Spatial correlation of satellite-derived PM2.5 with hospital admissions for respiratory diseases

Ching Ju Liu, Chian Yi Liu, Ngoc Thi Mong, Charles C.K. Chou

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Respiratory diseases, particularly allergic rhinitis, are spatially and temporally correlated with the ground PM2.5 level. A study of the correlation between the two factors should therefore account for spatiotemporal variations. Satellite observation has the advantage of wide spatial coverage over pin-point style ground-based in situ monitoring stations. Therefore, the current study used both ground measurement and satellite data sets to investigate the spatial and temporal correlation of satellite-derived PM2.5 with respiratory diseases. This study used 4-year satellite data and PM2.5 levels of the period at eight stations in Taiwan to obtain the spatial and temporal relationship between aerosol optical depth (AOD) and PM2.5. The AOD-PM2.5 model was further examined using the cross-validation (CV) technique and was found to have high reliability compared with similar models. The model was used to obtain satellite-derived PM2.5 levels and to analyze the hospital admissions for allergic rhinitis in 2008. The results suggest that adults (18-65 years) and children (3-18 years) are the most vulnerable groups to the effect of PM2.5 compared with infants and elderly people. This result may be because the two affected age groups spend longer time outdoors. This result may also be attributed to the long-range PM2.5 transport from upper stream locations and the atmospheric circulation patterns, which are significant in spring and fall. The results of the current study suggest that additional environmental factors that might be associated with respiratory diseases should be considered in future studies.

Original languageEnglish
Article number914
JournalRemote Sensing
Volume8
Issue number11
DOIs
StatePublished - 2016

Keywords

  • Aerosol optical depth
  • Allergic rhinitis
  • PM

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