globalchange  > 气候变化与战略
DOI: 10.1016/j.atmosenv.2019.117205
论文题名:
An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China
作者: Tan H.; Chen Y.; Wilson J.P.; Zhang J.; Cao J.; Chu T.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 223
语种: 英语
英文关键词: Eigenvalues and eigenfunctions ; Mean square error ; Microchannels ; Remote sensing ; Rivers ; Static Var compensators ; Geographically weighted regression models ; PM2.5 ; Satellite remote sensing data ; Spatial filterings ; Spatially varying coefficients ; Spatiotemporal characteristics ; Spatiotemporal distributions ; Yangtze river delta ; Beamforming ; concentration (composition) ; eigenvalue ; interpolation ; particle size ; particulate matter ; remote sensing ; spatial analysis ; spatiotemporal analysis ; article ; China ; filtration ; geographically weighted regression ; neglect ; remote sensing ; river ; China ; Yangtze Delta ; Spring viremia of carp virus
学科: Eigenvector spatial filtering ; GWR ; PM2.5 ; Spatially varying coefficient ; Yangtze river delta region
中文摘要: Ordinary interpolation using PM2.5 ground monitoring observations can seldom reveal the PM2.5 concentration distribution characteristics due to the uneven distribution of monitoring stations and because ordinary linear regression often neglects the spatial autocorrelation among geographical locations. In this study, we developed an eigenvector spatial filtering based spatially varying coefficient (ESF-SVC) model to estimate ground PM2.5 concentration. To generate and analyze the spatiotemporal distribution of PM2.5 concentration in the China's Yangtze River Delta (YRD) region, ESF-SVC model which uses a set of satellite remote sensing data, factory locations, and road networks, was fitted at different time scales from December 2015 to November 2016. Comparisons among the ESF-SVC, eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models suggest that the ESF-SVC model with an average annual and seasonal adjusted R2 of 0.684, is 10.3 and 13.8% higher than the GWR and ESF models, respectively. The average annual and seasonal cross validation root mean square error (RMSE) of the ESF-SVC models lower than the GWR and ESF models. PM2.5 concentration distribution maps for annual and seasonal were produced to illustrate YRD region's spatiotemporal characteristics. In summary, an ESF-SVC model offers a reliable approach for PM2.5 concentrations estimation in large area. © 2019 Elsevier Ltd
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160950
Appears in Collections:气候变化与战略

Files in This Item:

There are no files associated with this item.


作者单位: School of Resource and Environment Science, Wuhan University, Wuhan, Hubei 430079, China; Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089-0374, United States; Beijing Wanfang Technology Co., Ltd. Shanghai Branch, Shanghai, 201210, China

Recommended Citation:
Tan H.,Chen Y.,Wilson J.P.,et al. An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China[J]. Atmospheric Environment,2020-01-01,223
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Tan H.]'s Articles
[Chen Y.]'s Articles
[Wilson J.P.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Tan H.]'s Articles
[Chen Y.]'s Articles
[Wilson J.P.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Tan H.]‘s Articles
[Chen Y.]‘s Articles
[Wilson J.P.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.