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DOI : 10.1016/j.jweia.2020.104160
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Effects of wind speed and atmospheric stability on the air pollution reduction rate
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induced by noise barriers
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Nicolas Reiminger1,2*, Xavier Jurado1,2, José Vazquez2, Cédric Wemmert2, Nadège Blond3,
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Matthieu Dufresne1, Jonathan Wertel1
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1AIR&D, 67000, Strasbourg, France
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2ICUBE Laboratory, CNRS/University of Strasbourg, 67000, Strasbourg, France
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3LIVE Laboratory, CNRS/University of Strasbourg, 67000, Strasbourg, France
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*Corresponding author: Tel. +33 (0)6 31 26 75 88, Mail. nreiminger@air-d.fr
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Please cite this paper as : Reiminger, N., Jurado, X., Vazquez, J., Wemmert, C., Blond, N., Dufresne,
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M., Wertel, J., 2020. Effects of wind speed and atmospheric stability on the air pollution reduction rate
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induced by noise barriers. Journal of Wind Engineering and Industrial Aerodynamics 200, 104160.
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https://doi.org/10.1016/j.jweia.2020.104160
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ABSTRACT
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People around the world increasingly live in urban areas where traffic-related emissions can
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reach high levels, especially near heavy-traffic roads. It is therefore necessary to find short-term
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measures to limit the exposure of this population and noise barriers have shown great potential
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for achieving this. Nevertheless, further work is needed to better understand how they can act
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on pollution reduction. To do this, a Reynolds-Averaged Navier-Stokes model that takes into
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account thermal effects is used to study the effects of wind speed and atmospheric stability on
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the concentration reduction rates (CRR) induced by noise barriers. This study shows that the
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CRR behind the barriers may depend on both wind and thermal conditions. Although only the
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wind direction, and not the wind speed, has an impact on CRR in a neutral atmosphere, this
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parameter can be changed by both wind speed and thermal variations in non-neutral
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atmospheres. Stable cases lead to a higher CRR compared to unstable cases, while the neutral
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case gives intermediate results. Finally, it is shown that the variation of CRR is negligible for
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Richardson numbers ranging from -0.50 to 0.17.
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Keywords: Computational fluid dynamics, Noise barrier, Air pollution, Wind speed, Thermal
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stratification
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1. Introduction
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Nowadays, more than one in two people live in urban areas with 82% in the United States and
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74% in Europe, and this percentage will continue growing to reach 68% worldwide in 2050
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(United Nations, Department of Economic and Social Affairs, Population Division, 2019).
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Traffic-related emissions can reach high levels in such areas, particularly near heavy-traffic
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roads. Concentrations of air pollutants such as nitrogen dioxide (NO2) and particulate matter
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(PM) can reach high values in the vicinity of this kind of road and lead to several diseases
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(Anderson et al., 2012; Kagawa, 1985; Kim et al., 2015). In addition, it has been shown that
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people living near these roads are more likely to be at risk (Chen et al., 2017; Finkelstein et al.,
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2004; Petters et al., 2004). In Europe, emissions and therefore concentrations of air pollutants
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are expected to decrease in the future as air quality regulations increase and actions are taken
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(European Commission, 2013). Nevertheless, it will take time to achieve a significant decrease
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and, in the meantime, many people will still live in areas where air quality is poor. It is now
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necessary to find ways to limit exposure to air pollution for people living near busy roads and
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to better understand solutions that have already been found, like noise barriers.
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Noise barriers are civil engineering elements located along roadways and designed to protect
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inhabitants from noise pollution. These elements, often placed between heavy-traffic roads and
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residences, also have a beneficial impact on air quality. Indeed, several authors have
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investigated the efficiency of noise barriers in reducing atmospheric pollutant concentrations
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behind the barriers using in-field (Baldauf et al., 2008, 2016; Finn et al., 2010; Hagler et al.,
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2012; Lee et al., 2018; Ning et al., 2010), wind tunnel (Heist et al., 2009) measurements and
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numerical models (Bowker et al., 2007; Hagler et al., 2011; Schulte et al., 2014). Some authors
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have studied the effects of barrier heights and distances on pollution reduction (Amini et al.,
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2018; Gong and Wang, 2018). Other authors have studied the effects of barrier shapes and
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locations on improving the reduction of atmospheric pollutants (Brechler and Fuka, 2014;
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Enayati Ahangar et al., 2017; Wang and Wang, 2019). However, although some of these works
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have been performed by considering different atmospheric stabilities, knowledge is lacking on
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how the combination of wind conditions and thermal effects can affect pollutant reductions
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behind barriers. Further work is thus required in this direction.
