城市绿地系统降低夏季热岛的时空模拟与布局优化研究

In recent years, China’s Urban Heat Island (UHI) Effect in summer has been exacerbating along with global warming. Researches and practices at home and abroad have proved that Urban Green Space (UGS) is an effective means to reduce the UHI effect. And how to effectively reduce the UHI by using the layout of UGS, becomes a fundamental question of application science in the field of urban and landscape planning. In this regard, this project intends to create a three-dimensional function of key controllable indicators by analyzing the impacts of thermal properties of green space on its surrounding temperature field. From the function, the range of parameters and key parameters in terms of effective cooling green space will then be derived. Based on them, the sensitivity scale of the thermal performance of the UGS will be systematically and comprehensively revealed, which makes it possible to scientifically deconstruct the mechanism of the UGS’ cooling performance. Following that, the optimization of algorithms and models can be created, the pattern and process in which the UGS reduces the UHI can be simulated, and an effective and universal UGS layout pattern can be proposed to reduce the UHI, by adopting grey systems, artificial intelligence, neural network and other methods. There are two noteworthy perspectives of innovation in this project. One is the extension of a research on UGS’s impacts on UHI to a 3D function with key parameters, clarifying the sensitivity scale of its thermal performance. The other is the creation of optimized algorithms and models for grey intelligence, putting forward an adaptive intervention pattern to reduce UHI with UGS. As regards urban planning, this project will enrich relevant theories to build an ecological, livable and low-carbon city, providing a new approach to optimize the UGS layout.

近年来，全球气候变暖加剧，我国夏季城市热岛问题日益严重。国内外研究与实践证明，城市绿地是降低城市热岛的有效手段。而如何高效地布局绿地降低城市热岛，则成为城市与园林规划领域的应用基础科学问题。因此，本项目拟通过探析绿地降温的热性能组织结构对周边温度场的影响变化，来建立关键可控指标的三维函数方程，进而推导出高效降温的绿地布局参数区间、关键参数。从而系统地揭示绿地降温的热性能的尺度敏感性，实现科学的解构绿地降温的机制；进而，利用灰色系统、人工智能、神经网络等方法，构建智能化的空间优化算法和模型，模拟绿地降低热岛的格局与过程，提出具有推广和普适性的有效降低热岛的绿地布局模式。特色与创新：把城市绿地降低热岛研究，扩展到三维空间参数，明确其热性能的敏感性尺度；构建灰色智能优化算法和模型，提出城市绿地降低热岛的适应性干预模式。本项目将丰富生态、宜居、低碳城市规划理论，为优化绿地布局提供了一种新的方法。

随着全球气候变暖和城市化，热岛问题日益严重，成为严重影响城市生态和人类健康的国际前沿问题，如何利用城市绿地系统降低热岛，提高城市夏季人体舒适度，提升城市环境的健康效应，成为重要的科学问题。本项目以天津、北京、南京特大城市研究成果为基础，探索城市绿地系统降低夏季热岛的时空模拟与布局优化。研究发现：①绿地降低热岛具有随时间变化的空间尺度特征，日间宏观敏感尺度半径250m，微观敏感半径尺度为15m，夜晚敏感尺度半径为60m。②构建了绿地降低热岛的复杂适应性系统的微观基本行为模型和整体性模型，模型与城市建筑、水体等要素相适应耦合，遵循尺度敏感性规律发挥最大的降温效果原则。③随着尺度分辨率的增大地表温度与POI点密度的相关性在逐渐升高；气温与MNDWI、绿地率、建筑密度呈正相关关系，与NDVI、道路密度、容积率、POI点密度呈负相关关系；在空间分辨率为90m时，地表温度与平均气温具有较强线性关系。④最高热岛升温与特大城市热岛强度曲线相似性和变化率都与国家城镇化率的变化相一致，主要受国家城市化空间格局影响；最高热岛升温采用灰色预测模型具有较高的模拟精度，热岛升温总量预测采用时间和中国城市化率的复合模型具有较高的精度。⑤城市热岛加剧呼吸系统疾病死亡率，在景观格局上呈现：低等级影响区连通性较高、空间相对集中，高等级影响区呈组团状分布；整体景观特征呈现破碎化和间断分布，影响区在城市中心分布较为集中。⑥以历史惯性模式的优势驱动力的空间特征效应为基础，依靠外生发展模式的驱动力空间效应，使得因城市集聚带来的生态安全问题得以分散缓解。⑦公园可达面积比与公园服务人口两个指标，可作为评价城市公园整体可达性与服务状况的指标。本项目为引导形成科学的生态城市结构和形态、发挥生态服务功能提供理论基础，对研究区域与全球气候变化具有重要的理论意义，为健康城市和气候适应性城市规划提供可操作的布局参数体系。