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Atmosphere 2016, 7(7), 88; doi:10.3390/atmos7070088

Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model

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 Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China
*
Author to whom correspondence should be addressed.
Academic Editor: Robert W. Talbot
Received: 17 May 2016 / Revised: 21 June 2016 / Accepted: 22 June 2016 / Published: 27 June 2016
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Abstract

The estimation of ambient particulate matter with diameter less than 10 µm (PM10) at high spatial resolution is currently quite limited in China. In order to make the distribution of PM10 more accessible to relevant departments and scientific research institutions, a semi-physical geographically weighted regression (GWR) model was established in this study to estimate nationwide mass concentrations of PM10 using easily available MODIS AOD and NCEP Reanalysis meteorological parameters. The results demonstrated that applying physics-based corrections could remarkably improve the quality of the dataset for better model performance with the adjusted R2 between PM10 and AOD increasing from 0.08 to 0.43, and the fitted results explained approximately 81% of the variability in the corresponding PM10 mass concentrations. Annual average PM10 concentrations estimated by the semi-physical GWR model indicated that many residential regions suffer from severe particle pollution. Moreover, the deviation in estimation, which primarily results from the frequent changes in elevation, the spatially heterogeneous distribution of monitoring sites, and the limitations of AOD retrieval algorithm, was acceptable. Therefore, the semi-physical GWR model provides us with an effective and efficient method to estimate PM10 at large scale. The results could offer reasonable estimations of health impacts and provide guidance on emission control strategies in China. View Full-Text
Keywords: national-scale PM10; aerosol optical depth; physics-based revision; geographically weighted regression; reanalysis meteorological data national-scale PM10; aerosol optical depth; physics-based revision; geographically weighted regression; reanalysis meteorological data
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.; Zhu, Z.; Sun, K.; Huang, Y.; Ji, Y. Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model. Atmosphere 2016, 7, 88.

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