3.1. Model validation
Table 3 shows the results of fitting the measured Ta and RH values to the simulated values. The R2 values representing the correlation between the measured and simulated Ta and RH values at each measurement point ranged from 0.79 to 0.99 and 0.72 to 0.82, the RMSE values ranged from 0.36 to 0.85°C and 2.67 to 3.68%, and the MAE values ranged from 0.31 to 0.74°C and 2.24 to 3.11%, respectively. These results showed that the error was within an acceptable range and that the established ENVI-met model was reliable and suitable for this study.
Table 3
Correlation between measured and simulated values
Meteorological Elements | Indicators | PA1 | PA2 | PA3 | PA4 | PA5 |
Ta | R2 | 0.99 | 0.89 | 0.86 | 0.79 | 0.79 |
RMSE (℃) | 0.36 | 0.47 | 0.65 | 0.85 | 0.58 |
MAE (℃) | 0.31 | 0.41 | 0.58 | 0.73 | 0.74 |
RH | R2 | 0.82 | 0.74 | 0.74 | 0.72 | 0.81 |
RMSE (℃) | 3.24 | 3.70 | 2.96 | 3.68 | 2.67 |
MAE (℃) | 2.90 | 3.02 | 2.25 | 3.11 | 2.24 |
3.2. Effects of different tree species on Ta in different scenarios
Figure 3 shows the simulation results of the impact of different tree species on Ta in various scenarios. In all planting scenarios, Ta remained consistently lower than that in the control scenario. Planting Fa achieved the best cooling effect in forms PM2, PM3, PM4, and PM5, resulting in reductions of 1.65 ℃, 0.57 ℃, 1.34 ℃, and 2.15 ℃ in Ta at 16:00, respectively. In PM2, the cooling effect of Ls was the least effective, reducing Ta by only 0.91 ℃. In PM3, none of the tree species exhibited substantial ability to reduce Ta. Specifically, the cooling effects of Ks and Ls were the least pronounced, both leading to a modest reduction in Ta of only 0.09 ℃. In PM4, the cooling effect of Ls was the least effective, resulting in a reduction in Ta of only 0.49 ℃. Similarly, in PM5, the cooling effect of Ks was the least effective, leading to a reduction in Ta of only 0.99 ℃.
Planting Ks and Ls did not yield considerable reductions in Ta in any scenario. Nevertheless, the simulation results clearly demonstrated that an optimal cooling effect was achieved through contiguous planting of trees in patches. Table 4 shows the ANOVA results for the mean Ta values in the different scenarios. Significant differences in the mean Ta values were observed between PM2 and PM3, as well as between PM4 and PM5 (P < 0.05). Furthermore, the differences between PM3 and PM4 were highly significant (P < 0.01), whereas no significant differences (P > 0.05) were observed among the remaining scenarios.
Table 4
The average Ta, Tmrt, and PET difference at a height of 1.5 m in different scenarios
Parameters | Statistical results | PM2– PM3 | PM2– PM4 | PM2– PM5 | PM3– PM4 | PM3– PM5 | PM4– PM5 |
Ta | Mean difference (℃) | −0.48* | −0.21 | 0.14 | 0.27 | 0.62** | 0.35* |
P -value | 0.003 | 0.140 | 0.330 | 0.065 | < 0.001 | 0.021 |
Tmrt | Mean difference (℃) | −3.15* | 1.18 | 5.09** | 4.33* | 8.24** | 3.91* |
P -value | 0.014 | 0.316 | < 0.001 | 0.002 | < 0.001 | 0.003 |
PET | Mean difference (℃) | −2.28* | −0.37 | 2.19* | 1.91* | 4.48** | 2.57* |
P -value | 0.009 | 0.636 | 0.012 | 0.025 | < 0.001 | 0.004 |
*P < 0.05, **P < 0.01 indicate significant differences |
3.3. Effects of different tree species on Tmrt in different scenarios
Figure 4 presents the simulation results for the impact of different tree species on Tmrt in various scenarios. Throughout the simulation period, with the exception of 18:00, Tmrt in all scenarios remained lower than that in PM1. In PM2, Bb exhibited a more pronounced cooling effect at 9:00, resulting in a considerable reduction in Tmrt of 8.58 ℃. At 17:00, Fa demonstrated the most effective cooling performance, leading to a remarkable decrease in Tmrt of 17.22 ℃. Conversely, Ls displayed the weakest capability in reducing Tmrt, with a maximum reduction of 12.27 ℃, resulting in an average cooling effect of only 4.48 ℃. In PM3, the overall results for Tmrt were similar to those for Ta, indicating that various tree species did not exhibit considerable cooling effects in reducing Tmrt. This could be attributed to the limited shading area provided by the individually planted trees, which restricted their impact on Tmrt. However, unlike in other scenarios, in PM3, most tree species demonstrated the best cooling effect on Tmrt at 9:00. Among them, Fa showed the most considerable cooling effect, reducing Tmrt by 9.05 ℃, while Ks exhibited the lowest cooling effect, reducing Tmrt by only 2.06 ℃. Furthermore, Ls exhibited the best effect in reducing Tmrt at 16:00. These variations in the cooling effects of different tree species at different times may be attributed to their unique canopy characteristics, which influence the shading intensity and absorption of radiative heat, thus affecting Tmrt levels at different times of the day. In PM4 and PM5, all tree species demonstrated the most favorable cooling effects on Tmrt at 16:00. Among them, Fa exhibited the most pronounced reduction in Tmrt, with decreases of 18.25 ℃ and 22.72 ℃ in PM4 and PM5, respectively, while Ks displayed the least effective cooling, resulting in reductions of Tmrt by only 8.58 ℃ and 9.27 ℃, respectively.
