城市绿色基础设施降温作用及其影响因素研究进展

城市绿色基础设施（UGI）作为一种多尺度、多类型和多功能的绿色网络,对缓解城市升温和提升城市气候韧性具有重要作用。尽管如此,当前仍缺乏关于UGI降温作用研究方法、降温机制、尺度效应和影响因素等方面的系统性综述。为此,从尺度、方法、降温机制、影响因素等多个方面详尽回顾了UGI降温作用研究进展,并讨论了其局限性、挑战和未来研究方向。研究发现：（1） UGI降温作用的研究尺度可归纳为站点、建筑、街道、绿地、街区和城市6类,其中站点、绿地和街区等微尺度和局部尺度研究较多,而城市尺度关注则较少。（2）研究方法可分为温度数据获取、降温作用测度和影响因素分析三部分。其中,温度数据获取主要采用实地观测、遥感观测和模型模拟三类方法;降温作用测度可分为基于空间参考、基于时间或情景参考和基于统计关系三类;影响因素分析多采用相关分析与多重线性回归等传统统计方法,此外,可探究非线性影响的机器学习方法也逐渐得到应用。（3） UGI降温作用主要依赖于遮荫、蒸散发与冷空气团平流三类机制;UGI植被物种构成与形态、UGI空间配置和外部环境是影响降温作用的三类主要因素;不同尺度降温作用的主控影响因素存在差异,随着尺度扩大UGI空间配置因素的重要性逐渐增加。最后,研究从时空尺度、降温机制、综合影响因素、生态系统服务视角等方面讨论了当前研究的局限和未来挑战,并强调了新型数据获取技术、组合降温策略以及城市尺度UGI系统降温作用等未来研究方向。

Cooling effect of urban green infrastructure and its impacting factors: A review

Urban green infrastructure (UGI), a multi-scale, multi-type, and multi-function network composed of green elements, plays a pivotal role in alleviating the increase in urban temperature and promoting the urban resilience. Nevertheless, systematic discussions about the methodology, mechanisms, scale effect, and influencing factors of cooling effects of the UGI are rare in the literature. Thus, we reviewed the progress on the cooling effects of UGI in terms of research methods, physical mechanisms, and impact factors. Moreover, we discussed the limitations, challenges, and future works in the field. We found that (1) the current studies could be classified into six scales, i.e., site, building, street, greenspace, landscape, and city scale, in terms of the spatial scope of cooling effect. Among them, micro- and local- scales such as site, greenspace, and street have received more concerns, while relatively few studies were conducted at the city scale. (2) The research methods could be grouped into three parts, i.e., data acquisition, measurements of cooling effects, and analysis of impacting factors. Data acquisition mainly relied on three methods:on-site observation, remote sensing, and simulation. Measurements of cooling effects could be classified in terms of the choice of reference, i.e., spatially-based reference, temporally- or scenario-based reference, and statistical relationship. The last part mainly used the traditional statistical method, such as multiple regression and correlation analysis, while the machine learning methods with the potential to explore the non-linear influences have been gradually applied. (3) The cooling effects of UGI rely mainly on three physical mechanisms:shading, evapotranspiration, and advection of cool air mass. The influential factors could be classified into the plant species composition and morphology, spatial configuration, and external environment factors, e.g., the urban geometry and meteorological parameters. Moreover, the main impacting factors vary with the research scales, and with the increase in the scale, the spatial configuration of UGI becomes more important. Finally, we discussed the current gaps and challenges on the spatial-temporal scales, physical mechanisms, comprehensive impact factors, and perspective of ecosystem service. Besides, we emphasized the prospects of the new data acquisition methods, the combination of multiple cooling measures, and the cooling effects of UGI at the city scale as the future research directions.