## 2 Numerical Model and Simulation Setup
### Simulation Setup  
To investigate the effects of wall- and tree-type fences on airflow and PM\({}_{2.5}\) concentration, we selected a building-congested district that included a school with fences as the target area (Figure 1a). This study focused on the area around the school, where many trees are planted along the school boundary, so the effect of trees could be modeled well. Torealistically model the topography, buildings, and roads, we used geographic information system (GIS) data with a horizontal resolution of 1 \(\times\) 1 m. The domain sizes (cell numbers) in the x, y, and z directions were 600 \(\times\) 600 \(\times\) 300 m (300 \(\times\) 300 \(\times\) 150), respectively. The grid intervals in all directions were 2 m.  
The wall-type fence was along the school boundary, and the trees were within the wall-type fence (Figure 1b,c). The heights of trees were set to 2 m and 4 m (Table 1). The CNTL case was conducted with no fence around the school. Others were conducted with wall-type (W) or tree-type (T) fences. 2M and 4M stand for 2-m and 4-m fences, respectively. The porosity of the wall-type fence was set to 0%. For the tree-type fence, we set \(n_{c}\), LAD, and \(C_{d}\) to 1.0, 3.33 (medium-density trees), and 0.2, respectively [7,10]. The vertical profiles of wind, TKE, and TKE dissipation rate (\(\epsilon\)) were set following [PERSON] and [PERSON] [22] as follows:  
\[U(z)=\frac{U_{*}}{\kappa}In\bigg{(}\frac{z}{z_{0}}\bigg{)}cos\theta, \tag{4}\]  
\[V(z)=\frac{U_{*}}{\kappa}In\bigg{(}\frac{z}{z_{0}}\bigg{)}sin\theta, \tag{5}\]  
\[W(z)=0, \tag{6}\]  
\[k(z)=\frac{1}{C_{\mu}^{1/2}}U_{*}^{2}\bigg{(}1-\frac{z}{\delta}\bigg{)}^{2}, \tag{7}\]  
\[\varepsilon(z)=\frac{C_{\mu}^{3/4}k^{3/2}}{\kappa z}, \tag{8}\]  
where \(U_{*}\) is the friction velocity, \(\delta\) is the boundary-layer depth (1000 m), \(\kappa\) is the von [PERSON] constant (0.4), and \(C_{\mu}\) is an empirical constant (0.09) in the RNG \(k\)-\(\epsilon\) turbulence closure scheme. The numerical simulations were performed for 16 inflow directions for each fence configuration summarized in Table 1. The reference wind speed was determined from the average wind speeds measured at the ASOS 159 (69.5 m above sea level) from 2011 to \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Wall Height** & **Tree Height** \\ \hline CNTL & 0 m & 0 m \\ W2M & 2 m & 0 m \\ W4M & 4 m & 0 m \\ T2M & 0 m & 2 m \\ T4M & 0 m & 4 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the fence heights considered in this study. Figure 1: (a) Satellite image, (b) fence location, and (c) computational configurations of the target area. 2020 (Table 2). The background PM\({}_{2.5}\) concentrations for the 16 directions were determined by the PM\({}_{2.5}\) concentrations measured at the AQMS 221112 from 2015 to 2020 (PM\({}_{2.5}\) measurements in South Korea began in 2015). The AQMS 221112 is located 820 m east of the target area. We assumed that the background PM\({}_{2.5}\) was well mixed within the model domain height and the PM\({}_{2.5}\) concentrations at the model boundaries were constant with height. To estimate a road emission rate of PM\({}_{2.5}\), we used the CAPSS (Clean Air Policy Support System) established by the Ministry of Environment, South Korea. We allocated the PM\({}_{2.5}\) emission rate at each road using the monthly, daily, and hourly emission factors [23] based on the total amount of PM\({}_{2.5}\) mobile emission of the target area in the CAPSS. The emission rate, \(E\) in kg m\({}^{-2}\) h\({}^{-1}\), was estimated by the top-down emission inventory method [24] as follows:  
\[E=\frac{E_{d}}{A_{r}}\times EF_{m}\times EF_{d}\times EF_{h}, \tag{9}\]  
were \(E_{d}\) and \(A_{r}\) indicate the daily mean PM\({}_{2.5}\) emission rate (kg day\({}^{-1}\)) and the total area of roads in the target area, respectively. \(EF_{m}\), \(EF_{d}\), and \(EF_{h}\) indicate the monthly, daily and hourly allocation coefficients, respectively.