Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

doi:10.3390/rs9060620

	Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States
	Li, Xueke 1; Zhang, Chuanrong 1; Li, Weidong 1; Liu, Kai 2
刊名	REMOTE SENSING
	2017-06-01
卷号	9 期号:6 页码:16
关键词	PM2.5 nighttime light (NTL) Vegetation Adjusted NTL Urban Index (VANUI) aerosol optical depth (AOD) geographically weighted regression (GWR)
ISSN号	2072-4292
DOI	10.3390/rs9060620
通讯作者	Zhang, Chuanrong(chuanrong.zhang@uconn.edu)
英文摘要	Degraded air quality by PM2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM2.5 monitoring and prediction.
WOS关键词	GEOGRAPHICALLY WEIGHTED REGRESSION ; AEROSOL OPTICAL DEPTH ; GROUND-LEVEL PM2.5 ; FINE PARTICULATE MATTER ; LONG-TERM EXPOSURE ; AIR-POLLUTION ; LUNG-FUNCTION ; MODIS ; QUALITY ; CITIES
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000404623900111
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/62923]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Chuanrong
作者单位	1.Univ Connecticut, Dept Geog, Storrs, CT 06269 USA 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Li, Xueke,Zhang, Chuanrong,Li, Weidong,et al. Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States[J]. REMOTE SENSING,2017,9(6):16.
APA	Li, Xueke,Zhang, Chuanrong,Li, Weidong,&Liu, Kai.(2017).Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States.REMOTE SENSING,9(6),16.
MLA	Li, Xueke,et al."Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States".REMOTE SENSING 9.6(2017):16.