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The aim of this work is to study the combined effects of wind and thermal effects on the
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reduction of pollutant concentrations behind the noise barrier. More specifically, computational
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fluid dynamics (CFD) simulations are used to assess the evolution of the concentration
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reduction rate behind noise barriers for several wind speeds and atmospheric stabilities, ranging
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from very unstable to stable conditions, including all the intermediate conditions (unstable,
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slightly unstable, neutral and slightly stable). The two key parameters of this study are defined
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and described in Section 2. The numerical model, including the governing equations, boundary
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conditions and model validation used in this work, is presented in Section 3. The results of the
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study are presented in Section 4, after which these results are discussed in Section 5.
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2. Description of the study
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This paper examines the impact of wind speed and atmospheric stability on the reduction of
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downwind air pollution induced by the presence of noise barriers. It is therefore necessary to
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define two recurring parameters: the Richardson number and the concentration reduction rate.
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The thermal effects can be quantified using the Richardson number noted . The
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corresponding equation taken from (Woodward, 1998) is given in (1).
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where is the gravitational acceleration, is the noise barrier height, is the reference
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velocity (which is the velocity at in this study), is the ambient temperature, is
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the temperature at , and is the surface temperature of the heated ground. The difference
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will be noted in the following.
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The Richardson number is also an indicator of atmospheric stability: corresponds to
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isothermal (neutral) cases, corresponds to unstable cases, and to stable cases. A
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better discretization of atmospheric stability, related to Pasquill’s stability classes, also exists
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(Woodward, 1998) and is summarized in Table 1
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Table 1. Atmospheric stability correlated with the Richardson number (Woodward, 1998).
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Atmospheric stability
Richardson number
Very unstable
Unstable
Slightly unstable
Neutral
Slightly stable
Stable
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The reduction of the pollution behind the noise barriers compared to an area without these
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barriers is quantified using an indicator called concentration reduction rate () given in (2).
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where is the concentration with a noise barrier and is the reference concentration
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corresponding to the same case but without noise barriers.
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The provides information on both the positive and negative impact of noise barriers
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( > 0 means that noise barriers reduce downwind pollution; < 0 means that noise
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barriers increase downwind pollution) and their effectiveness ( = 40% means that the
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concentration behind noise barriers is reduced by 40% compared to the same case without
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them).
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3. Numerical model
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3.1. Governing equations
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Simulations were performed using the buoyantPimpleFoam solver from OpenFOAM 6.0. This
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transient solver is able to resolve Navier-Stokes equations for buoyant and turbulent flows of
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compressible fluids including the effects of forced convection (induced by the wind) and natural
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convection (induced by heat transfers). The corresponding continuity (3), momentum (4) and
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energy (5) equations are given below:
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104
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where is the velocity, the pressure, the density, the thermal energy, the rate of
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strain tensor given in (6), the kinetic energy given in (7), the gravitational acceleration,
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the effective viscosity defined as the sum of molecular and turbulent viscosity and
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the effective thermal diffusivity defined as the sum of laminar and turbulent thermal
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diffusivities.
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No chemical reactions are considered in this study. Thus, the equation governing passive scalar
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transport (8) has been added to the solver. This advection-diffusion equation is given below:
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where C is the pollutant concentration, the molecular diffusion coefficient, the turbulent
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diffusivity, the turbulent Schmidt number and the source term of the pollutants
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(emissions).
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A Reynolds-averaged Navier-Stokes (RANS) methodology was used to resolve the equations.
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When using this methodology, a new term called Reynolds stress tensor appear and it is
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necessary to choose a turbulence model to resolve it. The RNG k-ε turbulence model proposed
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by Yakhot et al. (1992) has been selected because it gives significant improvements compared
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to the standard turbulence model for recirculatory flows (Papageorgakis and Assanis, 1999),
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whereas anisotropic models such as the Reynolds Stress Model (RSM) may not improve the
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results (Koutsourakis et al., 2012) for a higher calculation cost and more calculation
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instabilities.
