摘要
分析城市可吸人颗粒物的空间特征及影响因素对于制定更为有效的可吸入颗粒物污染控制政策具有重要的现实意义．本研究在实际采样和遥感数据处理基础上，构造差值植被指数（DVI），通过建立TM影像与对应PM5．0颗粒物实测值之间的相关关系模型，对北京市2008—2010年3a的PM5．0的分布进行了反演计算和精度分析；研究了NDBI和NDMI对PM5．0浓度的影响，进一步揭示了北京五环区域范围内可吸人颗粒物的时空分布特征．结果表明，采用DVI反演PM5．0分布的方法可行，反演精度在可接受范围内；整体上研究区2008年PM5．0颗粒物污染最轻，西南三环和四环以及东南三四环之间为颗粒物污染的高值区，西北五环附近颗粒物污染较轻；NDBI（归一化建筑指数）、NDMI（归一化水汽指数）与PM5．0可吸人颗粒物的分布分别呈现显著的负相关和正相关关系，且二者对PM5．0的影响相近．
Study on urban spatial characteristics of inhalable particulate and its influencing factors has an important practical significance for the development of more effective inhalable particulate pollution control policy. On the basis of the actual sampling and remote sensing data processing, a correlation model was established between the difference vegetation index （DVI） values of TM images and the measured values of the corresponding PM5.0 particulate matter, and PM5.0 distributions from 2008 to 2010 in Beijing were acquired by the use of inversion experiments and their accuracies were tested. Furthermore, the impact of NDBI and NDMI on PM5.0 was explored, as well as the spatial and temporal characteristics of inhalable particulate within the five rings of Beijing. The results showed that： ① the PM5.0 inversion method using DVI was feasible, and the inversion accuracy was acceptable; ②Overall, in 2008, PM5.0 particulate matter pollution was the lightest in the study area. The higher values of particle pollution were distributed between the southwest third ring road and southwest fourth ring road, as well as between the southeast third ring road and southeast fourth ring road, and the lower values of particle pollution were distributed around the northwest fifth ring road; ③NDBI and NDM1 had equal significant impact on inhalable particulate, which respectively showed significant negative correlation and positive correlation.
出处
《环境科学》
EI
CAS
CSCD
北大核心
2014年第2期428-435,共8页
Environmental Science
基金
国家自然科学基金项目(41371375)
北京市自然科学基金项目(8132018)
"十二五"国家科技支撑计划项目(2012BAH33B03
2012BAH33B05)
关键词
PM5
0
可吸入颗粒物
遥感反演
差值植被指数
时空分布
关联分析
PM5.0
inhalable particulate
remote sensing inversion
difference vegetation index （DVI）
spatial-temporal distribution
correlation analysis
