气候变化领域集成服务门户(CCPortal): An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China

CCPortal

DOI	10.1016/j.atmosenv.2019.117205
	An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China
	Tan H.; Chen Y.; Wilson J.P.; Zhang J.; Cao J.; Chu T.
发表日期	2020
ISSN	1352-2310
卷号	223
英文摘要	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
英文关键词	Eigenvector spatial filtering; GWR; PM2.5; Spatially varying coefficient; Yangtze river delta region
语种	英语
scopus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
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/129600
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
推荐引用方式 GB/T 7714	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],2020,223.
APA	Tan H.,Chen Y.,Wilson J.P.,Zhang J.,Cao J.,&Chu T..(2020).An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China.Atmospheric Environment,223.
MLA	Tan H.,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".Atmospheric Environment 223(2020).