delta/H$=11 is in the typical range
($\delta/H\approx 10-20$) for atmospheric flow over urban canopies [53]. The
characteristic Reynolds numbers based on the obstacle height are
$Re_{\infty}=U_{\infty}H/\nu\approx 3.3\times 10^{4}$ and
$Re_{H}=U_{H}H/\nu\approx 1.25\times 10^{4}$, where $U_{H}=1.94$ m/s is the
mean horizontal velocity at $z=H$. These values are sufficiently high to
ensure fully-developed turbulent flow. For a square cavity, Allegrini et al.
[11] obtained a Reynolds independent flow for $Re_{\infty}>1.3\times 10^{4}$,
while Castro and Robins [54] and Marucci and Carpentieri [13] showed that the
condition was met for $Re_{H}>4000$.

Four velocity vertical profiles were measured at different positions within a
periodic unit of the urban canopy (see the inset and the velocity profiles on
the left side of Fig. 4.a, red bottom $x$-axis). The influence of the
individual obstacles is evident in the lower part of the velocity profiles
where the velocity profiles collected in different positions show
discrepancies one from another up to $z=0.15\,\delta$ ($z=1.65\,\delta$). This
height can be considered as the upper limit of the so-called roughness
sublayer [55] and is in line with respect to the typical value of 2$H$
estimated in European cities with regular building geometries [56]. Above this
height, the inertial sublayer develops and the flow variables depend on the
vertical coordinate only. In this zone, the mean velocity profile is usually
modelled by the logarithmic law (Fig. 4.b):

$\frac{U}{u^{*}}=\frac{1}{\kappa}\ln\frac{z-d}{z_{0}},$ (4)

where $\kappa=0.4$ is the Von Kármán constant, $z_{0}$ is the aerodynamic
roughness length, $d$ is the zero-plane displacement, and $u_{*}$ is the
friction velocity. In the literature, several techniques have been developed
to determine the values of these parameters [57]. In the Supplementary
Material (Section S1), we compare the results from two different methods and
we obtain the following values: $u^{*}=0.29$ m/s ($u_{*}/U_{\infty}=0.051$),
$d=0.094$ m ($d/\delta=0.085$), $z_{0}=1\times 10^{-3}$ m
($z_{0}/\delta=9\times 10^{-4}$). The normalised roughness length $z_{0}/H$
and zero-plane displacement $d/H$ are in the reasonable range of variation
proposed by Grimmond and Oke [58], who collected and reviewed different
estimate methods based on the packing density of buildings, i.e. on the non-
dimensional plan area $\lambda_{p}$ (here $\lambda_{p}=0.6$) and the non-
dimensional frontal area $\lambda_{f}$ (here $\lambda_{f}=0.12$).

Figure 4.c reports the vertical profile of the Reynolds stresses, obtained as
a spatial average over the four horizontal positions reported in the inset of
Fig. 4.a. The constant-stress region is represented by full markers. The
vertical profiles of the standard deviation of the velocity components
($\sigma_{u}$, $\sigma_{v}$, and $\sigma_{w}$) and of the turbulent kinetic
energy ($k$) are reported in Figs. 4.d and e, and are in line with previous
experiments [59] performed in the same wind tunnel. The turbulent velocity
components above roofs, scaled on the friction velocity $u_{*}$, provide
$\sigma_{u}/u_{*}\approx 2.1$, $\sigma_{v}/u_{*}\approx 1.6$, and
$\sigma_{w}/u_{*}\approx 1.3$. It is worth noting that these values are
consistent with those provided by Britter and Hanna [60] for typical urban
areas ($\sigma_{u}/u_{*}=2.4$, $\sigma_{v}/u_{*}=1.9$, and
$\sigma_{w}/u_{*}=1.3$), albeit the horizontal and lateral components are
slightly underestimated. We also provide in Fig. 4.f the vertical profile of
the turbulent kinetic energy dissipation rate ($\varepsilon$), which is a
fundamental parameter for turbulence closure models in CFD simulations. The
dissipation rate was estimated from the HWA measurements as:

$\varepsilon=\frac{15\nu}{U^{2}}\overline{\left(\frac{\partial u}{\partial
t}\right)^{2}},$ (5)

by employing the isotropic approximation and Taylor’s hypotheses of frozen
turbulence [61]. The vertical profile of $\varepsilon$ agrees well with the
production rate of turbulent kinetic energy, here estimated as
$\mathcal{P}\approx-\overline{u^{\prime}w^{\prime}}\frac{\partial U}{\partial
z}$. This shows that, in most of the boundary layer, the production and
dissipation rate of turbulent kinetic energy are in local equilibrium [38], a
condition required to recover a typical logarithmic boundary layer profile
[62].

Figure 4: a) Mean velocity at 4 different position in a spatial periodic unit
(red bottom $x$-axis) and at 4 different distances along the streamwise
direction of the wind tunnel (blue top $x$-axis). For the two groups of
profiles, a vertical line corresponding the $U/U_{\infty}=1$ is reported. The
horizontal dashed line corresponds to the canyon roof level ($H$). b) Mean
velocity obtained as average over four different positions. The line
represents the logarithmic law with $u_{*}/U_{\infty}=0.051$,
$z_{0}/\delta=9\times 10^{-4}$, and $d/\delta=0.085$. The full symbols
indicate the region where the logarithmic law applies. c) Reynolds stresses
$-\overline{u^{\prime}w^{\prime}}$. The full symbols indicate the constant-
stress region. d) Standard deviation of the three velocity components. e)
Turbulent kinetic energy. f) Production and dissipation rate of turbulent
kinetic energy.

##  4 Street canyon ventilation

###  4.1 Mean concentration field

The mean concentration field inside the street canyon was characterized for
the three configurations of tree density presented in Fig. 2. As mentioned
above (Section 2.2), the concentration of ethane - released from the line
source - was measured, by means of a Flame Ionization Detector, at around 1000
sampling points (for each configuration) distributed on a three-dimensional
grid. The non-dimensional concentration is expressed as
$\overline{C^{*}}=CU_{\infty}L_{s}\delta/Q_{et}$, where $C$ is the time-
averaged concentration of ethane in each sampling point, $L_{s}$ is the source
length, and $Q_{et}$ is the mass flow rate of ethane. In the following, the
results are presented in two-dimensional sections obtained from linear
interpolation of the measured data. For a complete visualisation of the
concentration field inside the canyon refer to Section S3 of the Supplementary
Material.

Figure 5 shows two cross-sections for each configuration of tree density.
Panels a-c report the concentration field on a lateral cross-section
($y/H\approx$-1.5), whereas panels d-f correspond to the central (around
$y/H\approx$0) cross-section. Regardless of the presence of trees, a clear
increase in the concentration from the downwind wall to the upwind wall can be
observed in all the sections. This pattern is in accordance with previous
studies [19, 20] and evidences the action of the main recirculating cell of
the velocity field inside the canyon: fresh air enters the canyon at the
downwind wall and transports the pollutant (emitted in the centre of the
street) to the upwind wall, where part accumulates at the lower corner, part
is moved outside, and part is entrained towards the downwind wall. The
inhomogeneity of the concentration field along the $x$-axis results in a
significant difference in air quality in the lower part of the canyon (i.e. at
$z/H=0.2$). This difference is accentuated when trees are present: in a canyon
without vegetation (Fig. 5.a and d), pollutant concentration at the downwind
wall ($x/H=0.1$ and $z/H=0.2$) is roughly 3 times lower than the one at the
upwind wall ($x/H=0.9$ and $z/H=0.2$), while in presence of trees this
difference along $x$ increases up to 8 times in the lateral section (Fig. 5.b
and c). Although the lowest measured horizontal section ($z/H=0.2$,
corresponding to 4 m at the 1:200 scale) is too high to represent the
pedestrian level (which is usually considered to be at 1.5 m), the
concentration field at this height highlights the strong impact of trees on
concentration spatial pattern (see Fig. S6 in the Supporting Information for
further details). We also remark that, for the non-vegetated canyon, the
concentration field remains almost unchanged along the $y$-axis (panels 5.a,d)
while the presence of trees alters this behaviour: in both the Half and Full
configurations, pollutant concentration in the central section (panels 5.e,f)
is significantly lower than in the lateral one (panels 5.b,c).

