DOI: 10.1016/j.atmosenv.2018.03.017
Scopus记录号: 2-s2.0-85043984981
论文题名: An improved geographically weighted regression model for PM2.5 concentration estimation in large areas
作者: Zhai L ; , Li S ; , Zou B ; , Sang H ; , Fang X ; , Xu S
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 181 起始页码: 145
结束页码: 154
语种: 英语
英文关键词: China
; Collinearity
; GWR
; PCA-GWR
; PM2.5
; Remote sensing
Scopus关键词: Regression analysis
; Remote sensing
; China
; Collinearity
; Correlation analysis
; Geographically weighted regression models
; PCA-GWR
; Performance comparison
; PM2.5
; Pollution concentration
; Principal component analysis
; concentration (composition)
; correlation
; environmental factor
; numerical model
; particulate matter
; principal component analysis
; regression analysis
; remote sensing
; China
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: Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables’ contributions to PM2.5 variations. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82900
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: National Geographic Conditions Monitoring Research Center, Chinese Academy of Surveying and Mapping, Beijing, China; College of Geomatics, Shandong University of Science and Technology, Qingdao, China; School of Geosciences and Info-Physics, Central South University, Changsha, China
Recommended Citation:
Zhai L,, Li S,, Zou B,et al. An improved geographically weighted regression model for PM2.5 concentration estimation in large areas[J]. Atmospheric Environment,2018-01-01,181