## 3 Experimental Results and Analysis
### Effect of Impermeable Surfaces on Surface Temperature
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**Temperature Classification Rules**} \\ \hline Lower temperature & U\({}_{\text{m}}<\text{U}_{\text{m}}-\text{U}_{\text{s}}\) \\ Subcooled zone & U\({}_{\text{m}}-\text{U}_{\text{s}}<\text{U}_{\text{m}}<\text{U}_{\text{m}}-0.5 \times\text{U}_{\text{s}}\) \\ Central temperature area & U\({}_{\text{m}}-0.5\times\text{U}_{\text{s}}<\text{U}_{\text{m}}<\text{U}_{\text{m}}+0.5 \times\text{U}_{\text{s}}\) \\ Subtropical region & U\({}_{\text{m}}+0.5\times\text{U}_{\text{s}}<\text{U}_{\text{m}}<\text{U}_{\text{m}}+ \text{U}_{\text{s}}\) \\ High-temperature zone & U\({}_{\text{m}}>\text{U}_{\text{m}}+\text{U}_{\text{s}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of temperature classifications. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Percentage of Impervious Surface Area by Temperature Classification, Xuzhou City, 2013–2018**} \\ \hline  & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** & **2022** \\ \hline Low temperature & 2.2\% & 1.7\% & 1.8\% & 11.4\% & 7.7\% & 3.9\% & 4.5\% & 3.1\% & 6.9\% & 1. To deeply understand the influence of impervious surfaces on surface temperature, Xuzhou City was randomly divided into 300 blocks and the impervious surface density and average temperature of each block were calculated. The linear regression analysis showed that impervious surface density and surface temperature were positively, but poorly, correlated. It is observed that most of the areas corresponding to outliers are far away from the impervious surface aggregation area and are located in areas with a high percentage of bare soil, where the influence of bare soil on surface temperature is greater than that of impervious surface temperature. In total, 130 sample areas were re-selected in and around the area of impervious surface aggregation, and impervious surface density and average temperature were calculated for each sample. A regression analysis using the percentage of impervious surfaces in the selected blocks as the x-axis and the average temperature as the y-axis revealed that the density of impervious surfaces and the surface temperature are positively correlated. And, the coefficients of determination after removing the outliers were greater than 0.7, which indicated good correlation, and the results are shown in Figure 6. In summary, the increase in impervious surfaces is one of the reasons for the increase in surface temperature. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Zonal Average Temperature 2013–2022 (\({}^{\circ}\)C)**} \\ \hline \multirow{2}{*}{**Year**} & **Impervious Surface** & **Other Impervious** & **Permeable Surface Area** \\  & **Aggregation Zone** & **Surface Areas** & **Permeable Surface Area** \\ \hline
2013 & 37.11 & 34.46 & 32.64 \\
2014 & 31.90 & 30.23 & 28.90 \\
2015 & 38.54 & 36.02 & 34.57 \\
2016 & 37.34 & 34.06 & 31.85 \\
2017 & 36.13 & 34.09 & 32.17 \\
2018 & 34.30 & 32.20 & 30.10 \\
2019 & 37.30 & 34.68 & 31.14 \\
2020 & 30.58 & 29.61 & 27.91 \\
2021 & 40.23 & 37.62 & 35.48 \\
2022 & 32.60 & 30.44 & 28.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average temperature of Xuzhou City from 2013 to 2022. Figure 5: Comparison of temperature classification and impervious surface areas in 2013.