An Improved LUR-based Spatial Distribution Simulation for the Large Area PM<sub>2.5</sub> Concentration

LI Shuang; ZHAI Liang; SANG Huiyong; ZHOU Bin; FANG Xin; ZHEN Yunpeng

doi:10.13203/j.whugis20170042

Volume 43 Issue 10

Oct. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(10): 1574-1579, 1587. > DOI: 10.13203/j.whugis20170042

LI Shuang, ZHAI Liang, SANG Huiyong, ZHOU Bin, FANG Xin, ZHEN Yunpeng. An Improved LUR-based Spatial Distribution Simulation for the Large Area PM_2.5 Concentration[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1574-1579, 1587. DOI: 10.13203/j.whugis20170042

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An Improved LUR-based Spatial Distribution Simulation for the Large Area PM_2.5 Concentration

LI Shuang^1,,
ZHAI Liang^{2, 3},
SANG Huiyong^{2, 3, ,},
ZHOU Bin⁴,
FANG Xin⁴,
ZHEN Yunpeng⁵

1.
Shenzhen Investigation & Research Institute, Shenzhen 518026, China
2.
Chinese Academy of Surveying & Mapping, Beijing 100830, China
3.
Key Laboratory of Earth Observation and Geospatial Information Science of NASG, Beijing 100830, China
4.
School of Geosciences and Info-physics, Central South University, Changsha 410083, China
5.
Wuhan Geomatics Institute, Wuhan 430000, China

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

Graphical Abstract

Abstract

Abstract

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 PM_2.5 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 PM_2.5. 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 PM_2.5 modeling method and could be very use-ful for air pollution mapping.
- PM_2.5,
- PCA,
- SMLR,
- contribution,
- spatial distribution

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References

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