The shading effect of trees played a crucial role in reducing Tmrt. The superior performance of Fa in reducing Tmrt was attributed to its larger canopy size, which allowed it to provide a wider shading environment, effectively reduce solar radiation and enhance the cooling effect of Tmrt. Furthermore, PM5, a fully shaded space in which trees were planted in close proximity, exhibited the best cooling effect. In this layout, the branches and leaves of the trees intersect or overlap, providing maximum shading and resulting in the most effective reduction in Tmrt. Table 4 shows the ANOVA results for the mean Tmrt values in the different scenarios. Except for the comparison between PM2 and PM4, there were significant differences in the mean Tmrt values among all the other scenario comparisons (P < 0.05). In particular, comparisons between PM2 and PM5 as well as between PM3 and PM5 showed highly significant differences (P < 0.01).
3.4. Effects of different tree species on WS in different scenarios
Figure 5 presents the simulation results for the impact of different tree species on WS in various scenarios. This study conducted simple forced simulations with a wind direction of 135°, and the simulated results were obtained at a height of 1.5 m. The findings revealed a consistent trend of WS variations across different scenarios: a gradual decrease from 9:00 to 10:00, followed by a gradual increase from 10:00 to 12:00, which eventually stabilized. Except for Ls, all tree species in the four scenarios exhibited a slightly higher WS than PM1. Notably, in scenarios PM2, PM3, and PM5, the WS for Ls was lower than that in PM1, with reductions of 0.15 m/s, 0.06 m/s, and 0.29 m/s, respectively. However, in PM4, the WS below the Ls canopy was slightly higher than in PM1, with an increase of 0.04 m/s. This phenomenon may be attributed to the obstructive effect of trees with a higher LAI and lower under branch height on wind flow. Trees with a higher LAI possess a greater LAD and smaller interleaf gaps, thereby reducing the wind diversion effect to some extent. Additionally, in PM4, the planting arrangement of trees formed a U-shape with an opening facing south, allowing the wind from the southeast to enter the space through the opening, resulting in a slight increase in WS.
3.5. Effects of different tree species on the PET in different scenarios
Figure 6 presents the simulation results of the impact of different tree species on the PET in various scenarios. The research findings indicated a similar overall trend between PET and Tmrt, suggesting that PET is primarily influenced by solar radiation. From 9:00 to 17:00, the PET for all scenarios was lower than that for PM1. However, the temperatures of most scenarios fell within the domain of strong heat stress, despite the presence of trees. This observation underscored the constrained efficacy of inducing considerable cooling effects through the cultivation of a sparse tree population in outdoor settings. Among the tree species, Fa exhibited the greatest cooling effect in PM2, PM3, PM4, and PM5, reducing the PET by 11.85°C, 8.07°C, 12.16°C, and 15.43°C at 16:00, respectively. In PM2 and PM3, the shade of the trees varied with solar activity. As a result, the tree species with a smaller canopy size, such as Ls, demonstrated the smallest cooling effect on the PET, reducing it by only 7.95 ℃ and 2.49 ℃ at 16:00, respectively. The order of the cooling effect of different tree species on the PET, from greatest to least, was: Fa > Bb > Ks > Dr > Ls in PM2, and Fa > Dr > Ks > Bb > Ls in PM3. In PM4 and PM5, the Ks tree species demonstrated the least efficacy in cooling the PET, resulting in reductions of approximately 5.78 ℃ and 9.16 ℃ at 16:00, respectively. In these scenarios, tree species exhibited the following PET cooling effectiveness rankings: Fa > Bb > Ls > Dr > Ks.
The simulation results showed that Fa exhibited the greatest cooling effect in all scenarios. Meanwhile, PM5 had a lower PET value than the other scenario, indicating better thermal comfort. Table 4 shows the ANOVA results for the mean PET in different scenarios; there was no significant difference (P > 0.05) in the PET between PM2 and PM4, but there were significant differences among that in other scenarios (P < 0.05). In addition, there was a significant difference in the PET between PM3 and PM5 (P < 0.01).