Next Article in Journal
Measurements and Analysis of Polycyclic Aromatic Hydrocarbons near a Major Interstate
Previous Article in Journal
Satellite Assessments of Tropopause Dry Intrusions Correlated to Mid-Latitude Storms
Open AccessReview

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

1
Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
2
Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
3
The National Environmental Satellite, Data, and Information Service (NESDIS), National Oceanic and Atmospheric Administration (NOAA), 5830 University Research Court, College Park, MD 20740, USA
4
International Baccalaureate Diploma Program, Wuhan Foreign Languages School, Wan Song Yuan Road, Wuhan 430022, China
5
Environmental Health Laboratory, Department of Public Health Sciences, University of Hawaii at Manoa, 1960 East-West Road, Honolulu, HI 96822, USA
6
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129# Luoyu Road, Wuhan 430079, China
7
College of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Robert W. Talbot
Atmosphere 2016, 7(10), 129; https://doi.org/10.3390/atmos7100129
Received: 25 July 2016 / Revised: 26 September 2016 / Accepted: 5 October 2016 / Published: 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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop