J. Cent. South Univ. (2012) 19: 1657-1662

DOI: 10.1007/s11771-012-1189-9

Quantitative evaluation of urban park cool island factors in mountain city

LU Jun(卢军)¹, LI Chun-die(李春蝶)¹, YANG Yong-chuan(杨永川)¹,

ZHANG Xin-hui(张歆晖)¹, JIN Ming(靳鸣)²

1. Key Laboratory of The Three Gorges Reservoir Region’s Eco-Environment (Chongqing University),

Ministry of Education, Chongqing 400045, China;

2. Sichuan Southwest Project Management Consulting Co. Ltd., Chengdu 610081, China

? Central South University Press and Springer-Verlag Berlin Heidelberg 2012

Abstract: 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.

Key words: park cool island; park characteristics; regression analysis; photosynthesis midday depression; statistical model

1 Introduction

Urban heat island (UHI), a phenomenon that air temperatures in densely built cities are higher than the rural areas, becomes a current principal character of urban climate [1]. The UHI not only causes high temperature in summer and increases building energy consumption, but also leads to serious problems in human thermal comfort and even health [2-6]. WONG and YU found that in Singapore, the maximum temperature difference between urban and rural areas reached 4.0 ℃ for 2 d in summer [7]. SANTAMOURI  et al [8] found that the buildings in the central urban areas had almost double cooling load in summer, and 30%-55% heating load in winter when compared to buildings in suburban areas. It is also found that extreme heat may harm human health: heat stress could cause cramps, rash, heat exhaustion, heat stroke and might exacerbate underlying medical conditions, such as heart or lung diseases [9]. Thus, to mitigate UHI effects is   an essential approach to improve human habitat condition.

Vegetation is able to cool and humidify the thermal environment [10], with the direct shading and evapo- transpiration [11]. Experimental researches demonstrate that urban parks are 1-2 ℃, and sometimes even 5-7 ℃, cooler than their surroundings, forming a “park cool island” (PCI) phenomenon. The cool island effect could exceed 300 m sometimes in summer. Green shading could change the cooling and heating loads of building by reducing incident solar radiation and building surface temperature [12]. Planned plating of trees near buildings could reduce the air-conditioning energy consumption in summer by 10%-35% [13]. In addition, the reduction of wind speed by vegetation may reduce the infiltration of outside air, the effectiveness of natural ventilation, and the convective cooling of building surface [14].

The PCI effect presents seasonal variations, and is extremely influenced by factors of park characteristics such as park area, shape and green ratio [15-17]. CHANG et al [16] found that large parks were usually cooler than smaller parks, but there might be no linear relation between PCI effect and park size. SHASHUA and HOFFMAN [18] proved that the tree shading is a significant factor for the cooling effect. XIN et al [19] proposed the park vegetation and shape which could well predict the PCI intensity.

So far, researches on the PCI effect mostly use remote sensing technology, or conduct observation in different seasons, with the purpose of obtaining the seasonal variation pattern [16-17]. Remotely sensed observations have confirmed that vegetation had cooling effect [20]. However, remote sensing could only reflect the surface temperature but not real air temperature, and could not reflect real air temperature reduction by vegetation. SHUKO and TAKESHI found that the cooling effect of urban green areas in Japan is more obvious in summer than in winter, and in summer the maximum temperature difference reaches 1.9 ℃ [17]. Whereas, it is unable to reflect the daily variation pattern for influencing factors of PCI intensity. SHASHUA and HOFFMAN established an empirical model to predict the PCI effect and a temperature boundary layer model, which could analyze the impact on surroundings by greenbelt [18]. Yet this model cannot be applied to forecasting the PCI intensity in mountain cities. With these issues in mind, actual measurements on PCI intensity of parks were conducted, and the particular factor of mountain city was brought, in order to resolve the following problems: 1) To summarize the condition of daily variation, and analyze the leading factors which influence the PCI intensity; 2) To establish a prediction model of PCI intensity in parks, which could be utilized for park planning in cities in mountain area.

2 Methodology

2.1 Study site and data

The complex canopy structure and special climate in Chongqing are due to the two rivers (Yangtze River and Jaling River) and mountains. Land use in Chongqing is heterogeneous, with a complex assemblage of business districts, densely populated residential areas, vegetated spaces and water body. Chongqing is situated in a region characterized by the wet cold winter, stifling hot summer and minor daily temperature range. In summer, there are 15 to 25 d with temperature above 35 ℃ and the extreme temperature in the hottest month can reach 44 ℃. UHI effect aggravates torridness, and the peak value of actual measurement about UHI intensity in the summer of 2007 reached 4.2 ℃ [21], so this work focuses on PCI analysis in summer.

