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Inversion of Nighttime PM2.5 Mass Concentration in Beijing Based on the VIIRS Day-Night Band
- Source :
- Atmosphere; Volume 7; Issue 10; Pages: 136, Atmosphere, Vol 7, Iss 10, p 136 (2016)
- Publication Year :
- 2016
- Publisher :
- MDPI AG, 2016.
-
Abstract
- 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.
- Subjects :
- low-light
nighttime PM2.5
VIIRS/DNB
BP neural network
Atmospheric Science
Visible Infrared Imaging Radiometer Suite
010504 meteorology & atmospheric sciences
Meteorology
0211 other engineering and technologies
Inversion (meteorology)
02 engineering and technology
lcsh:QC851-999
Environmental Science (miscellaneous)
01 natural sciences
Back propagation neural network
Light intensity
Beijing
Environmental science
lcsh:Meteorology. Climatology
Linear correlation
Air quality index
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Subjects
Details
- ISSN :
- 20734433
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Atmosphere
- Accession number :
- edsair.doi.dedup.....9f541bd0ddf6e46dd9e2ee671ec1ff46