Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship

Hone Jay Chu, Bo Huang, Chuan Yao Lin

Research output: Contribution to journalArticlepeer-review

93 Citations (Scopus)


This paper explores the spatio-temporal patterns of particulate matter (PM) in Taiwan based on a series of methods. Using, fuzzy c-means clustering first, the spatial heterogeneity (six clusters) in the PM data collected between 2005 and 2009 in Taiwan are identified and the industrial and urban areas of Taiwan (southwestern, west central, northwestern, and northern Taiwan) are found to have high PM concentrations. The PM10-PM2.5 relationship is then modeled with global ordinary least squares regression, geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR). The GTWR and GWR produce consistent results; however, GTWR provides more detailed information of spatio-temporal variations of the PM10-PM2.5 relationship. The results also show that GTWR provides a relatively high goodness of fit and sufficient space-time explanatory power. In particular, the PM2.5 or PM10 varies with time and space, depending on weather conditions and the spatial distribution of land use and emission patterns in local areas. Such information can be used to determine patterns of spatio-temporal heterogeneity in PM that will allow the control of pollutants and the reduction of public exposure.

Original languageEnglish
Pages (from-to)176-182
Number of pages7
JournalAtmospheric Environment
Issue number1
Publication statusPublished - 2015 Feb 1

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Atmospheric Science


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