To evaluate how park characteristics affect the formation of a PCI, six parks located in city center without large area of water body were selected, whose size ranged from 1.2 to 45 hm². The observations were carried out in calm days during the period of July to August in the summer of 2009 and 2010, which were conducted five times a day, namely every 2 h from 9:00 to 17:00. Six stationary observation points were set up in each park. All the observation points were settled 1.5 m above the ground. Measurement of the temperature outside park is based on mobile observation, by walking 500 m along the normal direction of the park. 3 010 data have been recorded, which were recorded every 10 s during the period of measurement.

2.2 Definition of PCI intensity and LSI

Usually, PCI intensity is measured from air temperature in a park and the surrounding urban area [10]. The definition of PCI intensity in this work is temperature difference of the average temperature value between mobile observation outside the park and stationary observation inside the park:

T_PCI=T_O-T_I                                  (1)

Where T_O is the average temperature of urban surroundings within 500 m from the park, which is wide enough to include thermal information on roads, business buildings and other spaces. T_I is the average temperature inside the park, which includes open lawn, lawn shaded from trees, open cement ground and cement ground shaded from trees.

This work brings in landscape shape index (LSI) [21] as an influencing factor of PCI intensity. The LSI is defined as

                               (2)

where I_LS is the landscape shape index; P_t is the total perimeter around a park; A is the area of the park. It is shown in Eq. (2) that the LSI of a circle is 1. The area of a circle has the minimum value on the condition that each area has the same perimeter, therefore, the LSI value of other shapes are all greater than 1. Figure 1 shows the shapes of parks and their LSI values which range from 1.24 to 2.49.

2.3 Data analysis

All the data analysis, graphical displays, ANOVA, t-tests and correlations were calculated by SPSS. To analyze the factors affecting PCI intensity in summer, the analytical methods were used as follows: 1) The PCI intensity was calculated for each park in every time interval to uncover daily variation of the PCI intensity. 2) Multivariate regression utilizing stepwise method was conducted to evaluate the roles of characteristics of parks (LSI, green ratio, area, altitude) affecting PCI intensity.  3) In order to test and verify a plant physiology phenomenon called “midday depression”, which influences the PCI intensity at noon, regression analysis of the PCI intensity and air humidity in parks was completed. 4) A new linear statistical model was developed to predict PCI intensity of parks in Chongqing, based on multivariate regression, then the linear model was validated by leave-one-out method.

3 Results and discussion

3.1 General results

From Fig. 2 and Fig. 3, it is observed that the PCI effect exists in each park at each moment. Furthermore, the parks are about 1 ℃ cooler than their surroundings. However, there is slight heat island phenomenon (opposite of PCI effect) which has a PCI intensity of -0.3 ℃ only in park F at 15:00. The daily variation of PCI intensity in each park has distinct difference. In parks A and B, the maximum value of PCI intensity is 1.65 ℃ and 1.34 ℃, respectively, at 17:00. This is due to the high traffic heat emission on the mobile observation outside the parks during the rush hour of 17:00. Besides, parks are the best places for morning exercise, so the number of people is maximum at 9:00, which leads to the increase of anthropogenic heat emission. Hence, the average PCI intensity value is just 0.9 ℃ at 9:00, which is the minimum value. But the PCI intensity of parks E and F at this moment is 3.52 ℃ and 1.50 ℃, respectively, which are the maximum values all day long. This phenomenon indicates that traffic heat emission plays the leading role in comparison with anthropogenic heat emission. The average PCI intensity value at 13:00 is 1.36 ℃, which is the second-smallest value besides the value at 9:00. Additionally, the average PCI intensity values are 1.45 ℃ and 1.48 ℃, respectively, at 11:00 and 15:00. The maximum average PCI intensity value is  1.56 ℃ at 17:00.

3.2 PCI and park characteristics

Park characteristics play different roles in the PCI phenomenon. XIN et al proved that area of grass, shrubs, trees and LSI are all significantly related factors of PCI intensity [19]. Consequently, three factors of park characteristics, LSI, green ratio and area, are chosen in this work for multiple regression analysis of PCI intensity. In addition, Chongqing is a typical mountain city with different altitudes of each park, so the altitude is considered as an essential park characteristic factor in regression analysis.

