【作者】 陈明； 姜佳怡；

【机构】 华中科技大学建筑与城市规划学院； 日本千叶大学；

【摘要】 城市绿色空间是缓解PM_2.5的重要方式。基于武汉主城区511个控规管理单元划分的5类功能,选取1km分辨率的全球PM_2.5浓度,以及0.8m分辨率遥感影像解译的绿色空间数据,采用空间回归模型,揭示不同功能单元中绿色空间格局对PM_2.5的影响差异。结果显示,武汉主城区功能单元的PM_2.5浓度存在显著空间自相关,且PM_2.5浓度与4个绿色空间格局指标在不同功能单元之间存在较多显著差异。各类功能单元（除教科单元）的MPS均对PM_2.5存在显著负影响,PD仅在商业、教科单元中发挥显著作用,LSI与AI仅在居住、教科单元中对PM_2.5有影响。这些指标对PM_2.5的相对重要性为:MPS>PD>AI>LSI。研究为城市不同功能单元的绿色空间格局优化提供了一定指导建议。更多还原

【Abstract】 Urban green spaces play an important role in PM_2.5 reduction.This study revealed the impacts of urban green space pattern on PM2.5 among different urban function units by using spatial regression models based on 511 zoning units in the main urban area of Wuhan.Global PM_2.5 concentration data with 1 km spatial resolution and green space data interpreted from remote sensing image with 0.8 m resolution were collected for analysis.Results show that PM_2.5 concentration in function units was significant spatial autocorrelation in the main urban area of Wuhan.PM_2.5 level and four green space pattern indicators were significant differences among different function units.Except for education units,MPS had a significant negative impact on PM_2.5.PD played a significant role in business and education units,and LSI and AI affected PM_2.5 in residential and education units.The relative importance of these indicators to PM_2.5 was MPs>PD>AI>LSI.The study provides some suggestions for the optimization of green space pattern in different function units.更多还原