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Evaluating the benefits of ecosystem-based urban cooling using a dynamic "on-site" method
被引:5
|作者:
Han, Baolong
[1
]
Wu, Tong
[2
]
Cai, Zhengwu
[1
]
Meng, Nan
[1
]
Wang, Haoqi
[1
,3
]
Ouyang, Zhiyun
[1
,4
]
机构:
[1] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China
[2] Stanford Univ, Nat Capital Project, Stanford, CA 94305 USA
[3] Southwest Univ, Coll Hort & Gardens, Chongqing 400100, Peoples R China
[4] Room 606,Shengtai Bldg,Shuangqing Rd, Beijing 100085, Peoples R China
基金:
中国国家自然科学基金;
关键词:
Urban heat island effect;
Urban cooling;
Ecosystem service;
Climate change;
On-site;
USE/LAND COVER CHANGES;
HEAT-ISLAND;
PATTERN;
IMPACT;
D O I:
10.1016/j.scitotenv.2023.162908
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Ecosystem-based cooling helps residents cope with the urban heat-island problem. In order to improve the accuracy of traditional heat-island measurements based on comparisons between urban and rural areas, we use an "on-site" method developed with only urban data. The essence of this method is a regression analysis of the relationships among different types of green space and blue space, elevation, vegetation dynamics, and temperature. We then simulate the temperature pattern in a scenario where there is no built-up area (Scenario A), and then in another scenario where there are no ecological spaces (Scenario B). The gap between the actual temperature pattern and the simulated temperature pattern of Scenario A is considered the heat-island effect. Conversely, the gap between the actual temperature pattern and that of Scenario B is considered as the effect of ecosystem-based urban cooling. This method was tested using data from two megacities in China (each had a population of over 10 million people). For Beijing, the average heat-island effect was 4.87 center dot C and effect of the ecosystem cooling service was 9.07 center dot C. For Shenzhen, the respective values were 0.8 center dot C and 2.71 center dot C. The "on-site" (local small size sampling), "dynamic coefficient", and "no-positive-coefficient rule" are the three defining characteristics of this method. The application of this method to model ecosystem-based urban cooling can aid urban planning and management in improving the residential thermal environment.
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