*2.2. Wind Environment and Pollutant Monitoring*

The objective of the field monitoring is to count the concentration levels of pollutants in the neighborhoods as well as the characteristics of the wind environment and to conduct a preliminary analysis of the differences in pollutant concentrations and wind environment between neighborhoods, which serves as a foundation for testing and validating simulation results. The XL68 intelligent environmental monitoring equipment is chosen to monitor PM2.5 concentration (resolution: 1 μg/m3, range: 0~1000 μg/m3) and wind speed (resolution: 0.1 m/s, range: 0~60 m/s) of the neighborhoods at monitoring points P1 and P2 (z = 3 m, z = 2 m) (Figure 1b). Details of equipment are shown in Table S1.

#### *2.3. "Pollutant-Wind Environment" Model Setting*

Micro-scale CFD numerical simulations have been widely used in the simulation of outdoor wind environments and pollutant dispersion [17]. ANSYS FLUENT 21.0 based on the finite volume method was adopted for numerical simulations, and the governing equation was the Reynolds-averaged Navier–Stokes equation. The standard K-ε turbulence model was adopted to simulate the airflow [18]. Pollutants and air were considered as continuous phases, and the pollutant concentrations were solved with the component transport model [19,20].

#### 2.3.1. Computational Domain and Grid Generation

The calculation domain was constructed according to the method specified by the European Cooperation in the Field of Scientific and Technical Research (COST) [21], keeping a minimum of 5H for vertical distances (H—the maximum building height) and 5H for horizontal and horizontal distances (Figure 2). At the same time, an unstructured meshing method based on a hexahedron was adopted to save computational costs. Three sets of coarse–medium–fine meshes were divided, and grid irrelevance was tested. The final grid was 2.4 × 10<sup>8</sup> for Neighborhood A and 2.6 × 108 for Neighborhood B.

,. ,**%**.

**Figure 2.** Computing domain construction of neighborhoods: (**a**) Neighborhood A; (**b**) Neighborhood B.

2.3.2. Boundary Condition

The incoming wind speed is exponentially distributed with height [22]. For the simulation, the calculation domain entry was set as the velocity-inlet boundary condition and adopted a user-defined function:

$$\mathbf{U} = \frac{\mathbf{U}\_{\ast}}{\kappa} \ln(\frac{\mathbf{z} + \mathbf{z}\_{0}}{\mathbf{z}\_{0}}) \tag{1}$$

U—horizontal wind speed at height z(m), m/s

U\*—ground friction speed, m/s

κ—Von·Karman constant, κ = 0.42

z0—surface roughness, z0 = 0.25

PM2.5 was mainly emitted from traffic emissions and was relatively stable by default. Pollutants were emitted vertically upwards at 0.5 m/s, and the source intensity was from the nearest urban monitoring station on the simulation day. The zero static gauge pressure outlet was used for the downstream boundary condition, and the standard wall functions with roughness modification were used for the building surface and the bottom of the computational domain. The roughness height was 0.0025–0.003 m, and the roughness constant was 0.75. Symmetry boundary conditions were served to the side-face computational domain and the upper-face computational domain [23]. Detailed boundary conditions are shown in Figure 3.

**Figure 3.** Calculation of domain boundary condition settings.

The study treated the canopy section of the tree with a porous medium due to the influence of trees on the surrounding flow field in reducing wind speed and increasing flow disturbance. According to the relevant literature [24], the modeling of the influence of tree canopy on the flow field was accomplished by adding source terms to the momentum equation, the K equation and the ε equation, respectively. The porosity was 0.7, the inertial resistance was 0.18, and the viscous resistance was 1.67. Meanwhile, pollutant sorption and deposition by trees were adjusted to a constant value, and the rate of deposition was determined by wind speed and pollutant concentration [25]:

$$\mathbf{Y}\_{\text{PM2.5}} = \mathbf{v} \cdot \mathbf{d} \cdot \text{LAD} \cdot \mathbf{t} \tag{2}$$

YPM2.5—Pollutant adsorption capacity per unit area (μg/m2) v—Adsorption rate (m/s) d—Pollutant concentration (μg/m3) LAD—leaf area density; (m2/m3) t—Adsorption time (s)

#### 2.3.3. Solution Settings

The finite volume method was used to discretize the control equation, solved by the SIMPLE algorithm, and the second-order upwind algorithm was adopted. In the initial condition setting, the ground observation data of the Beijing meteorological station on typical dates (Table 1) were used as the initial conditions for the simulation. PM2.5 monitoring concentrations close to those of national control stations were used as the basis for selecting typical dates, and four typical dates with typical meteorological characteristics during the monitoring period were selected to establish the CFD numerical model.

**Table 1.** Meteorological data of national control stations on simulation dates.