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Each simulation was performed using second order schemes for all the gradient, divergent and
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Laplacian terms. The streamwise velocity U and the pollutant concentration C were monitored
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for several locations behind the downwind noise barrier and the results were checked to ensure
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that each simulation has converged. At the end of the simulations, all the residuals were under
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10-5.
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3.2. Computational domain and boundary conditions
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This study focuses on the concentration reduction rates induced by the presence of noise
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barriers. Thus, to quantify this reduction, two distinct cases have to be considered in terms of
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computational domain: a case with noise barriers and a case without them. Fig. 1 shows a sketch
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of the computational domain and the boundary conditions used for the case with noise barriers.
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The second case is strictly the same but without the noise barriers.
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Fig.1. Sketch of the computational domain with H = 0.5 m.
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The recommendations given by Franke et al. (2007) were followed concerning the boundary
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conditions and domain size. The inlet boundary is localized 10H before the upwind noise barrier
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where velocity, turbulence and temperature profiles are specified using a perpendicular wind
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direction, unless otherwise stated. The outlet boundary is placed 40H behind the downwind
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noise barrier with a freestream condition to allow the flow to fully develop. Symmetry
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conditions are applied for the upper and lateral limits, with the top of the calculation domain
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placed 20H from the ground and the lateral limits located 20H from each other. No-slip
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conditions are applied to any other boundaries including the ground and the two noise barriers,
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where the temperature can be specified to simulate stable and unstable cases. Finally, traffic
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exhausts are modeled by two volumetric sources along the y-direction, with a width of 1.4H
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each, and over one mesh height (0.25 m) where an emission source term is added in the pollutant
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transport equation. A mass flow rate of 100 g/s is used for all the simulations performed. Further
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information can be found in Table 2.
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Table 2. Summary of the boundary conditions.
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Inlet
Velocity and turbulence profiles are calculated according to
Richards and Norris (2011):
(9)
(10)
(11)
with U the wind velocity, k the turbulent kinetic energy (TKE), ε
the dissipation of TKE, the friction velocity, the von Kármán
constant taken to 0.41, z the altitude, z0 the roughness height taken
as 0.5 m, and a CFD constant taken as 0.085.
Fixed temperature: Tair = 293 K.
Outlet
Freestream outlet.
Top
Symmetry plane.
Lateral surfaces
Symmetry plane.
Ground and noise
barriers surfaces
No-slip condition (U = 0 m/s).
Fixed temperature (Tw) depending on the case studied.
Emission
Surface source with emission rate qm = 100 g/s.
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Mesh sensitivity tests were carried out to ensure that the results are fully independent of mesh
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size. Successive simulations were performed with different mesh sizes and the Grid
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Convergence Index (GCI) methodology (Roache, 1994) was used to assess the mesh-related
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errors on both the flow field and the concentration field. Mean GCIs of 2% and 1% were
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obtained for flow and concentration fields, respectively, when comparing the results from mesh
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sizes of 0.5 m and 0.25 m. Thus, a mesh size of 0.5 m was considered sufficient to avoid
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excessive calculation costs and was used for the study. This mesh size corresponds to the
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meshes localized between an altitude of z = 0 and z = 2H. However, greater refinement was
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applied near the noise barrier walls and the road because of the strong gradients that can occur
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in these areas. This mesh size resulted in a total of 2.6 million meshes and an illustration of the
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meshes selected is provided in Figure 2. The meshing was done using the unstructured grid
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generator snappyHexMesh available with OpenFOAM.
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Fig.2. Grid selected for computation.
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3.3. Model validation
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The numerical model was compared against the experimental data proposed by Cui et al. (2016)
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because they provided results on both velocity and the concentration field for a complex 3D
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situation. Indeed, the experiment setup consists of two buildings with the downwind building
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being higher than the upwind building. A gas is released at the top of the upwind building and
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the ground between the two buildings is heated to simulate several atmospheric stabilities and
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heat exchanges. The downwind building is opened and closed by two windows to simulate
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indoor/outdoor pollutant exchanges.
