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

Chu, Yuanyuan; Liu, Yisi; Li, Xiangyu; Liu, Zhiyong; Lu, Hanson; Lu, Yuanan; Mao, Zongfu; Chen, Xi; Li, Na; Ren, Meng; Liu, Feifei; Tian, Liqiao; Zhu, Zhongmin; Xiang, Hao

doi:10.3390/atmos7100129

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

2016
By Chu, Yuanyuan ; Liu, Yisi ; Li, Xiangyu ; ...
Source: Atmosphere 2016, 7(10), 129

[PDF-1.15 MB]

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Journal Title:

Atmosphere
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Chu, Yuanyuan ; Liu, Yisi ; Li, Xiangyu ; Liu, Zhiyong ; Lu, Hanson ; ... More +

Chu, Yuanyuan ; Liu, Yisi ; Li, Xiangyu ; Liu, Zhiyong ; Lu, Hanson ; Lu, Yuanan ; Mao, Zongfu ; Chen, Xi ; Li, Na ; Ren, Meng ; Liu, Feifei ; Tian, Liqiao ; Zhu, Zhongmin ; Xiang, Hao Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service)
Description:

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.
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Aerosol Optical Depth Chemical Transport Model Mixed-Effect Model PM2.5 Satellite Retrieving
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Atmosphere 2016, 7(10), 129
DOI:

https://doi.org/10.3390/atmos7100129
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Journal Article
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CC BY
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urn:sha256:7165733d39ba10785661920f2365c92b89e3005cbec1d644a9ea9f0e894548db
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A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

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