## 3 Data Analysis
### Simulation Accuracy Verification  
Because the setting parameters for the four scenarios were derived from actual venues and corresponding measured data exist, the simulation data were validated. In this study, two methods were used to verify the accuracy of the simulation results through an error analysis of the simulated and measured data. Three indicators were selected for error evaluation: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The MAE is the average of the absolute errors between the simulated and measured data and is used to evaluate the magnitude of the absolute error in simulation experiments. The MAPE is the average value of the percentage error between the simulated and measured data and is used to evaluate the magnitude of the percentage error in simulated experiments. The RMSE is an indicator of the consistency between the trend of the simulated and measured data and is used to evaluate whether the trend of the simulated experiments is consistent with the measured data. The equations are expressed as follows:  
MAE:  
MAE \[=\frac{1}{\text{p}}\sum_{i=1}^{\text{p}}\left|\text{x}_{\text{i}}-\text{y}_{ \text{i}}\right|\] (5)  
MAE \[=\frac{1}{\text{p}}\sum_{i=1}^{\text{p}}\left|\text{x}_{\text{i}}-\text{y}_{ \text{i}}\right|\] (6)  
MAPE \[=\frac{1}{\text{p}}\sum_{i=1}^{\text{p}}\frac{\left|\text{x}_{\text{i}}-\text {y}_{\text{i}}\right|}{\left|\text{x}_{\text{i}}\right|}\times 100\%\] (7) RMSE:  
\[\text{RMSE}=\sqrt{\frac{1}{\text{p}}\sum_{i=1}^{\text{p}}\left|\text{x}_{ \text{i}}-\text{y}_{\text{i}}\right|}2^{*} \tag{8}\]  
where xi is the onsite measurement value, y is the simulation result, and p is the number of sampling points. The lower the MAE, MAPE, and RMSE values, the smaller the error. If MAE and RMSE exceed half of the standard deviation (SD) of the measured data and MAPE is lower than 25%, the simulated data are in good agreement with the measured data [45,46].  
Four error evaluation indicators were calculated for two scenarios (Table 3).  
(1) Scenarios 1 and 2 were compared with the measured data, and the MAE between the simulated and measured data for the PM\({}_{2.5}\) concentration was 4.4 ug/m\({}^{3}\). Both the MAE and RMSE were lower than SD/2, and the MAPE values were lower than 25%, indicating a high degree of consistency between the simulated and measured data. The simulated and measured values of the PM\({}_{2.5}\) concentration at each collection point in Scenarios 1 and 2 showed consistent trends (Figure 7a).  
(2) Scenarios 3 and 4 were compared with the measured data, and the simulated data for various indicators of the PM\({}_{2.5}\) concentration were consistent with the measured data. Both the MAE and RMSE values were lower than SD/2, and the MAPE values were lower than 25%. The simulated and measured values of the PM\({}_{2.5}\) concentration at each collection point in Scenarios 3 and 4 showed consistent trends (Figure 7b).  
The MAE, MAPE, and RMSE values of the PM\({}_{2.5}\) concentration simulation data and measured data satisfied the error requirements, and the data were consistent. The existing  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**PM\({}_{2.5}\) Concentration**} \\ \cline{2-5}  & **Scene 1** & **Scene 2** & **Scene 3** & **Scene 4** \\ \hline MAE & 3 & 5.8 & 0.01 & 1.8 \\ MAPE & 23.10\% & 24.90\% & 0.30\% & 24.10\% \\ RMSE & 3.1 & 7.2 & 0.02 & 2 \\ SD/2 & 3.5 & 13 & 0.01 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of errors between on-site measured data and simulated data.  
models can satisfactorily reflect the actual conditions, providing support for further detailed research.