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Fig. 3 shows the comparison between the experimental data and the numerical model used in
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this study for a stable case where Ri = 1.22 (Ufree stream = 0.7 m/s and = 135 °C) and for a
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vertical profile localized between the two buildings. These results are presented in a
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dimensionless form that can be found in the paper of Cui et al. (2016). The results show good
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agreement between the numerical model and the experimental data on both velocity and
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concentration profiles, with a mean difference of 6% between the experimental and numerical
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concentration profiles. The numerical model is therefore capable of accurately reproducing
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velocity and concentration profiles in a 3D case with a high thermal gradient. According to
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these results, the numerical model was considered validated for the purpose of this study.
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Fig.3. Vertical distribution of dimensionless velocity and concentration for Ri = 1.22 given by Cui et al. for the wind tunnel
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measurements (Cui et al., 2016), and comparison with the CFD model with Sct = 0.25.
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4. Results
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Several simulations were performed to study the combined effects of wind speed and thermal
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effects on the concentration reduction rate behind the barriers. All the simulations performed
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with their specific conditions (UH and ) and their corresponding Richardson numbers are
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given in Table 3. Each of these conditions was simulated twice to obtain results with and
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without noise barriers to calculate the concentration reduction rates. A total of 64 simulations
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were carried out including:
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- 24 simulations for the neutral case (6 simulations for each of the three turbulent Schmidt
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numbers considered to assess their impact on the concentration reduction rates and 6
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supplementary simulations for a non-perpendicular case);
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- 20 simulations for the stable cases;
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- 20 simulations for the unstable cases.
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All the results were extracted at the center of the computational domain along y/H = 0 with
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x/H = 0 corresponding to the end of the downwind noise barrier wall.
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Table 3. Summary of the simulations performed with wind velocity and thermal conditions () and
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their corresponding Richardson numbers.,
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UH [m/s]
1.18
1.96
3.15
5.51
7.87
Ri [-]
0
ΔT = 0 K
ΔT = 0 K
ΔT = 0 K
0.06
ΔT = 10 K
0.17
ΔT = 10 K
ΔT = 30 K
ΔT = 62 K
0.33
ΔT = 7.5 K
ΔT = 19.5 K
0.50
ΔT = 11.5 K
ΔT = 29.5 K
1.20
ΔT = 10 K
ΔT = 27.5 K
-0.06
ΔT = -10 K
-0.17
ΔT = -10 K
ΔT = -30 K
ΔT = -62 K
-0.50
ΔT = -11.5 K
ΔT = -29.5 K
-0.75
ΔT = -17.5 K
ΔT = -44.5 K
-1.20
ΔT = -10 K
ΔT = -71 K
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4.1. Study without thermal effects
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4.1.1. Turbulent Schmidt number sensitivity
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The turbulent Schmidt number (Sct) is a dimensionless number found in air pollution CFD to
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consider the effect of turbulent diffusivity. However, this number is widely spread between 0.2
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and 1.3, depending on the situation studied, and can significantly change the results in terms of
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concentration (Tominaga and Stathopoulos, 2007). To assess the effect of this parameter on
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noise barrier studies, three Sct were considered: 0.3, 0.7 and 1.1.
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The evolutions of the CRR behind the barriers for the three Sct considered and for four altitudes
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(z = 0.25H, 0.50H, 0.75H and 1.00H) are presented in Fig. 4 as a function of the dimensionless
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distance from the downwind noise barrier x/H. The results show considerable variability for the
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concentration reduction rate as a function of the turbulent Schmidt number and no general trend
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can be observed. Indeed, while for Sct = 1.1 and z = 0.25H the CRR is globally higher than for
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other turbulent Schmidt numbers, for the three other altitudes the CRR is not globally higher.
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Additionally, while the CRR is globally lower with Sct = 0.3 and z = 0.25H, this observation is
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no longer true for the other altitudes. Moreover, the turbulent Schmidt number has also an
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impact on the distance after the barriers were there is a positive impact of the noise barriers
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(CRR > 0), this distance being higher for higher Sct.
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Fig.4. Evolution of the concentration reduction rate behind the downwind wall as a function of Sct and for several altitudes
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with the same wind profile (UH = 1.18 m/s).
