*5.1. Impact Factors of PCI*

The results of Section 4.1 show that park types can have a different impact on PCI. Among the five park types, the theme park category has the highest PCI, which reaches 2.76 ◦C. The reason is that the theme park has higher vegetation cover and higher water surface coverage than other types. For instance, Zhengzhou Botanical Garden, where the mean FVC, NDWI, NDISI is 0.40, 0.16, 0.19, respectively. More specifically, the vegetation coverage is higher than 50%, and the diversity of species is high as well, as its primary function is science education for citizens. The linear park category has the weakest PCI. This may be because the linear parks (Figure 2c, 4-linear park) are mostly riverside green spaces, or very close to the water surface (e.g., Riverside Park). In case of linear parks in Zhengzhou, there is small LST difference between the park and its neighboring water surfaces. Therefore, the linear park type's average PCI of is the lowest.

For the results of PCI and its impact factors, we have similar conclusions. The FVC, NDWI, NDISI regression coefficient of determination (R2) are 0.237, 0.433, 0.618, respectively. This means the complex correlation between PCI and park characteristics cannot be represented only by those three factors. The park patch metrics (size, fractal dimension, perimeter-area ratio, and shape index) also could not determine alone the PCI variance. As we can recognize from Section 4.3 PCI is also related to the types of the surrounding areas (Figures 6–8). High FVC and NDWI in surrounding areas make the buffer LST closer to the park internal LST, which results in low PCI. There is a positive effect between PCI and surrounding NDISI, related to the surrounding land cover types. As the result shown in Figure 8, the location factor and park type factor also effect PCI.

The cooling effect of the park can be explained from the perspective of thermal balance [39]. We can use the heat transfer theory (Bowen ratio) as an analogy to explain some of the results of this article. The Bowen ratio is the ratio of sensible heat flux to latent heat flux [60]. The surrounding areas are heat sources because the heat capacity of these is significantly smaller than the heat capacity of the parks. In heat conduction, the thermal power (sensible heat flux) absorbed by the parks from the surroundings should be equal to the excess energy resistance by photosynthesis and transpiration (latent heat flux), thus the heat conduction reaches balance. A larger green space means more energy is dissipated which results in more conducted thermal energy. Therefore, parks with large sizes, high vegetation coverage, and high water surface rate have greater energy resistance, which reduces Bowen ratio, and finally, result in higher PCI.

Furthermore, the heat conduction can also explain why parks with high Paratio and fractal dimension have lower PCI. High Paratio and fractal dimension mean that the park boundary is in a large contact surface (complex edges) with the surrounding heat sources, which is conducive to heat conduction and heat exchange. This causes temperature difference decreases, resulting in lower PCI. At the same time, this can also explain the relationship between PCI and surrounding land cover. The ambient temperature also affects the heat transfer. As a whole, to increase the cooling effect of the park, it is recommended to consider the factors of the park itself, improve the resistance to the thermal environment, and increase latent heating, so as to reduce the heat island.
