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Remote Sens. 2017, 9(6), 620; doi:10.3390/rs9060620

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

1
Department of Geography, University of Connecticut, Storrs, CT 06269, USA
2
Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Academic Editors: Bailang Yu, Yuyu Zhou, Chunyang He, Xiaofeng Li and Prasad S. Thenkabail
Received: 3 May 2017 / Revised: 1 June 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Abstract

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. View Full-Text
Keywords: PM2.5; nighttime light (NTL); Vegetation Adjusted NTL Urban Index (VANUI); aerosol optical depth (AOD); geographically weighted regression (GWR) PM2.5; nighttime light (NTL); Vegetation Adjusted NTL Urban Index (VANUI); aerosol optical depth (AOD); geographically weighted regression (GWR)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, X.; Zhang, C.; Li, W.; Liu, K. Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States. Remote Sens. 2017, 9, 620.

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