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According to these results, it is important to state that the turbulent Schmidt number is also a
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very sensitive parameter when studying the impacts of noise barriers and its choice should be
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considered carefully, especially when performing quantitative studies. For the rest of this paper,
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and since no information or studies to determine the best turbulent Schmidt number for noise
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barrier studies are available an intermediate turbulent Schmidt number of 0.7 is used and the
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results are presented qualitatively rather than quantitatively.
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4.1.2. Impact of wind speed and wind direction on the CRR in neutral atmosphere
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The impact of wind speed and wind direction on the concentration reduction rate was first
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studied in neutral atmosphere, thus, considering only forced convection (i.e. convection due to
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the wind).
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Fig. 5 shows the evolution of the pollutant concentrations behind the barriers for the cases with
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and without barriers (A) and the corresponding concentration reduction rates (B) as a function
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of the wind speed at z = 0.25H. According to Fig. 5 (A), regardless of the wind speed and for
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z = 0.25H, pollutant concentrations were generally higher without the noise barrier than with it.
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Additionally, concentrations changed inversely with wind speed, leading to lower
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concentrations for higher wind speeds. The concentrations were thus different as a function of
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this parameter. However, as depicted in Fig. 5 (B), the CRR is the same whatever the wind
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speed considered and this is also true for the other altitudes considered (z = 0.5H, 0.75H and
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1.00H). This result is linked to the fact that, for a given wind direction and without thermal
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stratification, the concentration was inversely proportional to the wind velocity (Schatzmann
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and Leitl, 2011). Thus, since the concentration evolved in the same way with wind speed both
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with and without noise barriers, the CRR remained unchanged for a given wind direction under
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neutral conditions.
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The effects of the wind direction under neutral conditions were also investigated and the results
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are presented in Fig. 6 for a perpendicular wind (90°) and a wind oriented at 60°. Fig 6 (A)
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shows that for the 60° case, the concentrations are lower with the noise barriers and higher
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without the noise barriers compared to the perpendicular case. This inevitably leads to a lower
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CRR for the perpendicular case, as shown in Fig. 6 (B) for z = 0.25H and z = 0.75H. The same
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result was obtained for z = 0.50H and z = 1.00H. Therefore, the CRR are higher for oblique
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wind directions.
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Fig.5. Evolution of the concentrations with and without noise barriers (A) and the concentration reduction rates (B) as a
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function of wind speed for a perpendicular wind direction at z = 0.25H.
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Fig.6. Evolution of the concentrations with and without noise barriers (A) and the concentration reduction rates (B) as a
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function of the wind direction and for a given wind speed (UH = 3.15 m/s).
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According to the previous results, when studying the CRR behind noise barriers for neutral
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cases, it is necessary to study only one wind speed for each wind direction. Moreover, if the
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minimal CRR is assessed, the study can be reduced to only one direction. Indeed, the
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perpendicular direction leads to the lowest CRR while the non-perpendicular directions lead to
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higher CRR.
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4.2. Study with thermal effects
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4.2.1. Evolution of the CRR as a function of the atmospheric stability
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The concentration reduction rate was then studied considering mixed convection: forced
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convection induced by wind speed and natural convection induced by thermal stratifications.
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The CRR was therefore studied as a function of the Richardson number which includes wind
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speed (UH) and thermal variations ().
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The first results are presented in Fig. 7 for three different Richardson numbers: (A) Ri = -0.17
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corresponding to a stable atmosphere; (B) Ri = 0 corresponding to a neutral atmosphere; and
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(C) Ri = -0.17 corresponding to a slightly unstable atmosphere, for the same wind conditions
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(perpendicular wind with UH = 3.15 m/s). Thus, is the only variable here. For the three cases
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considered, the concentration is highest directly behind the barriers (x = 0 m), just above them
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(h = 5 m) and generally decreases as the distance from the barrier increases or the height
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decreases. However, the concentrations are different depending on the case. Indeed, the
290
concentrations are lowest for the stable case (A) and highest for the slightly unstable case (C).
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The neutral case (B) leads to intermediate results but closer to the unstable one. For a given
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wind speed and direction, thermal effects therefore have a high impact on the concentration
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behind the barriers and seem to have a greater impact for > 0 than for < 0.
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Fig.7. Evolution of the concentration behind the downwind barrier as a function of the temperature variation in the same
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wind conditions (perpendicular wind, UH = 3.15 m/s).
