A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship
https://orcid.org/0000-0002-5063-3522
Abstract 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.
Highlights This study explores the spatio-temporal patterns of particulate matter (PM). Spatial heterogeneity of the PM data is identified using fuzzy clustering. PM10-PM2.5 relationship is modeled by GWR and GTWR. GTWR provides spatio-temporal variations of the PM10-PM2.5 relationship.
Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship
https://orcid.org/0000-0002-5063-3522
Abstract 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.
Highlights This study explores the spatio-temporal patterns of particulate matter (PM). Spatial heterogeneity of the PM data is identified using fuzzy clustering. PM10-PM2.5 relationship is modeled by GWR and GTWR. GTWR provides spatio-temporal variations of the PM10-PM2.5 relationship.
Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship
https://orcid.org/0000-0002-5063-3522
Abstract 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.
Highlights This study explores the spatio-temporal patterns of particulate matter (PM). Spatial heterogeneity of the PM data is identified using fuzzy clustering. PM10-PM2.5 relationship is modeled by GWR and GTWR. GTWR provides spatio-temporal variations of the PM10-PM2.5 relationship.
Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship
Chu, Hone-Jay (author) / Huang, Bo (author) / Lin, Chuan-Yao (author)
Atmospheric Environment ; 102 ; 176-182
2014-11-27
7 pages
Article (Journal)
Electronic Resource
English
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