Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship
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AbstractThis 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. (C) 2014 Elsevier Ltd. All rights reserved.
All Author(s) ListChu HJ, Huang B, Lin CY
Journal nameAtmospheric Environment
Year2015
Month2
Day1
Volume Number102
PublisherElsevier
Pages176 - 182
ISSN1352-2310
eISSN1873-2844
LanguagesEnglish-United Kingdom
KeywordsGTWR; GWR; Particulate matter; PM10-PM2.5 relation; Spatial clustering; Spatio-temporal variation
Web of Science Subject CategoriesEnvironmental Sciences; Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences

Last updated on 2020-03-12 at 00:18