Figure 5: Mean concentration of the passive scalar on a lateral cross-section
at $y/H\approx-15$ (first line) and in the centre of the canyon $y/H\approx-0$
(second line). Zero (a and d), Half (b and e) and Full (c and f)
configurations are shown. Measurement points are reported as circles colored
according to the measured value.

To better visualize the spatial distribution of the concentration field, we
show in Fig. 6 an horizontal plane along the canyon axis, at $z/H$=0.6. The
concentration gradient along the $x$-axis, from the downwind to the upwind
wall, is clearly visible in all three configurations. As highlighted above,
this gradient is enhanced in the vegetated canyons. Analysing the
concentration near the walls (see also the entire cross sections at $x/H=0.1$
and $x/H=1.9$ in the Supplementary Material), we find that this is due to an
average increase in the concentration at the upwind wall in the vegetated
canyons, rather than to a decrease of the concentration at the downwind wall,
which remains almost constant in the different configurations. This result is
in line with the studies of Gromke and Ruck [19] and Buccolieri et al. [22]
who found that the presence of trees lead to a significant increase in
pollutant concentration at the upwind wall and slight to moderate decrease at
the downwind wall. Figure 6 also shows that, along the longitudinal axis
($y$-axis), the concentration is almost homogeneous in the Zero configuration
(panel a), except for the low values at the edges of the domain due to the
limited length of the linear source. On the other hand, the homogeneity along
the $y$-axis is lost when trees are added. In the Full configuration (panel
c), it is possible to identify a region with lower concentration in the middle
of the canyon and two nearly symmetric accumulation regions at its sides. In
the Half configuration (panel b), the concentration field is even more
heterogeneous and three accumulation regions can be identified. The same
spatial distribution can be inferred from Fig. 7, where a vertical section in
the middle of the canyon ($x/H$=1) is represented. Again, we observe a
homogeneous concentration field in the empty canyon, while pollution peaks are
evident in the Half and Full configurations. Along the vertical axis, the
concentration remains fairly constant. This is typical in the centre of the
canyon (i.e. $x/H$=1) and was already visible when focusing on vertical
profiles at $x/H$=1 in Fig. 5. Moving towards the upwind wall (Fig. 8 shows
the vertical section at $x/H$=0.15), however, the concentration is greater at
street level and a gradient along the vertical axis emerges.

By averaging the concentration over the vertical direction ($z$) in the centre
of the canyon ($x/H$=1), we obtain in Fig. 9 the concentration trend along the
longitudinal axis ($y$). The curves highlight an homogeneous concentration in
the empty canyon and a spatial pattern with pronounced peaks in the vegetated
canyons. In the Full configuration, the peak concentration is approximately
twice the minimum concentration measured in the centre of the canyon. In Fig.
9, we also observe that the symmetry along the $y$-axis observed for the Zero
case (orange line) is disturbed in the vegetated configurations (blue and
green lines). The reason for this can be found in the irregular and non-
identical shape of the trees, and in their random orientation with respect to
their axis.

Finally, from the characterization of the concentration field (Figs. 6-8),
some observations about the local effect of trees on pollution can be deduced.
For both configurations with trees, the inhomogeneity along the longitudinal
axis ($y$) is maintained along the $x$-axis (Fig. 6), both near the two rows
of trees ($x/H\rightarrow 0$ and $x/H\rightarrow 2$) and in the centre
($x/H=$1). Furthermore, the number of concentration peaks (2 and 3 in the Full
and Half configurations, respectively) and their spacing do not correspond to
the number and spacing of the trees, represented by dashed lines in Figs. 6 to
8. These two aspects, namely the concentration inhomogeneity along the
$y$-axis in the centre of the street and the independence of the concentration
peaks on tree pattern, suggest that the variation of the concentration field
along the canyon is not due to local effects of trees acting as obstacles.
Rather, the presence of trees seems to modify the dynamics of flow and
dispersion within the whole canyon leading to a different spatial organization
of the concentration field at the canyon-scale.

