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


Title (Dublin Core)

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

Description (Dublin Core)

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.

Creator (Dublin Core)

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

Subject (Dublin Core)

aerosol optical depth
satellite retrieving
Mixed-Effect Model
Chemical Transport Model
Meteorology. Climatology

Publisher (Dublin Core)


Date (Dublin Core)


Type (Dublin Core)


Identifier (Dublin Core)


Source (Dublin Core)

Atmosphere, Vol 7, Iss 10, p 129 (2016)

Language (Dublin Core)


Relation (Dublin Core)

Provenance (Dublin Core)

Journal Licence: CC BY