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Open AccessArticle

Inversion of Nighttime PM2.5 Mass Concentration in Beijing Based on the VIIRS Day-Night Band

Institute of Meteorology and Oceanograph, PLA University of Science and Technology, Nanjing 211101, China
Author to whom correspondence should be addressed.
Academic Editors: Giovanni Pitari and Gabriele Curci
Atmosphere 2016, 7(10), 136;
Received: 24 September 2016 / Revised: 13 October 2016 / Accepted: 14 October 2016 / Published: 19 October 2016
(This article belongs to the Special Issue Atmospheric Aerosols and Their Radiative Effects)
In order to monitor nighttime particular matter (PM) air quality in urban area, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The study focuses on the moonless and cloudless nights in Beijing during March–May 2015. A test is carried out by selecting surface PM2.5 data from 12 PM2.5 automatic monitoring stations and the corresponding night city light intensity from DNB. As indicated by the results, the linear correlation coefficient (R) between the results and the corresponding measured surface PM2.5 concentration is 0.91, and the root-mean-square error (RMSE) is 14.02 μg/m3 with the average of 59.39 μg/m3. Furthermore, the BP neural network model shows better accuracy when air relative humility ranges from 40% to 80% and surface PM2.5 concentration exceeds 40 μg/m3. The study provides a superiority approach for monitoring PM2.5 air quality from space with visible light remote sensing data at night. View Full-Text
Keywords: low-light; nighttime PM2.5; VIIRS/DNB; BP neural network low-light; nighttime PM2.5; VIIRS/DNB; BP neural network
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Zhao, X.; Shi, H.; Yu, H.; Yang, P. Inversion of Nighttime PM2.5 Mass Concentration in Beijing Based on the VIIRS Day-Night Band. Atmosphere 2016, 7, 136.

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