LI Shuang, ZHAI Liang, SANG Huiyong, ZHOU Bin, FANG Xin, ZHEN Yunpeng. An Improved LUR-based Spatial Distribution Simulation for the Large Area PM2.5 Concentration[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1574-1579, 1587. DOI: 10.13203/j.whugis20170042
Citation: LI Shuang, ZHAI Liang, SANG Huiyong, ZHOU Bin, FANG Xin, ZHEN Yunpeng. An Improved LUR-based Spatial Distribution Simulation for the Large Area PM2.5 Concentration[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1574-1579, 1587. DOI: 10.13203/j.whugis20170042

An Improved LUR-based Spatial Distribution Simulation for the Large Area PM2.5 Concentration

Funds: 

Thematic Monitoring of the First Package of National Geographic Situation Monitoring Project in 2017(2nd Batch) Q1722

the National Natural Science Foundation of China 41701213

Basic Research Funding in CASM 7771716

Open Fund from the Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation 2016NGCMZD03

the National Natural Science Youth Fund Project of China 41501192

More Information
  • Author Bio:

    LI Shuang, master, specializes in the information statistics of geographical conditions monitoring. E-mail:ls02020029@163.com

  • Corresponding author:

    SANG Huiyong, PhD. E-mail:huiyong.sang@casm.ac.cn

  • Received Date: February 27, 2017
  • Published Date: October 04, 2018
  • There exists the shortage of traditional land use regression (LUR) model in losing information of predictor variables when simulating the air pollutant concentration. An improved model which combined principal component regression (PCR) and stepwise multiple line regression (SMLR)-LUR (PCA+SMLR) was developed to simulate the spatial distribution of PM2.5 in large area. Firstly, the correlation analysis was conducted to screen out effective predictor variables. Secondly, principal component analysis (PCA) was employed to transform effective predictor variables to principle components. Finally, all principal components were used to conduct SMLR to simulate the spatial distribution of PM2.5. Meanwhile, the reliability of the improved model was tested in Beijing-Tianjin-Hebei urban agglomeration. Experimental results of three models (PCR, SMLR and PCA+SMLR) were compared and analyzed. The results indicated that the PCA+SMLR model has an adjusted R2 of 0.883 by improving the contribution of the predictor variables. Besides, it is better than the traditional mo-del for accuracy index and the mapping results. Therefore, it can be concluded that the PCA+SMLR is a promising PM2.5 modeling method and could be very use-ful for air pollution mapping.
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