干旱区生态环境知识资源中心(Arid): Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China

Arid

DOI	10.3390/rs11222679
	Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China
	Zhang, Kainan 1,2; de Leeuw, Gerrit 2; Yang, Zhiqiang 1; Chen, Xingfeng 2,3; Su, Xiaoli 4; Jiao, Jiashuang 1
通讯作者	Yang, Zhiqiang
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:22
英文摘要	Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 mu m) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km x 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R-2), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, -0.378 mu g/m(3), and 12.746 mu g/m(3), respectively, and the model cross-validation results were 0.703, 1.451 mu g/m(3), and 15.731 mu g/m(3), respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM2.5 concentrations in the Guanzhong Basin, and PM2.5 concentrations vary with the seasons. This method can provide reliable PM2.5 predictions to reduce the bias of exposure assessment in air pollution and health research.
英文关键词	VIIRS AOD PM2.5 Guanzhong Basin Geographically weighted regression Generalized additive model
类型	Article
语种	英语
国家	Peoples R China ; Finland
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000502284300084
WOS关键词	GROUND-LEVEL PM2.5 ; AEROSOL OPTICAL DEPTH ; FINE PARTICULATE MATTER ; LONG-TERM EXPOSURE ; HAZE EPISODE ; TIME-SERIES ; DESERT DUST ; MODIS ; XIAN ; MORTALITY
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
EI主题词	2019-11-02
来源机构	中国科学院地球环境研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310731
作者单位	1.Changan Univ, Sch Geol Engn & Geomat, Xian 710054, Shaanxi, Peoples R China; 2.Finnish Meteorol Inst, Climate Res Dept, Helsinki 00560, Finland; 3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China; 4.Chinese Acad Sci, Inst Earth Environm, Xian 710075, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Kainan,de Leeuw, Gerrit,Yang, Zhiqiang,et al. Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China[J]. 中国科学院地球环境研究所,2019,11(22).
APA	Zhang, Kainan,de Leeuw, Gerrit,Yang, Zhiqiang,Chen, Xingfeng,Su, Xiaoli,&Jiao, Jiashuang.(2019).Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China.REMOTE SENSING,11(22).
MLA	Zhang, Kainan,et al."Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China".REMOTE SENSING 11.22(2019).