Figure 6: Mean concentration of the passive scalar on the horizontal section
at $z/H$=0.6. Zero (a and d), Half (b and e) and Full (c and f) configurations
are shown. The position of trees is represented by dashed lines. Measurement
points are reported as circles colored according to the measured value. Figure
7: Mean concentration of the passive scalar on a vertical section at $x/H$=1.
Zero (a and d), Half (b and e) and Full (c and f) configurations are shown.
The position of trees is represented by dashed lines. Measurement points are
reported as circles colored according to the measured value. Figure 8: Mean
concentration of the passive scalar on a vertical section at $x/H$=0.15. Zero
(a and d), Half (b and e) and Full (c and f) configurations are shown. The
position of trees is represented by dashed lines. Measurement points are
reported as circles colored according to the measured value. Figure 9:
Concentration profile along the canyon axis ($x/H=1$) for the three
configurations of tree density. Each point is obtained as the average
concentration along the vertical axis (z).

###  4.2 Vertical exchange velocity

While the previous section highlighted the effect of trees on the spatial
pattern of the concentration field, in this section we investigate the effect
of vegetation on the ventilation efficiency of the canyon. To this aim, we
adopt a box model with one degree of freedom to evaluate the wash-out velocity
of the canyon for the different configurations of tree density.

A street canyon can be in general described as a unique box with volume-
averaged concentration, $C_{vol}$, and scalar flux through the boundary
surfaces at the roof height (of area $L\cdot W$) and at the lateral edges (two
rectangles of area $H\cdot W$). The mass balance for the canyon reads:

$V\dfrac{\partial C_{vol}}{\partial
t}+\int_{-L/2}^{L/2}\int_{0}^{W}\bigl{[}\overline{wc}\bigr{]}_{z=H}dxdy+\int_{0}^{H}\int_{0}^{W}\bigl{[}\overline{vc}\bigr{]}_{y=0}^{y=L}dxdz=Q_{et},$
(6)

where $V$ is the volume of the canyon, $Q_{et}$ is the mass flow rate of
ethane at the source, $\overline{wc}$ and $\overline{vc}$ are the mass fluxes
of passive scalar in the vertical and longitudinal directions, respectively.
The latter are provided by the time-averaged product of the instantaneous
vertical ($w$) or longitudinal velocity ($v$) and concentration $c$. In steady
state conditions (i.e. $\partial C_{vol}/\partial t=0$) and considering that
the lateral boundaries of the volume are solid walls (i.e. $\overline{vc}=0$)
in the configuration under study, the balance for the canyon (Eq. 6) becomes:

$\int_{-L/2}^{L/2}\int_{0}^{W}\bigl{[}\overline{wc}\bigr{]}_{z=H}dxdy=Q_{et}.$
(7)

Following the study of Soulhac et al. [63], the vertical flux at roof level
can be parametrized as the product of a bulk exchange velocity $u_{d}$ and the
difference between the concentration within the canyon ($C_{vol}$) and the
concentration in the external flow ($C_{ext}$):

$u_{d}(C_{vol}-C_{ext})WL=\int_{-L/2}^{L/2}\int_{0}^{W}\bigl{[}\overline{wc}\bigr{]}_{z=H}dydx.$
(8)

Eqs. 7 and 8 can be combined, and the exchange velocity expressed as [64]:

$u_{d}=\frac{Q_{et}}{C_{vol}WL}.$ (9)

or in non-dimensional form as:

$\frac{u_{d}}{U_{\infty}}=\frac{L_{s}\delta}{C_{vol}^{*}WL},$ (10)

where $C_{vol}^{*}$ is the non-dimensional concentration averaged over the
volume and $L_{s}$ is the source length inside the volume. Thanks to this
formulation, the vertical exchange velocity can be easily estimated from the
quantities measured in the experiment: the flow rate at the source $Q_{et}$ is
imposed and monitored by a mass-flow rate controller, while the FID
measurements inside the street canyon provide the average concentration in the
entire volume ($C_{vol}^{*}$).

