摘要
Evaluating how park characteristics affect the formation of a park cool island(PCI) is the premise of guiding green parks planning in mountain cities.The diurnal variation of PCI intensity was achieved,and correlations between PCI intensity and park characteristics such as park area,landscape shape index(LSI),green ratio and altitude were analyzed,using 3 010 temperature and humidity data from measurements in six parks with typical park characteristics in Chongqing,China.The results indicate that:1) the main factor determining PCI intensity is park area,which leads to obvious cool island effect when it exceeds 14 hm2;2) there is a negative correlation between PCI intensity and LSI,showing that the rounder the park shape is,the better the cool island effect could be achieved;3) regression analysis of humidity and PCI intensity proves that photosynthesis midday depression(PMD) is an important factor causing the low PCI intensity at 13:00;4) the multivariable linear regression model proposed here could effectively well predict the daily PCI intensity in mountain cities.
Evaluating how park characteristics affect the formation of a park cool island （PCI） is the premise of guiding green parks planning in mountain cities. The diurnal variation of PCI intensity was achieved, and correlations between PCI intensity and park characteristics such as park area, landscape shape index （LSI）, green ratio and altitude were analyzed, using 3 010 temperature and humidity data from measurements in six parks with typical park characteristics in Chongqing, China. The results indicate that： 1） the main factor determining PCI intensity is park area, which leads to obvious cool island effect when it exceeds 14 hm2; 2） there is a negative correlation between PCI intensity and LSI, showing that the rounder the park shape is, the better the cool island effect could be achieved; 3） regression analysis of humidity and PCI intensity proves that photosynthesis midday depression （PMD） is an important factor causing the low PCI intensity at 13：00; 4） the multivariable linear regression model proposed here could effectively well predict the daily PCI intensity in mountain cities.
基金
Project(2006BAJ02A02-05) supported by the National Key Technologies R&D Program of China
