# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 3 Results and Discussion
### Regression Model and Applicability
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Source** & **DF** & **Contribution** & **Adj SS** & **Adj MS** & **F-Value** & **\(p\)-Value** & **VIF** \\ \hline Model & 14 & 91.26\% & 6182.65 & 441.618 & 69.34 & 0.000 & \\ Linear & 4 & 83.62\% & 674.25 & 168.561 & 26.47 & 0.000 & \\ A (m\({}^{2}\)) & 1 & 0.02\% & 2.19 & 2.188 & 0.34 & 0.559 & 1.17 \\ M (\%) & 1 & 5.50\% & 131.09 & 131.093 & 20.58 & 0.000 & 3.47 \\ WS (m/s ) & 1 & 7.75\% & 6.13 & 6.133 & 0.96 & 0.329 & 4.63 \\ AT (\({}^{\circ}\)C) & 1 & 70.34\% & 540.83 & 540.835 & 84.92 & 0.000 & 4.02 \\ Square & 4 & 2.81\% & 132.96 & 33.239 & 5.22 & 0.001 & \\ A\(\times\)A & 1 & 0.17\% & 11.64 & 11.639 & 1.83 & 0.180 & 1.00 \\ M\(\times\)M & 1 & 2.11\% & 0.20 & 0.196 & 0.03 & 0.861 & 7.65 \\ WS\(\times\)WS & 1 & 0.15\% & 46.62 & 46.619 & 7.32 & 0.008 & 5.41 \\ AT\(\times\)AT & 1 & 0.38\% & 6.70 & 6.698 & 1.05 & 0.308 & 1.79 \\
2-Way & 6 & 4.83\% & 327.20 & 54.534 & 8.56 & 0.000 & \\ Interaction & & & & & & & \\ A\(\times\)M & 1 & 0.01\% & 0.15 & 0.155 & 0.02 & 0.876 & 1.13 \\ A\(\times\)WS & 1 & 0.04\% & 2.19 & 2.191 & 0.34 & 0.559 & 1.25 \\ A\(\times\)AT & 1 & 0.01\% & 0.81 & 0.814 & 0.13 & 0.722 & 1.06 \\ M\(\times\)WS & 1 & 0.12\% & 0.52 & 0.518 & 0.08 & 0.776 & 3.79 \\ M\(\times\)AT & 1 & 3.86\% & 263.35 & 263.346 & 41.35 & 0.000 & 2.11 \\ WS\(\times\)AT & 1 & 0.78\% & 52.59 & 52.587 & 8.26 & 0.005 & 4.18 \\ Error & 93 & 8.74\% & 592.28 & 6.369 & & & \\ Total & 107 & 100.00\% & 6774.93 & & & & \\ \hline Model Summary & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \end{tabular}
\end{table} 
Table 3: Analysis of Variance for shrubs. Figure 7: Residual analysis and Pareto chart of standardized effects, evaluating factor significance, normality, homoscedasticity, and independence for shrubs. Figure 7, Pareto chart and residual analysis for shrubs. The ambient AT and its interaction with M (M\(\times\)AT) are identified as the most impactful predictors of T. The normal probability plot confirms the normality of residuals, while the Versus Fits and Versus Order plots show randomly scattered residuals, verifying the assumptions of homoscedasticity and independence. These diagnostics validate the statistical robustness and predictive performance of the model. Figure 8, Contour plots of T for shrubs, showing how the T varies with the interaction of environmental factors. The most significant interactions--AT\(\times\)M and M\(\times\)AT--demonstrate strong gradients with visible red and yellow zones, indicating higher cooling effects. The influence of WS is also observable in the WS\(\times\)AT and WS\(\times\)WS plots. These graphical representations reinforce the model's findings by visually depicting how different variables synergistically regulate microclimate conditions in shrub-covered areas. According to Table 4, the RSM model is statistically significant (F = 81.76, \(p\) < 0.001). Linear terms explain a large portion of the model's variance (12.48%), with AT being the strongest variable (9.68%), followed by M (2.83%). Quadratic terms account for 2.34% of the total variance, while two-factor interactions contribute 4.00%. The M\(\times\)AT interaction (F = 41.04, 3.31%) has been identified as the most significant interaction. The model exhibits a low error rate (7.51%, Adj SS = 591.98) and is considered reliable. Regression Equation in Uncoded Units  
\[\begin{array}{l}\text{T = 108.7 -- 0.0521 A -- 1.185 M -- 47.7 WS -- 1.575 AT + 0.000114 A \(\times\)A + 0.00225 M \(\times\)M + 21.48 WS\(\times\)WS + 0.00587}\\ \text{AT\times\)AT + 0.000076 A \(\times\)M + 0.0040 A \(\times\)WS -- 0.000579 A \(\times\)AT + 0.009 M \(\times\)WS + 0.03806 M \(\times\)AT -- 0.446 WS\(\times\)AT}\end{  
highlighting the determining influence of the AT and M factors. In conclusion, the AT factor is the most significant independent variable, and its interaction with M enhances the predictive power of the model.