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Int. J. Environ. Res. Public Health 2016, 13(10), 974; doi:10.3390/ijerph13100974

Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Shanghai Institute of Satellite Engineering, Shanghai 201100, China
3
College of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China
4
Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jamal Jokar Arsanjani
Received: 25 August 2016 / Revised: 26 September 2016 / Accepted: 26 September 2016 / Published: 30 September 2016
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Abstract

The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China. View Full-Text
Keywords: real-time estimation; national-scale PM2.5; aerosol optical depth; fusion by DT and DB; semi-physical geographically weighted regression real-time estimation; national-scale PM2.5; aerosol optical depth; fusion by DT and DB; semi-physical geographically weighted regression
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MDPI and ACS Style

Zhang, T.; Liu, G.; Zhu, Z.; Gong, W.; Ji, Y.; Huang, Y. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model. Int. J. Environ. Res. Public Health 2016, 13, 974.

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