In this regard, we recall that the choice of reproducing a canyon closed at
the lateral edges was made to simplify the estimate of the exchange velocity
$u_{d}$. Otherwise, in the case of a canyon with lateral street intersections,
the mass balance in Eq. 6 should be used instead of Eq. 7. As a consequence of
that, the flux of passive scalar along the $y-$direction would also appear in
Eqs. 9 and 10 and thus the estimate of $u_{d}$ would require additional
coupled measurements of velocity and concentration. In the case of an
infinitely long canyon (i.e. a canyon long as the wind-tunnel width), the
balance would be applied to a reference volume since the high resolution
measurement of the concentration over the entire length of the canyon would be
experimentally unfeasible. Also in this case, the estimate of the mass flux
$\overline{vc}$ at the lateral boundaries of the reference volume would be
necessary to compute $u_{d}$.

The mean concentration within the canyon was estimated with different spatial
averaging techniques of the point measurements: (i) the arithmetic mean of the
data, (ii) the mean weighted by the volume associated to each measurement
point, and (iii) the mean over a regular grid obtained from interpolation on
the concentration data. Table 1 reports the values of $C_{vol}^{*}$ obtained
with these different methods. The greatest differences in the estimate of
$C_{vol}^{*}$ are found for the Full configuration, where the measurement grid
is more irregular due to the presence of trees. In this case, the arithmetic
mean is expected not to be a good indicator as it does not take into account
the uneven spacing of the measuring points. This limitation is overcome by the
weighted average but its value can vary considerably depending on where the
measurements are taken with respect to the spatial variability of the
concentration field. Moreover, the choice of the weights could be not unique
and involve a certain degree of arbitrariness affecting the final value. The
interpolated concentration field can describe the spatial variations by
reconstructing (linearly) the concentration pattern even where the
measurements are not available. In this case, it is important that the
interpolation grid is sufficiently fine, at least as refined as the
measurement grid. Regardless the method used, the average level of pollution
does not show a trend with the number of trees, and the variation in
$C_{vol}^{*}$ between the configurations (up to 22$\%$) is much lower than the
spatial variations induced by the presence of vegetation (Figs. 5-8). We also
observe that the mean concentration in the Half configuration is higher than
in the Full configuration, where the number of trees is double. This is a
remarkable and unexpected result, as it is generally believed that trees,
acting as aerodynamic obstacles, are responsible for the accumulation of
pollutants in the street.

To better quantify the effect of trees on street canyon ventilation, we use
Eq. 10 to estimate the exchange velocity $u_{d}$, starting from $C_{vol}^{*}$.
The values of $u_{d}$ are also presented in Table 1 and show, once again, that
a trend of the ventilation efficiency with the vegetation density is absent,
being the Half configuration the one exhibiting the lowest exchange rate. We
also note that the estimated values of $u_{d}$ are higher with respect to
those presented by Salizzoni et al. [64], Soulhac et al. [63], and Fellini et
al. [14] for a square cavity ($H/W$=1), confirming that the enlargement of the
cavity enhances canyon ventilation.

The values reported in Table 1 have been estimated considering the entire
canyon as the reference volume. In Fig. 10, we show how the estimate of
$u_{d}$ varies as a function of the size of the reference volume. To this aim,
we consider a reference volume centred at $y$=0, extended to the entire width
($W$) and height ($H$) of the canyon, but of variable length ($L_{vol}$) along
the longitudinal axis ($y$). For the estimate of $u_{d}$ by means of Eq. 10,
the average concentration is computed as the mean of the measurement data
interpolated over a regular grid (Table 1) inside the reference volume, the
source length $L_{s}$ becomes the effective length included inside the
reference volume ($L_{s}^{\prime}$ in Fig. 10) and the length $L$ is replaced
by $L_{vol}$. For the empty canyon, the exchange rate is almost unchanged as
the reference volume increases. This is due to the homogeneity of the
concentration field along $y$. In the Full configuration, the velocity $u_{d}$
is greatly overestimated if a volume less than 40% of the total canyon is
considered. The reason for this is that, according to the balance in Eq. 10,
the lower concentration in the centre of the canyon results in a misleading
high ventilation efficiency. The Half configuration shows an intermediate
behaviour between the two. This analysis highlights the importance of a
characterization of the concentration field on an extended volume for a
correct evaluation of the overall ventilation efficiency. To analyse the
effect of the presence of trees in the street, characterizing a single two-
dimensional section or a limited volume in the middle of the canyon may lead
to misleading conclusions.

