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

清华大学学报(自然科学版) ›› 2017, Vol. 57 ›› Issue (5) : 555-560.

PDF(1897 KB)
PDF(1897 KB)
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

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
作者信息 +

Classification of PM2.5 for natural cities based on co-Kriging and head/tail break algorithms

  • LIU Zhao, XIE Meihui, TIAN Kun, XIE Xiaoxiao
Author information +
文章历史 +

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。

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.

关键词

PM2.5空气污染 / 协同Kriging插值 / 首尾分割 / 自然城市

Key words

PM2.5 air pollution / co-Kriging interpolation / head/tail break / natural city

引用本文

导出引用
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 https://doi.org/10.16511/j.cnki.qhdxxb.2017.22.037
LIU Zhao, XIE Meihui, TIAN Kun, XIE Xiaoxiao. Classification of PM2.5 for natural cities based on co-Kriging and head/tail break algorithms[J]. Journal of Tsinghua University(Science and Technology). 2017, 57(5): 555-560 https://doi.org/10.16511/j.cnki.qhdxxb.2017.22.037
中图分类号: X513   

参考文献

[1] 郑思齐, 张晓楠, 宋志达, 等. 空气污染对城市居民户外活动的影响机制: 利用点评网外出就餐数据的实证研究[J]. 清华大学学报 (自然科学版), 2016, 56(1): 89-96.ZHENG Siqi, ZHANG Xiaonan, SONG Zhida, et al. Influence of air pollution on urban residents' outdoor activity: Empirical study based on dining-out data from the Dianping Website [J]. J Tsinghua Univ (Sci and Tech), 2016, 56(1): 89-96. (in Chinese) [2] 施益强, 王坚, 张枝萍. 厦门市空气污染的空间分布及其与影响因素空间相关性分析[J]. 环境工程学报, 2014(12): 5406-5412.SHI Yiqiang, WANG Jian, ZHANG Zhiping. Analysis on spatial distribution of air pollution and its spatial correlation with influencing factors in Xiamen City [J]. Chinese Journal of Environmental Engineering, 2014(12): 5406-5412. (in Chinese) [3] Hoek G, Beelen R, De Hoogh K, et al. A review of land-use regression models to assess spatial variation of outdoor air pollution [J]. Atmospheric Environment, 2008, 42(33): 7561-7578. [4] 易湘生, 李国胜, 尹衍雨, 等. 土壤厚度的空间插值方法比较: 以青海三江源地区为例[J]. 地理研究, 2012, 31(10): 1793-1805.YI Xiangsheng, LI Guosheng, YIN Yanyu, et al. Comparison on soil depth prediction among different spatial interpolation methods: A case study in the Three-River Headwaters Regions of Qinghai Province [J]. Geographic Research, 2012, 31(10): 1793-1805. (in Chinese) [5] 张成才, 秦昆, 卢艳, 等. GIS 空间分析理论与方法[M]. 武汉: 武汉大学出版社, 2004.ZHANG Chengcai, QIN Kun, LU Yan, et al. Theories and Methods of Spatial Analysis in GIS [M]. Wuhan: Wuhan University Press, 2004. [6] Sampson P D, Richards M, Szpiro A A, et al. A regionalized national universal kriging model using partial least squares regression for estimating annual PM 2.5 concentrations in epidemiology [J]. Atmospheric Environment, 2013, 75: 383-392. [7] Van D A, Martin R V, Park R J. Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing [J]. Journal of Geophysical Research: Atmospheres, 2006, 111, D21201. [8] XIE Yuanyu, WANG Yuxuan, ZHANG Kai, et al. Daily estimation of ground-level PM2.5 concentrations over Beijing using 3 km resolution MODIS AOD [J]. Environmental Science & Technology, 2015, 49(20): 12280-12288. [9] LONG Ying, WANG Jianghao, WU Kang, et al. Population Exposure to Ambient PM2.5 at the Subdistrict Level in China [Z/OL]. (2014-08-25) [2016-02-15]. https://ssrn.com/ab-stract=2486602. [10] LIU Qingling. A Case Study on the Extraction of the Natural Cities from Nightlight Image of the United States of America [D]. Gävle, Sweden: University of Gävle, 2013. [11] JIA Tao, JIANG Bing. Measuring Urban Sprawl Based on Massive Street Nodes and the Novel Concept of Natural Cities [Z/OL]. (2010-12-08) [2016-02-15]. https://arxiv.org/abs/1010.0541. [12] JIANG Bing. Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution [J]. The Professional Geographer, 2013, 65(3): 482-494. [13] JIANG Bing, YIN Junjun. Ht-index for quantifying the fractal or scaling structure of geographic features [J]. Annals of the Association of American Geographers, 2014, 104(3): 530-540. [14] GAO Peichao, LIU Zhao, XIE Meihui, et al. CRG index: A more sensitive ht-index for enabling dynamic views of geographic features [J]. The Professional Geographer, 2016, 68(4): 533-545. [15] 张小娟. 基于 MODIS 遥感 DT 和 DB 数据集的中国 AOD 分布与变化[D]. 南京: 南京信息工程大学, 2014.ZHANG Xiaojuan. The Distribution and Variation of AOD over China Based on MODIS Remote Sensing DT and DB Data Set [D]. Nanjing: Nanjing University of Information Science and Technology, 2014. (in Chinese) [16] 孙晓雷, 甘伟, 林燕, 等. MODIS 3 km 气溶胶光学厚度产品检验及其环境空气质量指示[J]. 环境科学学报, 2015, 35(6): 1657-1666.SUN Xiaolei, GAN Wei, LIN Yan, et al. Validation of MODIS 3 km aerosol optical depth product and its air quality indication [J]. Acta Science Circumstantiae, 2015, 35(6): 1657-1666. (in Chinese) [17] ZHOU Chunyan, LIU Qinhuo, TANG Yong, et al. Comparison between MODIS aerosol product C004 and C005 and evaluation of their applicability in the north of China [J]. Journal of Remote Sensing, 2009(5): 854-872. [18] YOU Wei, ZANG Zengliang, ZHANG Lifeng, et al. National-scale estimates of ground-level PM2.5 concentration in China using geographically weighted regression based on 3 km resolution MODIS AOD [J]. Remote Sensing, 2016, 8(3), 184.

PDF(1897 KB)

Accesses

Citation

Detail

段落导航
相关文章

/