摘要
首先基于遥感平台的遥感影像数据,提取北京市4期各年份不透水表面数据并反演AOD值;然后,采用M估计稳健回归的思想,对AOD值与监测站点PM2.5数据进行回归分析,建立回归模型.根据回归模型和反演的AOD数据,生成空间连续的PM2.5质量浓度数据.最后,探讨城市扩张对PM2.5污染时空分布及演变的影响机制,定量分析两者关系.结果显示:北京市2000、2006、2012年和2016年不透水表面面积分别为6 646.37、9 680.52、9 736.31 km2和9 769.20 km2,2000-2016年不透水表面面积增长率为46.99%,相应的PM2.5质量浓度的增长率为56.61%.北京市2000-2016年的PM2.5污染时间上呈现先加重后在波动中减轻,空间上呈现从西北-东南方向逐渐增高的趋势,严重污染区域为房山区的东部、大兴区、通州区、海淀区、朝阳区、丰台区、石景山区、东城区、西城区、前宣武区及前崇文区.在此期间,北京市不透水表面空间分布与PM2.5污染空间分布高度一致.东南方向的通州区、顺义区、平谷区和大兴区不透水表面面积增长率达到90%以上,同时这些区域PM2.5质量浓度增长值也高于西北区域.
This paper firstly extracts the impervious surface data of Beijing in four periods of each year from remote sensing image data based on the remote sensing platform and inverts the AOD value. Then, using the idea of M-estimated robust regression, regression analysis was performed on the AOD value and the monitoring site PM2.5 data, and a regression model was established. Based on the regression model and the inverted AOD data,spatially continuous PM2.5 concentration data was generated. Finally, this paper discusses the mechanism of urban sprawl’s influence on the spatial and temporal distribution and evolution of PM2.5 pollution, and quantitatively analyzes the relationship between them. The results show: In 2000, 2006, 2012 and 2016, the impervious surface areas of Beijing are respectively 6 646.37 km2, 9 680.52 km2, 9 736.31 km2 and 9 769.20 km2. Within 2000 and2016, the growth rate of impervious surface was 46.99%, and the corresponding growth rate of PM2.5 concentration was 56.61%. In the same period, the PM2.5 pollution in Beijing was first aggravated and then decreased fluctuantly.Spatially, there was a gradually increasing tendency from the northwest direction to the southeast and the serious polluted areas were the east of Fangshan District, Daxing District, Tongzhou District, Haidian District, Chaoyang District, Fengtai District, Shijingshan District, Dongcheng District, Xicheng Districtt, Xuanwu District and Chongwen District. During that period, the spatial distribution of impervious surface in Beijing was highly consistent with the spatial distribution of PM2.5 pollution. The impervious surface of Tongzhou District, Shunyi District, Pinggu District and Daxing District in the southeast of Beijing was more than 90%, and the growth of PM2.5 concentration in these regions was also higher than that in the northwest.
作者
冯娟
赵伟娜
蒋义然
冀松
翟伟芳
卢秀丽
刘永立
王艳
杜二玲
FENG Juan;ZHAO Wei-na;JIANG Yi-ran;JI Song;ZHAI Wei-fang;LU Xiu-li;LIU Yong-li;WANG Yan;DU Er-ling(School of Engineering and Technology,Baoding University of Technology,Baoding 071000,China;Department of Computer Application Engineering,Hebei Software Institute,Baoding 071000,China)
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第2期308-318,共11页
Journal of Yunnan University(Natural Sciences Edition)
基金
保定市科技计划(18ZF083).
关键词
不透水表面
PM_(2.5)污染
时空分异
遥感反演
北京市
impervious surface
PM_(2.5) pollution
spatio-temporal differentiation
remote sensing inversion
Beijing
