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

Details

• Journal Title:
  Atmosphere
• Personal Author:
  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
• 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.
• Keywords:
  Aerosol Optical Depth Chemical Transport Model Mixed-Effect Model PM2.5 Satellite Retrieving
• Source:
  Atmosphere 2016, 7(10), 129
• DOI:
  https://doi.org/10.3390/atmos7100129
• Document Type:
  Journal Article
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:7165733d39ba10785661920f2365c92b89e3005cbec1d644a9ea9f0e894548db
• Download URL:
  https://repository.library.noaa.gov/view/noaa/41101/noaa_41101_DS1.pdf
• File Type:
  [PDF - 1.15 MB ]