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The evolution of the CRR as a function of the distance from the downwind barrier was studied
299
for several atmospheric stabilities by changing both the wind speed (UH) and the thermal
300
variation (∆T). The results for Ri = -1.20, -0.17, -0.06, 0.00, 0.17, 0.06 and 1.20 are given in
301
Fig. 8 for z = 0.25H (A), 0.50H (B), 0.75H (C) and 1.00H (D). Further results are presented in
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Fig. 8 (E) and correspond to the CRR averaged over z for z ranging from 0 to 5 m giving global
303
information along the height of the noise barriers.
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As can be seen in Fig. 8, the evolution of the CRR follow the same trends. Indeed, for all the
305
altitudes considered and also for the CRR averaged over z = H, the results for the neutral case
306
are bounded by the results for the stable cases and the unstable cases: the unstable cases lead to
307
lower CRRs compared to the neutral case, with the lowest CRR being obtained for the highest
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unstability level (Ri = -1.20). On the contrary, the stable cases lead to higher CRRs with the
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highest CRR being obtained for the highest stability level (Ri = 1.20). However, the evolution
310
of the CRR according to the level of stability or unstability is not equivalent between the two
311
cases. Indeed, whereas the results are different for the three unstable cases presented in Fig. 8,
312
the CRR for the two highest stable cases (Ri = 0.17 and Ri = 1.20) are very similar. Furthermore,
313
the CRR changes more quickly as a function of the Richardson number for the stable cases than
314
for the unstable cases, which is consistent with the previous results discussed in relation with
315
Fig. 7. Thus, atmospheric stability has an impact on the CRR, leading to higher CRRs for stable
316
cases (Ri > 0), quickly reaching maximum values, while lower CRRs are obtained for unstable
317
cases (Ri < 0) and no maximum values were reached for the Richardson numbers considered in
318
this study.
319
Fig. 8 also shows that the CRR not only depends on the distance from the barriers but also on
320
their height. For a given atmospheric stability, the CRR decreases with height and can reach
321
negative values corresponding to an increase in pollutant concentration due to the barriers.
322
These observations are related to the heights at which the plumes spread in both configurations,
323
with and without the barriers. Indeed, without the noise barriers the plume spreads along the
324
ground, leading to lower concentrations at z = H, while with the noise barriers the plume spreads
325
from the top of the barriers and the concentrations are generally lower at ground level compared
326
to the case without barriers.
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Fig.8. Evolution of the concentration reduction rates for 4 given altitudes (A—D) and averaged over the noise barrier height
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(E) as a function of the distance from the downwind barrier and for several Richardson numbers.
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4.2.2. Conservation of the CRR with the Richardson number
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It has been shown previously that the concentration reduction rate for a given wind direction is
333
constant when considering only forced convection (neutral atmosphere) due to an inversely
334
proportional link between the pollutant concentrations and the wind speed. However, this link
335
is no longer valid when considering both forced and natural convection. The question was then
336
to assess if the CRR is still constant for stable and unstable cases. To answer this question,
337
several simulations were performed for numerous Richardson numbers but with distinct couples
338
of wind speed and thermal variations. The Richardson numbers considered were Ri = -1.20,
339
-0.75, -0.50, -0.17, -0.06, 0.00, 0.06, 0.17, 0.33, 0.50 and 1.50.
340
Fig. 9 (A) shows the evolution of the CRR for three couples of UH and ∆T giving Ri = -0.17
341
(slightly unstable atmosphere) while Fig. 9 (B) shows the evolution of the CRR for two couples
342
giving Ri = 0.50 (stable atmosphere). According to Fig. 9 (A), the CRR can be constant for a
343
given Ri. Indeed, with Ri = -0.17, while the pollutant concentrations are not the same for the
344
three couples of UH and ∆T considered, the CRR is quasi-constant (± 3%). However, this
345
observation is not true for all the Richardson numbers according to Fig. 9 (B), which shows that
346
for Ri = 0.50 the CRRs are significatively different for the two couples of UH and ∆T considered.
347
Thus, the CRR can be constant for a given Ri but this is not generalizable.
