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清华大学学报(自然科学版)  2017, Vol. 57 Issue (5): 555-560    DOI: 10.16511/j.cnki.qhdxxb.2017.22.037
  土木工程 本期目录 | 过刊浏览 | 高级检索 |
基于协同Kriging插值和首尾分割法的PM2.5自然城市提取
刘钊, 谢美慧, 田琨, 谢晓晓
清华大学 土木工程系, 地球空间信息研究所, 北京 100084
Classification of PM2.5 for natural cities based on co-Kriging and head/tail break algorithms
LIU Zhao, XIE Meihui, TIAN Kun, XIE Xiaoxiao
Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Beijing 100084, China
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摘要 PM2.5空气污染问题目前是社会关注热点以及学术研究重点。该文对PM2.5污染的自然城市提取进行了研究,结合PM2.5的站点监测数据和气溶胶遥感数据并采用协同Kriging插值实现了PM2.5数据空间化,然后采用首尾分割分类方法实现了PM2.5污染分布的分类和污染自然城市的提取。对中国大陆PM2.5自然城市的提取结果进行了分析和讨论。结果表明:采用适当的分割阈值,首尾分割分类方法可以有效进行PM2.5污染自然城市提取工作,有助于决策者合理划分PM2.5联合治理的区域范围。
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刘钊
谢美慧
田琨
谢晓晓
关键词 PM2.5空气污染协同Kriging插值首尾分割自然城市    
Abstract:PM2.5 air pollution is now a hot topic in both social and academic circles. This study investigated the classification of natural cities based on PM2.5 concentrations in Mainland China. Firstly, the PM2.5 data obtained at monitoring stations and aerosol optical depths (AOD) obtained by remote sensing were fused to yield more accurate PM2.5 spatial distributions using a co-Kriging algorithm. Then, the PM2.5 concentrations were classified using the head/tail break clustering algorithm to identify natural cities with high PM2.5 pollution levels. Distribution of natural cities was also analyzed. The results show that the head/tail break algorithm with an appropriate segmentation threshold can efficiently identify natural cities with high PM2.5 concentrations. These classification results can guide policy makers to divide the country into several areas for pollution control.
Key wordsPM2.5 air pollution    co-Kriging interpolation    head/tail break    natural city
收稿日期: 2016-03-31      出版日期: 2017-05-20
ZTFLH:  X513  
引用本文:   
刘钊, 谢美慧, 田琨, 谢晓晓. 基于协同Kriging插值和首尾分割法的PM2.5自然城市提取[J]. 清华大学学报(自然科学版), 2017, 57(5): 555-560.
LIU Zhao, XIE Meihui, TIAN Kun, XIE Xiaoxiao. Classification of PM2.5 for natural cities based on co-Kriging and head/tail break algorithms. Journal of Tsinghua University(Science and Technology), 2017, 57(5): 555-560.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.22.037  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I5/555
  图1 全国PM2.5监测站分布及MYD04_3K 分布示例
  图2 AERONET与MODISAOD 气溶胶光学厚度数据的相关性分析
  图3 2015年全国PM2.5与AOD 均值变化
  图4 2015年全国PM2.5均值分布
  表1 2015年全国PM2.5均值分布图的首尾分割统计结果
  图5 PM2.5自然城市提取结果示例
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