|  | Arithmetic mean | Weighted mean | Mean of interpolated data
---|---|---|---|---
$C_{vol}^{*}$ [-] | Zero | 154.84 | 161.32 | 162.96
Half | 197.40 | 185.56 | 190.61
Full | 179.54 | 147.86 | 182.32
$u_{d}/U_{\infty}$ [-] | Zero | 0.023 | 0.022 | 0.022
Half | 0.018 | 0.019 | 0.019
Full | 0.020 | 0.024 | 0.020
Table 1: Estimate of the volume-averaged (normalised) concentration
$C_{vol}^{*}$ for different spatial averaging techniques of the concentration
inside the canyon: (i) arithmetic mean, (ii) mean weighted on the reference
volume of each measurement point, and (iii) mean of the three-dimensional
interpolated concentration field. The corresponding values of the vertical
exchange velocity $u_{d}/U_{\infty}$, calculated by means of Eq. 10, are also
reported.  Figure 10: Estimate of the exchange velocity $u_{d}$ as a function
of the length ($L_{vol}$) of the reference volume along the longitudinal axis
of the canyon.

##  5 Discussion about the effect of lateral boundaries

As mentioned above, the canyon geometry with closed lateral borders was
adopted to ensure a correct and straightforward estimate of the ventilation
efficiency. However, this geometry is unusual compared to classic experimental
investigations (and realistic urban geometries) and could raise the question
if the insertion of side walls radically alters the flow field, and therefore
the dispersion process, in the canyon. For this reason, in this section we
investigate whether the interesting (and partially counter-intuitive) effects
of trees on the concentration field, observed in Section 4.1, may have been
forced by the presence of the side walls. To do this, we compare the results
presented in the previous sections with the concentration measurements
performed in identical experimental conditions, but with different geometries
at the lateral borders. We note that these measurements were carried out on a
coarser grid with respect to that adopted for the investigation of the closed
canyon. While these measurements provide a qualitative description, a deep
analysis of the influence of lateral boundaries on the concentration field
goes beyond the scope of this paper.

