基于LUR模型探究城市景观格局对PM2.5浓度的影响——以长株潭城市群为例

随着城市化进程的加快，空气污染问题已成为中国最主要城市问题之一，严重影响公众健康。当前微观尺度下空气监测点周围景观格局对PM25浓度影响的研究较少，以长株潭城市群为例，选取地形、污染源、人口、道路交通、土地利用与城市景观格局6大类预测变量，其中城市景观格局选取边缘密度、连续度、形状指数、斑块平均面积、蔓延度、均匀度指数7个景观指数，运用逐步线性回归模型，探究城市景观格局对PM25浓度的影响。研究结果显示：（1）所选取的土地景观格局指数可以解释研究区PM25浓度的732%的变异，模型拟合较好；（2）影响PM25浓度的土地利用类型包括建设用地、林地、草地与水体。微观尺度下城市各类型景观格局中连续度和形状指数对PM25影响显著，建设用地连续度越高，分布越集聚，PM25浓度越高；水体形状指数越小，形状越简单规则，越易降低PM25浓度；（3）城市整体景观格局中，景观聚集程度与景观多样性等因素对PM25浓度产生重要影响。减少景观内各类型斑块的离散分布，使各景观类型均匀分布于整体景观内，有助于降低PM25浓度。研究结果可为未来大气防治与城市规划提供参考依据。〖HJ1〗〖HJ〗〖JP+1〗

Investigating the Effect of Urban Landscape Pattern on PM2.5 Concentration Based on LUR Model: A Chang-Zhu-Tan Urban Agglomeration Case Study

With the acceleration of urbanization, air pollution has become one of the most important urban problems in China, affecting public health seriously. At present, there are few studies to explore the influence of landscape pattern on PM2.5 concentration at the microscopic scale. Therefore, the current study, taking the Chang-Zhu-Tan urban agglomeration as an example, selected six predictors including terrain, pollution, population, road traffic, land use and urban landscape pattern, then used a stepwise linear regression model to explore the effects of urban landscape pattern on PM2.5 concentration. The urban landscape pattern was represented by seven landscape metrics which were ED, CONTIG, LSI, AREA_MN, CONTAG and SHEI. The results showed that: 1) the selected landscapes metrics could explain 73.2% variation of PM2.5 concentration in the study area and the model fitted well; 2) the types of land use that affected PM2.5 concentration included construction land, woodland, grassland and water body. At the microscopic scale, landscape metrics including CONTIG and LSI had significant effects on PM2.5. When the contiguity of construction land was higher, the distribution was more concentrated. Consequently, PM2.5 concentration was higher. If the shape index of water body was smaller, the shape of water body was simpler and more regular. It was easier to reduce PM2.5 concentration; 3) at the overall urban landscape-level, landscape aggregation degree and landscape diversity had important impacts on PM2.5 concentration. Reducing the discrete distribution of various types of patches in the landscape, which could make each landscape type evenly distribute in the overall landscape, could help to reduce PM2.5 concentration. Results of this research could provide reference for future atmosphere control and urban planning.