城市三维扩展的居住区灰霾效应与景观格局优化

快速城市化导致景观格局演变与生态环境过程的相互作用更加复杂，城市灰霾污染问题日益突出。明确城市景观格局与灰霾效应之间的响应关系，对理解灰霾形成和发展机制及采取有效减缓措施意义重大。城市三维景观格局的灰霾效应研究面临数据局限性、方法可靠性和过程复杂性等难题，目前仍缺乏系统、深入的研究。城市灰霾污染的三维空间分布特征，以及景观格局对其分布和输移的影响是本研究的难点。本研究参考局地气候分区体系，基于建筑三维信息提取、无人机平台和3S技术，结合实地监测与微气象数值模拟方法，分析灰霾的三维分布和输移特征。确定关键影响因子，通过关联分析探讨城市居住区景观格局特征与局地灰霾污染程度的响应关系，进而识别有利于灰霾污染物扩散的景观格局特征。本研究有助于对城市景观“格局-过程”关系的深入理解，也为阐明灰霾分布与扩散特征的时空异质性，识别灰霾风险区及优化景观格局提供新路径。

Rapid urbanization leads to a more complex interaction between landscape pattern evolution and ecological environment process, and urban haze pollution becomes increasingly serious. To clarify the response relationship between urban landscape pattern and haze effect is of great significance to understand the formation and development mechanism of haze and take effective mitigation measures. The research on the haze effect of 3-dimensional urban landscape pattern is faced with difficulties such as data limitation, method reliability and process complexity. At present, there is still a lack of systematic and in-depth research. The 3-dimensional spatial distribution characteristics of urban haze pollution and the influence of landscape pattern on its distribution and transport are the difficulties in this study. We refer to the local climate zone system, and carry out field monitoring based on 3-dimensional information extraction of buildings, unmanned aerial vehicles (UAV) platform and 3S technology. Then, combining with the micro-meteorological numerical simulation method, the 3-dimensional distribution and transport characteristics of haze are analyzed. In addition, the key influence factors are determined. And the response relationship between urban landscape pattern characteristics of residential area and local haze pollution degree is explored through correlation analysis, so as to identify the landscape pattern characteristics conducive to the diffusion of haze pollutants. This study could help to better understand the relationship between "pattern-process" of urban landscape, and also provides a new path for clarifying the spatial and temporal heterogeneity of haze distribution and diffusion characteristics, identifying haze risk areas and optimizing the landscape pattern.