我国城市大气环境与周边区域二维和三维景观格局关系

Relationship between urban atmospheric environment and surrounding two-dimensional and three-dimensional landscape pattern in China

城市内部的大气环境受到周边区域景观格局的剧烈影响,小尺度上大气污染状况与周边区域景观格局的关系研究对从城市建设角度减缓城市大气污染有着重要现实意义。本研究以2017年中国30个省会城市的266个大气污染监测站点的NO_(2)、SO_(2)、PM_(2.5)和PM_(10)年均浓度为因变量,选择监测站点周边3 km区域内的10个二维和三维景观格局指数(建筑物数量、建筑物聚集度、建筑物密度、不透水面比例、餐饮数量密度、建筑占地面积、建筑高层比、容积率、建筑面积和建筑物类型Shannon多样性指数)为自变量,利用增强回归树模型研究景观格局对4种大气污染物浓度的影响。结果表明:4种大气污染物浓度在空间分布上总体呈现出中部和北部城市明显高于东南沿海城市和西南部城市。NO_(2)、SO_(2)、PM_(2.5)和PM_(10)浓度的最大影响因素均为不透水面比例,其相对影响贡献率分别为40.7%、36.3%、51.0%和51.8%。不同区域大气污染浓度最主要影响因子识别结果表明,华东和华中地区为不透水面比例;华南地区为建筑物数量和建筑物密度;华北地区是不透水面比例和建筑物类型多样性;东北地区是不透水面比例和建筑物数量;西南地区是建筑物类型多样性;西北地区是建筑物密度。各区域的主要影响因子差异是气候、地形、城市规划等因素所致。

Atmospheric environment in urban built-up area is severely influenced by the surrounding landscape pattern. Understanding the relationship between air pollution and surrounding landscape pattern at small scale has great significance for mitigating air pollution from the perspective of urban construction. The annual average concentrations of NO_(2), SO_(2), PM_(2.5) and PM_(10) from 266 air pollution monitoring stations in 30 provincial capitals of China in 2017 were chosen as dependent variables. Ten two-dimensional and three-dimensional landscape pattern indices(number of buildings, building aggregation, building density, impervious water ratio, quantitative density of catering, building footprint area, high building ratio, floor area ratio, total building area and building type Shannon diversity index) within the 3 km area around the monitoring stations were used as independent variables. The effects of landscape pattern on the concentration of four air pollutants were analyzed using the boosted regression trees model. The results showed that the concentration of four air pollutants in the central and northern cities were significantly higher than that in the southeast coastal cities and southwest cities. The most important factor affecting the concentrations of NO_(2), SO_(2), PM_(2.5) and PM_(10) was the impervious ratio, with relative contribution rates of 40.7%, 36.3%, 51.0% and 51.8% respectively. The results of sub-region analysis showed that the most important influencing factor differed in different regions, including the impervious ratio in the East and Central China;the number and density of buildings in South China;the impervious ratio and diversity of building types in North China;the impervious ratio and the number of buildings in Northeast China, the density of buildings in Northwest China. Such differences were mainly caused by climate, topography, urban planning, and other factors.