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应用生态学报 ›› 2021, Vol. 32 ›› Issue (5): 1593-1602.doi: 10.13287/j.1001-9332.202105.015

• 景观生态学专栏 • 上一篇    下一篇

我国城市大气环境与周边区域二维和三维景观格局关系

李迪康1,2, 刘淼1*, 李春林1, 胡远满1, 王聪1,2, 刘冲1,2   

  1. 1中国科学院沈阳应用生态研究所, 中国科学院森林生态与管理重点实验室, 沈阳 110016;
    2中国科学院大学, 北京 100049
  • 收稿日期:2020-12-16 接受日期:2021-02-03 出版日期:2021-05-15 发布日期:2021-11-15
  • 通讯作者: *E-mail: lium@iae.ac.cn
  • 作者简介:李迪康,男,1996年生,硕士研究生。主要从事城市景观生态研究。E-mail:lidikang25@163.com
  • 基金资助:
    国家自然科学基金项目(32071580,41871192)和国家自然科学基金重点基金项目(41730647)资助

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

LI Di-kang1,2, LIU Miao1*, LI Chun-lin1, HU Yuan-man1, WANG Cong1,2, LIU Chong1,2   

  1. 1Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China;
    2University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-12-16 Accepted:2021-02-03 Online:2021-05-15 Published:2021-11-15
  • Contact: *E-mail: lium@iae.ac.cn
  • Supported by:
    Natural Science Foundation of China (32071580, 41871192) and the Key Program of National Natural Science Foundation of China (41730647).

摘要: 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NO2、SO2、PM2.5和PM10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。NO2、SO2、PM2.5和PM10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。

关键词: 二维城市景观格局, 三维城市景观格局, 大气污染物, 增强回归树, 影响因素

Abstract: 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 NO2, SO2, PM2.5 and PM10 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 NO2, SO2, PM2.5 and PM10 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.

Key words: two-dimensional urban landscape pattern, three-dimensional urban landscape pattern, atmospheric pollutant, boosted regression tree, influencing factor