Index Geophysics

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

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.

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

aerosol optical depth

PM2.5

satellite retrieving

Mixed-Effect Model

Chemical Transport Model

Meteorology. Climatology

QC851-999

MDPI AG

2016-10-01T00:00:00Z

article

2073-4433

10.3390/atmos7100129

https://doaj.org/article/bc080f2abf06468bbfe7e56e63212e5c

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

EN

http://www.mdpi.com/2073-4433/7/10/129

https://doaj.org/toc/2073-4433

Journal Licence: CC BY