Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model

doi:10.1016/j.rse.2017.06.001

	Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model
	Guo, Yuanxi 1; Tang, Qiuhong 1,2; Gong, Dao-Yi 3; Zhang, Ziyin 4
刊名	REMOTE SENSING OF ENVIRONMENT
	2017-09-01
卷号	198 页码:140-149
关键词	PM2.5 Aerosol optical depth MODIS Geographically and temporally weighted regression Beijing
ISSN号	0034-4257
DOI	10.1016/j.rse.2017.06.001
通讯作者	Tang, Qiuhong(tangqh@igsnrr.ac.cn)
英文摘要	Most time-sequenced ambient air pollution data in China is published through daily Air Quality Index (AQI). However, few studies have used the AQI data to calibrate satellite-based estimates of fine particulate matter (PM2.5, particles no greater than 2.5 mu m in aerodynamic diameter) concentrations, partly because the AQI-derived PM2.5 is not continuously obtained each day. Taking Beijing as an example, we developed a geographically and temporally weighted regression (GTWR) model that can account for spatial and temporal variability in the relationship between the non-continuous AQI-derived PM2.5 and satellite-derived aerosol optical depth (AOD). The GTWR model, which uses AOD values with a 3-km spatial resolution obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological fields, and land-use variables as predictors, was fitted seasonally from April 2013 to March 2015. After being cross-validated against ground observations, the coefficient of determination (R-2) of PM2.5 ranged from 0.36 to 0.75, with a mean value of 0.58. The GTWR model outperforms several conventional models, such as the multiple linear regression (MLR) model, geographically weighted regression (GWR) model, temporally weighted regression (TWR) model, and linear mixed-effects (LME) model. Compared to a previous spatiotemporal model, the two-stage (LME + GWR) model, the GTWR model may be more feasible. When the number of daily records is >= 5, there is no obvious difference in prediction accuracy (cross-validated R-2 both valued at 0.68). However, when the number of daily records is <5, the GTWR model performs much better (cross-validated R-2 of 0.45 and 0.08). Our estimates indicate that the gridded annual mean PM2.5 values range from 62 to 110 mu g/m(3), denoting strong spatial variation. We find that when available, continuous daily PM2.5 observations can significantly improve model performance and therefore facilitate the estimation of surface PM2.5 concentrations at urban scales. The GTWR model may serve as a reference for studying regions where continuous air pollution data are limited. (C) 2017 Elsevier Inc. All rights reserved.
资助项目	National Natural Science Foundation of China[41425002] ; National Natural Science Foundation of China[41621061] ; China Postdoctoral Science Foundation[2015M570137] ; National Youth Top-notch Talent Support Program in China ; Beijing Municipal Natural Science Foundation[8152019]
WOS关键词	AEROSOL OPTICAL DEPTH ; FINE PARTICULATE MATTER ; LONG-TERM EXPOSURE ; AIR-QUALITY ; MODIS AOD ; CHINA ; SURFACE ; STATES ; LAND ; POLLUTION
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000406818500012
资助机构	National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; National Youth Top-notch Talent Support Program in China ; Beijing Municipal Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/61468]
专题	中国科学院地理科学与资源研究所
通讯作者	Tang, Qiuhong
作者单位	1.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China 4.China Meteorol Adm, Environm Meteorol Forecast Ctr Beijing Tianjin He, Beijing 100089, Peoples R China
推荐引用方式 GB/T 7714	Guo, Yuanxi,Tang, Qiuhong,Gong, Dao-Yi,et al. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model[J]. REMOTE SENSING OF ENVIRONMENT,2017,198:140-149.
APA	Guo, Yuanxi,Tang, Qiuhong,Gong, Dao-Yi,&Zhang, Ziyin.(2017).Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model.REMOTE SENSING OF ENVIRONMENT,198,140-149.
MLA	Guo, Yuanxi,et al."Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model".REMOTE SENSING OF ENVIRONMENT 198(2017):140-149.