DOI: 10.1016/j.atmosenv.2014.11.062
Scopus记录号: 2-s2.0-84949115608
论文题名: Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship
作者: Chu H ; -J ; , Huang B ; , Lin C ; -Y
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 102, 期: 1 起始页码: 176
结束页码: 182
语种: 英语
英文关键词: GTWR
; GWR
; Particulate matter
; PM10-PM2.5 relation
; Spatial clustering
; Spatio-temporal variation
Scopus关键词: Balloons
; Land use
; Regression analysis
; GTWR
; GWR
; Particulate Matter
; PM10-PM2.5 relation
; Spatial clustering
; Spatio-temporal variation
; Particles (particulate matter)
; anthropogenic source
; atmospheric pollution
; cluster analysis
; industrial location
; land use
; particle size
; particulate matter
; regression analysis
; spatiotemporal analysis
; urban area
; air monitoring
; air pollutant
; Article
; cluster analysis
; exhaust gas
; geographically and temporally weighted regression analysis
; geographically weighted regression analysis
; industrial area
; land use
; particulate matter
; priority journal
; regression analysis
; rural area
; Taiwan
; urban area
; weather
; Taiwan
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: 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. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82037
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong; Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Recommended Citation:
Chu H,-J,, Huang B,et al. Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship[J]. Atmospheric Environment,2015-01-01,102(1)