In the first laterally-open geometry [65], the reference canyon is part of the
network of streets that reproduces the idealized urban district. The width
($W=0.2$ m) and height ($H=0.1$ m) of the canyon are the same as in the
laterally closed geometry, while its length is 0.5 m as the canyon is limited
laterally by two street intersections (Fig. 11). As a consequence, the length-
to-width and length-to-height ratios are half compared to the laterally closed
geometry and equal to $L/W=2.5$ and $L/H=5$, respectively. Although the
length-to-width ratio is lower and a regular array of building is here
present, the street geometry is similar to that adopted by Gromke and Ruck
[19, 20], where a laterally open canyon is reproduced. Concentration
measurements were performed over 4 cross-sections (sections 1 to 4) placed at
$y/H=\pm 1.88$ and $y/H=\pm 0.63$. Results for an empty canyon and a canyon
with a full density of trees are presented in panels b and d of Fig. 11,
respectively. The density of trees corresponds to the Full configuration
presented in Section 2.1. Panels c and e refer to the canyon with closed
lateral edges (object of study in the previous sections) and report the
concentration in the $y/H$ positions corresponding to sections 1 to 4. As the
measurements with lateral walls were not carried out in the exact same
positions, we adopted the concentration values obtained from the spatial
(linear) interpolation of the point measurements. Without trees, pollution
levels are significantly lower in the open canyon (panel b) with respect to
the laterally closed one (panel c). This is an expected result as the
formation of corner eddies near the intersections provide additional turbulent
and advective exchange that favours the canyon ventilation. Despite this
variation in the pollution levels, we observe that the concentration in panel
b remains almost unchanged along the longitudinal axis. This suggests that the
concentration field (up to $y/H=\pm 1.88$, i.e. sections 1 and 4) in the empty
canyon with street intersections is homogeneous along the $y-axis$, as found
for the empty canyon with laterally closed edges (panel c). Also in the
vegetated canyon, the concentration tends to be lower in the canyon with
lateral intersections (panel d) with respect to the laterally closed canyon
(panel e). We also observe that the concentration trend along $y$ is similar
for panels d and e, with sections 1 and 4 exhibiting an increase in the
concentration with respect to section 2 and 3. This trend is in line with the
results found in Section 4.1 and suggests that trees induce a three-
dimensional concentration field. The scatterplot in Fig. 11.f compares the
concentration measurements in the two different geometries (closed edges and
lateral street intersections) for the different sections (different markers)
and for the different tree densities (different colours). This figure points
out that the concentration is higher when the lateral ends are closed. This is
especially true for the non-vegetated case, where point concentrations
increase by over 100%. Moreover, the scatterplot highlights a good correlation
between the concentration values, with the points that, despite the expected
translation along the $y$-axis, follow a linear trend. This unexpected good
correlation of point concentration in the most part of the domain of two
significantly different geometries may be due to the shape of the street
intersections and the pattern of the urban canopy: the decelerated
longitudinal wind along the narrow ($H/W$ = 1) street canyons generates weak
corner eddies which therefore have limited effect on the flow and
concentration fields inside the canyon.

A similar comparison is performed for the geometry presented in Fig. 12.a. In
this case, the canyon is extended to the entire width of the wind tunnel
($L/H=35$ and $L/W=17.5$). The other geometrical properties of the canyon, as
well as the experimental conditions, are the same as presented in Section 2.1.
Concentration measurements are available in four cross-sections placed at
$y/H=\pm 1.07$ and $y/H=\pm 0.36$. As for Fig. 11, we compare the
concentration measurements in the four sections (Fig. 12.b and d) with the
interpolated concentration in the same positions for the geometry with closed
lateral edges (Fig. 12.c and e). Also for the laterally open canyon (panel a),
the concentration field shows homogeneity along the $y-axis$ in the absence of
trees (panel b), while in the Full configuration (panel d) the concentration
tends to increase in sections 1 and 4 which are located near the two peaks
evident in Fig. 9. The comparison between panels b-c together with the scatter
plot in Fig. 12.f highlight a good agreement between point concentration in
the two different geometries (laterally closed and open canyons) for the case
without vegetation. This suggests that the lateral walls have a negligible
effect on the flow field and dispersion in the canyon, at least up to $y/H=\pm
1.07$. Lower agreement is found in the vegetated canyon and especially in
section 4. As already highlighted in the previous section, the concentration
field is strongly affected by the trees that are heterogeneous in shape and
placed with random orientation in the canyon. These features can therefore
lead to slight asymmetries in the concentration field. The observed
differences are more pronounced when comparing point-to-point values in areas
with a high concentration gradient, such as in section 4 (located near a
peak).

The comparisons discussed in this section suggest that, for the analysed
different geometries of lateral ends, the presence of trees in the street
induces the inhomogeneity of the concentration field along the $y-$axis. The
closure of the lateral ends with respect to the laterally open canyon (Fig.
12) has negligible effect on the concentration pattern in the case of the
empty canyon, while moderate to significant differences in the vegetated
canyon may be induced by the shape and orientation of the trees. The geometry
with lateral intersections, on the other hand, presents more marked
differences but the presence of trees seems, even in this case, to trigger the
transition from a two-dimensional to a three-dimensional concentration field.

