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Int. J. Environ. Res. Public Health 2016, 13(12), 1215; doi:10.3390/ijerph13121215

Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI)

1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China
3
College of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Jamal Jokar Arsanjani
Received: 21 September 2016 / Revised: 30 November 2016 / Accepted: 5 December 2016 / Published: 7 December 2016
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Abstract

Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 μm: PM2.5) is currently quite limited in China. By introducing NO2 and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM2.5 mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO2 and EVI, where cross-validation R2 increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM2.5 pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM2.5 pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM2.5 still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO2 and EVI in GWR models could more effectively estimate PM2.5 at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation in China. View Full-Text
Keywords: nationwide ambient PM2.5; MODIS (Moderate Resolution Imaging Spectroradiometer) AOD; satellite-derived NO2 column density; enhanced vegetation index; geographically weighted regression nationwide ambient PM2.5; MODIS (Moderate Resolution Imaging Spectroradiometer) AOD; satellite-derived NO2 column density; enhanced vegetation index; geographically weighted regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, T.; Gong, W.; Wang, W.; Ji, Y.; Zhu, Z.; Huang, Y. Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI). Int. J. Environ. Res. Public Health 2016, 13, 1215.

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