# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 3 Results and Discussion
### Regression Model and Applicability
The residuals vs. fitted values and residuals vs. order plots confirm randomness and independence, supporting the model's homoscedasticity and predictive validity. Figure 4, Contour plots showing the interaction effects between independent variables on T for Leafy Plants. AT and M interactions yield the most distinct thermal gradients, especially in the AT\(\times\)M and M\(\times\)AT plots, where red and yellow zones indicate optimal cooling potential. Hold values used in plots are A = 200, M = 73.9, WS = 1.25, AT = 15. These visualizations confirm that T response varies depending on the combination of \begin{table}
\begin{tabular}{l} T = 50.3 + 0.1284 A + 0.187 M \(-\) 64.4 WS \(-\) 0.984 AT \(-\) 0.000222 A\(\times\)A \(-\) 0.00640 M\(\times\)M + 26.43 WS\(\times\)WS \(-\) 0.01974 \\ AT\(\times\)AT \(-\) 0.000407 A\(\times\)M \(-\) 0.00015 A\(\times\)WS + 0.000342 A\(\times\)AT + 0.041 M\(\times\)WS + 0.03551 M\(\times\)AT \(-\) 0.264 WS\(\times\)AT \\ \end{tabular}
\end{table} 
Table 1, Analysis of Variance (ANOVA) for Leafy Plants, showing the influence of four independent variables—surface area (A), humidity (M), wind speed (WS), and ambient temperature (AT)—on surface temperature (T). The model explains 93.51% of the total variance, with AT being the dominant factor (70.14%), followed by M (7.76%) and A (1.69%). Significant quadratic and two-way interactions were also observed, especially between M and AT (F = 227.22). The low \(p\)-values (<0.05) indicate the model’s strong statistical significance, while the low error contribution (6.49%) confirms the robustness of the model. Figure 3, Residual analysis and Pareto chart of standardized effects, evaluating factor significance, normality, homoscedasticity, and independence for Leafy Plants. microclimatic conditions and plant characteristics, offering guidance for targeted landscape interventions. The experimental results indicate that the AT, M\(\times\)AT, M, and A factors have the highest impact. Residual analysis confirms that the model follows a normal distribution and exhibits high predictive power due to the randomly distributed residuals. The Pareto chart reveals that some interactions, such as M\(\times\)AT and A\(\times\)A, may be more influential than individual factors. Contour plots show that optimal results are achieved in the red and yellow regions. Overall, the model is reliable, and the identified factors and interactions are critically important for optimization. According to Table 2, the RSM model is statistically significant (F = 70.58, \(p\) < 0.001). Linear terms play a substantial role in explaining the model (10.89%), with AT being the most prominent factor (7.52%), followed by M (2.21%). Quadratic terms account for 2.87% of the total variance, while two-factor interactions contribute 4.88%. Among these, the AT\(\times\)M interaction (F = 38.54, 3.56%) emerges as the most significant. The low error rate (8.6%, Adj SS = 726.67) further supports the reliability of the model. \[ \begin{split}\text{T = 91.7 + 0.0376 A - 9.955 M - 54.1 WS - 12.08 AT - 0.00093 A \(\times\)A + 0.00159 M \(\times\)M + 29.83 WS \(\times\)WS - 0.01107 AT\(\times\)AT + 0.000228 A\(\times\)M - 0.0094 A\(\times\)WS + 0.000802 A\(\times\)AT - 0.088 M\(\times\)WS + 0.04088 M\(\times\)AT - 0.772 WS\(\times\)AT \\ \end{split}  \tag{3}\]  
Table 2, ANOVA results for Coniferous Plants. The model explains 91.40% of the total variance in T. The most influential variable is AT, with A 69.82% contribution, followed by M at 4.65% and A at 2.94%. While WS alone is statistically insignificant (\(p\) = 0.799), its interaction with AT (WS\(\times\)AT) and the M\(\times\)AT interaction is highly significant, with F-values of 10.71 and 38.54, respectively. These interactions suggest that combined microclimatic conditions are more impactful than individual variables. The low error rate (8.60%) supports the robustness and reliability of the RSM model.