Fig. 1 LSI, area and green ratio values of different parks

Fig. 2 PCI intensity in different parks

Fig. 3 Average of PCI intensity at each moment

Table 1 indicates the multiple linear regression results of PCI intensity and park characteristics. The coefficients of determination (R²) for multiple linear regression models were 0.673, 0.750, 0.514, 0.498 and 0.335 at 9:00, 11:00, 13:00, 15:00 and 17:00, respectively. LSI, green ratio, area and altitude are significant related with PCI intensity at each moment. This states clearly that these four factors can explain the phenomenon that the PCI effect is better before 15:00, and R² at 17:00 is minimum because of the increase of anthropogenic heat emission. The standardized coefficients (Std. coefficient) of multiple linear regressions are useful for indentifying which independent variable makes greater contribution to the dependent variable. Area and PCI intensity are positively correlated, and the Std. coefficient is of maximum value at any time, which indicates that area is the most critical factor affecting the PCI intensity. By combining with  Fig. 2, it is easy to find that the cooling effect is obvious when the area of park is greater than 14 hm². PCI intensity and green ratio, altitude are positively correlated, which shows that the higher the green ratio is, the more distinct the PCI intensity is; the higher the altitude is, the more distinct the PCI intensity is. PCI intensity and LSI present negative correlation, which illustrates that the PCI effect is more evident when the shape of park is more close to round.

3.3 PCI and “PMD” phenomenon

Figure 2 shows that at 13:00, PCI intensity values of each park are small, especially in park E. The PCI intensity value of park E is 1.31 ℃ at that moment. Moreover, the R² is just 0.514 (Table 1) at that moment, when the visitors flow rate is minimum. Statements above demonstrate that there are some other influencing factors which affect the PCI intensity.

Plants lower the temperature of ambient air mainly by shading effect and evapotranspiration. This evapotranspiration is caused by the evaporation of plant stomata and accompanied with latent exchange, which can increase the humidity of ambient air and lower air temperature accordingly. During the time period between 9:00 and 17:00, the average value of humidity differences inside and outside park E is 4.22%, 4.54%, 3.92%, 6.07% and 10.31%, respectively. The humidity difference is minimum at 13:00 during all day, which means the evapotranspiration of plants is least obvious at this moment. This is due to the highest solar altitude at the moment which makes plants have “PMD” phenomenon. The reason for this phenomenon starts in this way: under high light irradiation, the plant stomata positively correlated conductance diminishes or the stomata even closes, which result in reduction of blade transpiration rate. Figure 4 indicates that relative air humidity and the PCI intensity are remarkable positively correlated in park E at 13:00. The related coefficient R² is 0.697, which demonstrates that the “PMD” phenomenon is a key factor of generating the reduction of PCI intensity.

Table 1 Multivariate regression of PCI intensity and park characteristics

Fig. 4 Regression analysis of relative air humidity and PCI intensity in park E at 13:00

3.4 Predicting PCI by linear regression model

Based on the results of multiple linear regressions of PCI intensity and park characteristics during 9:00- 17:00, the linear model of daily PCI intensity in parks is established by significant variables to predict the PCI intensity of greenbelt for parks in Chongqing. This linear model of daily PCI intensity in parks is defined as

T_PCI=-2.741-2.368L+0.069A+7.326G+0.003H+ε        (3)

where T_PCI is the intensity of park cooling island (℃); L is LSI of the park; A is area of the park (hm²); G is the green ratio (%); H is the altitude (m); ε is random error, ε～N(0, δ).

Considering the limited number of park samples, leave-one-out cross validation method is used to validate PCI intensity. This method means using 30 data which have not take part in the regression operation by actual measurement to validate the regression results. Figure 5 shows that the observed value and predicted value are remarkable positively related, and R² is 0.787. This declares that this linear model is an appropriate way to forecast the PCI intensity of parks in Chongqing.

Fig. 5 Observed PCI intensity and predicted PCI intensity by linear model of whole day (leave-one-out validation method)

4 Conclusions

1) Altitude is an essential park characteristic describing mountain parks. The multivariable linear regression indicates that there is an obvious positive correlation between altitude and PCI intensity, and standard coefficient reaches the maximum value of 0.524 at 11:00.

2) Park area is the primary factor influencing PCI intensity, and the cool island effect is obvious when park area exceeds 14 hm². There is a negative correlation between PCI intensity and LSI, showing that the rounder the park shape is, the better cooling effect will be achieved.

3) Daily variation of PCI intensity in each park shows that PCI intensity appears low at 13:00. It is found that the reason for this is “PMD” phenomenon of plants at that time. This phenomenon slows down the transpiration rate of plants, reduces the latent heat exchange with surrounding air, and therefore weakens the plant’s cooling effect of air.

4) The linear regression model to describe daily PCI intensity is proposed, which could well predict the PCI intensity of parks in mountain cities. This model could be used when planners design parks with the purpose of enhancing cooling-island effect in mountain cities with the similar natural condition like Chongqing. In future, common models will be established to apply in regions of other terrain types.

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(Edited by HE Yun-bin)

Foundation item: Project(2006BAJ02A02-05) supported by the National Key Technologies R&D Program of China

Received date: 2011-07-26; Accepted date: 2011-11-14

Corresponding author: LU Jun, Professor, PhD; Tel: +86-23-65123777; E-mail: lujun66@vip.sina.com