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

doi:10.16511/j.cnki.qhdxxb.2017.22.037

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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.

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

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Issue Date: 15 May 2017

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	LIU Zhao
	XIE Meihui
	TIAN Kun
	XIE Xiaoxiao

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LIU Zhao,XIE Meihui,TIAN Kun, et al. 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.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.22.037 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I5/555

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