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Atmosphere 2016, 7(10), 129; doi:10.3390/atmos7100129

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
The National Environmental Satellite, Data, and Information Service (NESDIS), National Oceanic and Atmospheric Administration (NOAA), 5830 University Research Court, College Park, MD 20740, USA
International Baccalaureate Diploma Program, Wuhan Foreign Languages School, Wan Song Yuan Road, Wuhan 430022, China
Environmental Health Laboratory, Department of Public Health Sciences, University of Hawaii at Manoa, 1960 East-West Road, Honolulu, HI 96822, USA
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129# Luoyu Road, Wuhan 430079, China
College of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Robert W. Talbot
Received: 25 July 2016 / Revised: 26 September 2016 / Accepted: 5 October 2016 / Published: 14 October 2016
View Full-Text   |   Download PDF [1179 KB, uploaded 14 October 2016]   |  


This study reviewed the prediction of fine particulate matter (PM2.5) from satellite aerosol optical depth (AOD) and summarized the advantages and limitations of these predicting models. A total of 116 articles were included from 1436 records retrieved. The number of such studies has been increasing since 2003. Among these studies, four predicting models were widely used: Multiple Linear Regression (MLR) (25 articles), Mixed-Effect Model (MEM) (23 articles), Chemical Transport Model (CTM) (16 articles) and Geographically Weighted Regression (GWR) (10 articles). We found that there is no so-called best model among them and each has both advantages and limitations. Regarding the prediction accuracy, MEM performs the best, while MLR performs worst. CTM predicts PM2.5 better on a global scale, while GWR tends to perform well on a regional level. Moreover, prediction performance can be significantly improved by combining meteorological variables with land use factors of each region, instead of only considering meteorological variables. In addition, MEM has advantages in dealing with the AOD data with missing values. We recommend that with the help of higher resolution AOD data, future works could be focused on developing satellite-based predicting models for the prediction of historical PM2.5 and other air pollutants. View Full-Text
Keywords: aerosol optical depth; PM2.5; satellite retrieving; Mixed-Effect Model; Chemical Transport Model aerosol optical depth; PM2.5; satellite retrieving; Mixed-Effect Model; Chemical Transport Model

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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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Chu, Y.; Liu, Y.; Li, X.; Liu, Z.; Lu, H.; Lu, Y.; Mao, Z.; Chen, X.; Li, N.; Ren, M.; Liu, F.; Tian, L.; Zhu, Z.; Xiang, H. A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth. Atmosphere 2016, 7, 129.

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