Figure 11: (a) Canyon with street intersections at the lateral edges.
Concentration in 4 cross-sections for the configuration without trees (b) and
with dense trees (d). Comparison with the concentration in the closed cavity,
in the Zero (c) and Full (e) configurations. (f) Comparison of point
concentration in the two geometries for the different sections (different
markers) and for the different tree densities (different colours). Figure 12:
(a) Canyon extended to the width of the wind tunnel. Concentration in 4 cross-
sections for the configuration without trees (b) and with dense trees (d).
Comparison with the concentration in the closed cavity, in the Zero (c) and
Full (e) configurations. (f) Comparison of point concentration in the two
geometries for the different sections (different markers) and for the
different tree densities (different colours).

##  6 Conclusions

By means of a high-resolution experimental investigation, we have analysed the
effect of trees on ventilation efficiency and pollutant dispersion in an urban
street canyon.

The results show that the concentration field is homogeneous along the
longitudinal axis of the canyon in the Zero configuration, and can be
considered nearly two-dimensional. On the other hand, the vegetated streets
present a remarkable three-dimensional spatial distribution of the
concentration field. In particular, concentration peaks alternating with low
polluted regions are observed along the canyon both in the Half and Full
configurations and especially in the lower part of the canyon, suggesting high
spatial variation in pedestrian exposure. Moreover, the presence of trees lead
to a significant increase of pollutant concentration at the upwind wall of the
street, while the average concentration at the downwind wall is almost
constant in the different configurations. Despite the great influence of trees
on the spatial pattern of pollution, the average concentration in the entire
volume of the canyon does not show similar significant variations.
Additionally, the Half configuration has the highest volume averaged
concentration, suggesting that a trend with the tree density is absent.

The ventilation efficiency of the canyon was quantified by estimating the bulk
transfer velocity between the canyon and the boundary layer aloft. To this
aim, a mass balance within the laterally closed canyon was performed. We
demonstrated that the characterization of the concentration field over an
extended reference volume is necessary to accurately estimate the transfer
velocity when trees are present.

Finally, we investigated whether the effect of vegetation on the concentration
field is similar in street canyons with different lateral boundaries. As
expected, the presence of lateral street intersections increases the
ventilation of the canyon and therefore decreases the pollution levels.
However, also in this geometry, the presence of trees in the canyon seems to
induce inhomogeneities of the concentration field along the canyon axis. In
the case of a canyon that extends to the entire width of the wind tunnel, the
concentration pattern is quite similar to that observed in the configuration
with closed ends. This comparison suggests that the results of this study can
be generalized to the case of an infinitely long street canyon, which is a
classical geometry adopted in both experimental and numerical studies.

To conclude, despite a detailed description of the concentration field at the
pedestrian level was not carried out and trees hinder the measures for a
complete street canyon characterization, we showed that trees have a non-
trivial effect on the spatial distribution of the pollutant concentration,
leading to a highly inhomogeneous scalar field and strong pollution gradients
in the bottom part of the canyon. This suggests that the presence of trees
affects the turbulent flow field within the canyon. A new measurement campaign
is currently underway to investigate the reasons for the formation of
pollution peaks in the presence of trees.

Moreover, we found that the presence of two rows of trees has moderate (up to
$20\%$) effect on the vertical exchange velocity. On the one hand, this
suggests that the estimated value of the vertical exchange velocity could be
included, with good approximation, as a constant in parametric models that
simulate pollutant dispersion in cities [66, 67, 68]. On the other hand, the
strong spatial gradients of pollution level due to the presence of trees put
into question the reliability of the description of the street canyon as a
single box with homogeneous concentration, especially when the focus is on
pedestrian exposure.

Finally, the huge dataset provided by this experimental study can be of great
use for the validation of numerical simulations. To this end, the
characterization of the boundary layer and of the aerodynamic properties of
trees (described in the Section 3) are fundamental information.

## Data availability

The experimental dataset is available on the website:
https://github.com/sfellini/Tree_alpha90_HW05.git. We provide the
concentration data inside the canyon and the characterization of the flow
field above the buildings.

## Aknowledgements

We would like to express our gratitude to Horacio Correia for the technical
support in performing the wind tunnel experiments, and to Alessandro De
Giovanni, Giacomo Balestrieri, and Lucas Mechinaud for taking part in the
experimental campaign.

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