IGSNRR OpenIR
Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States
Li, Xueke1; Zhang, Chuanrong1; Li, Weidong1; Liu, Kai2
2017-06-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume9Issue:6Pages:16
Corresponding AuthorZhang, Chuanrong(chuanrong.zhang@uconn.edu)
AbstractDegraded 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.
KeywordPM2.5 nighttime light (NTL) Vegetation Adjusted NTL Urban Index (VANUI) aerosol optical depth (AOD) geographically weighted regression (GWR)
DOI10.3390/rs9060620
WOS KeywordGEOGRAPHICALLY WEIGHTED REGRESSION ; AEROSOL OPTICAL DEPTH ; GROUND-LEVEL PM2.5 ; FINE PARTICULATE MATTER ; LONG-TERM EXPOSURE ; AIR-POLLUTION ; LUNG-FUNCTION ; MODIS ; QUALITY ; CITIES
Indexed BySCI
Language英语
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000404623900111
PublisherMDPI AG
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Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62924
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhang, Chuanrong
Affiliation1.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
Recommended Citation
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.
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