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The Richardson numbers for which the CRR can be considered constant were assessed and the
349
results are presented in Fig. 10. The results show that, for a Ri ranging from -0.50 to 0.17, the
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variation over the CRR is less than 3% and the CRR can be considered as constant for a given
351
Ri. For Richardson numbers outside this range, the variation over the CRR for a given Ri can
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reach 15% for a Ri ranging from -0.75 to -0.5 and 30% for a Ri ranging from -0.75 to -1.20 and
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from 0.17 to 1.20. According to these results, for a given Ri ranging from -0.50 to 0.17, a unique
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couple of UH and ∆T must be considered when assessing the concentration reduction rates
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behind noise barriers in non-neutral cases.
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Fig.9. Evolution of the concentration reduction rate for Ri = -0.17 (A) and Ri = 0.50 (B) as a function of wind speed (UH) and
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thermal variation (∆T) at z = 0.25H and z = 0.50H.
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360
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Fig.10. Conservation of the concentration reduction rate with the Richardson number.
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5. Discussion
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This study provides better understanding of how noise barriers can reduce air pollution and how
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this reduction can vary with wind conditions and atmospheric stability. Additional work can be
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done to further improve this understanding and is discussed below, as is the relevance of these
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results.
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It was shown that for a given Ri ranging from -0.50 to 0.17, variations over the CRR are
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negligible. Moreover, the evolution of the CRR as a function of distance from the downwind
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barrier seemed to follow the same trends, as the curves appear the same. Thus, it may be
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possible to find relationships between the CRR and the Richardson number in the range -0.50
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to 0.17. If such relationships can be found, it will allow estimating all the CRRs in this Ri range
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by performing only one simulation, or with only one in-field measurement.
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This study considered only one noise barrier configuration, with two walls of the same height
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placed on either side of a heavy-traffic road. Further studies could be performed to verify if the
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results obtained for this configuration are generalizable, for example for noise barriers with
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only one upwind or downwind wall and also with a combination of solid and vegetative barriers.
377
Finally, according to the results of this study, further studies can be simplified. Indeed, for
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future studies in neutral atmosphere (without thermal variations), they could be reduced to only
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wind direction and noise barrier configuration studies when assessing the evolution of the CRR.
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For studies including mixed convection (with thermal variations), for a Ri ranging from -0.50
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to 0.17, only one couple of wind speed and thermal variation is needed to assess the evolution
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of the CRR.
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6. Conclusion
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The effects of wind speed and atmospheric stability on the concentration reduction rate (CRR)
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of air pollutants induced by noise barriers were studied with a validated CFD model. This study
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considered both numerous wind conditions (wind speed and direction) and thermal variations,
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leading to different atmospheric stabilities ranging from very unstable cases to stable cases.
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Several CFD simulations were carried out and the main conclusions are as follows:
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(a) When no thermal variations are considered, i.e. for a neutral atmosphere, the evolution
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of the CRR depends only on the wind direction: wind speed changes the pollutant
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concentrations behind the barriers but this parameter does not change the CRR.
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(b) A non-perpendicular wind direction leads to higher pollutant concentrations without
393
noise barriers and lower concentrations with the barriers compared to perpendicular
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cases. The CRRs are therefore minimal for a perpendicular wind.
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(c) The CRR decreases with height due to the different locations of the plume for the two
396
cases with and without noise barriers. The global CRR decreases with distance from the
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downwind barrier.
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(d) The CRR obtained with forced convection (neutral atmosphere) is bounded by the CRR
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obtained with mixed convection (stable and unstable atmospheres): higher CRRs are
400
obtained in stable conditions (Ri > 0) while lower CRRs are obtained in unstable
401
conditions (Ri < 0).
402
(e) For a given Richardson number ranging from -0.50 to 0.17, the CRR is constant with a
403
variation of less than 3%. For numbers outside this range the variation increases to 15%
404
for a Ri ranging from -0.75 to -0.5 and 30% for a Ri ranging from -1.20 to -0.75 and
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from 0.17 to 1.20.
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Finally, these results give insights to researchers and civil engineers to better understand
407
variations of air pollutant concentrations behind noise barriers, for example for carrying out
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further assessment studies on the impact of noise barriers on the reduction of air pollution, and
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for in-field monitoring campaigns.
410
Acknowledgments
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We would like to thank the ANRT (Association Nationale de la Recherche et de la Technologie)
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for their support.
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