A review on predicting ground PM2.5 concentration using satellite aerosol optical depth

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

Research output: Contribution to journalReview articlepeer-review

153 Scopus citations

Abstract

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 GeographicallyWeighted 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.

Original languageEnglish
Article number129
JournalAtmosphere
Volume7
Issue number10
DOIs
StatePublished - Oct 14 2016

Bibliographical note

Publisher Copyright:
© 2016 by the authors.

Keywords

  • Aerosol optical depth
  • Chemical transport model
  • Mixed-Effect Model
  • PM
  • Satellite retrieving

ASJC Scopus subject areas

  • Environmental Science (miscellaneous)

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