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Author Zhao, X.; Shi, H.; Yu, H.; Yang, P.
Title Inversion of Nighttime PM2.5 Mass Concentration in Beijing Based on the VIIRS Day-Night Band Type Journal Article
Year 2016 Publication (up) Atmosphere Abbreviated Journal Atmosphere
Volume 7 Issue 10 Pages 136
Keywords Remote Sensing
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2073-4433 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number LoNNe @ kyba @ Serial 1546
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Author Paranunzio, R.; Ceola, S.; Laio, F.; Montanari, A.
Title Evaluating the Effects of Urbanization Evolution on Air Temperature Trends Using Nightlight Satellite Data Type Journal Article
Year 2019 Publication (up) Atmosphere Abbreviated Journal Atmosphere
Volume 10 Issue 3 Pages 117
Keywords Remote Sensing
Abstract Confounding factors like urbanization and land-use change could introduce uncertainty to the estimation of global temperature trends related to climate change. In this work, we introduce a new way to investigate the nexus between temporal trends of temperature and urbanization data at the global scale in the period from 1992 to 2013. We analyze air temperature data recorded from more than 5000 weather stations worldwide and nightlight satellite measurements as a proxy for urbanization. By means of a range of statistical methods, our results quantify and outline that the temporal evolution of urbanization affects temperature trends at multiple spatial scales with significant differences at regional and continental scales. A statistically significant agreement in temperature and nightlight trends is detected, especially in low and middle-income regions, where urbanization is rapidly growing. Conversely, in continents such as Europe and North America, increases in temperature trends are typically detected along with non-significant nightlight trends.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2073-4433 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2249
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Author Wang, J.; Aegerter, C.; Xu, X.; Szykman, J.J.
Title Potential application of VIIRS Day/Night Band for monitoring nighttime surface PM2.5 air quality from space Type Journal Article
Year 2015 Publication (up) Atmospheric Environment Abbreviated Journal Atmospheric Environment
Volume 124 A Issue Pages 55-63
Keywords Remote Sensing
Abstract A pilot study is conducted to illustrate the potential of using radiance data collected by the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite for particulate matter (PM) air quality monitoring at night. The study focuses on the moonless and cloudless nights in Atlanta, Georgia during August – October 2012. We show with radiative transfer calculations that DNB at night is sensitive to the change of aerosols and much less sensitive to the change of water vapor in the atmosphere illuminated by common outdoor light bulbs at the surface. We further show both qualitatively that the contrast of DNB images can indicate the change of air quality at the urban scale, and quantitatively that change of light intensity during the night (as characterized by VIIRS DNB) reflects the change of surface PM2.5. Compared to four meteorological variables (u and v components of surface wind speed, surface pressure, and columnar water vapor amount) that can be obtained from surface measurements, the DNB light intensity is the only variable that shows either the largest or second largest correlation with surface PM2.5 measured at 5 different sites. A simple multivariate regression model shows that consideration of the change of DNB light intensity can yield improved estimate of surface PM2.5 as compared to the regression model with consideration of meteorological variables only. Cross validation of this regression model shows that the estimated surface PM2.5 has nearly no bias and a linear correlation coefficient (R) of 0.67 with respect to the observed hourly surface PM2.5. Furthermore, ground-based observation supports that surface PM2.5 concentration at the VIIRS night overpass (∼1:00 am local) time is representative of daily PM2.5 air quality (R = 0.82 and mean bias of -0.1 μg m-3). While the potential appears promising, mapping surface PM2.5 from space with visible light at night still face various challenges and the strategies to address some of these challenges are elaborated for future studies.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1352-2310 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number LoNNe @ kyba @ Serial 1293
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Author Li, C.; Hsu, N.C.; Sayer, A.M.; Krotkov, N.A.; Fu, J.S.; Lamsal, L.N.; Lee, J.; Tsay, S.-C.
Title Satellite observation of pollutant emissions from gas flaring activities near the Arctic Type Journal Article
Year 2016 Publication (up) Atmospheric Environment Abbreviated Journal Atmospheric Environment
Volume 133 Issue Pages 1-11
Keywords Remote Sensing
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1352-2310 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number LoNNe @ kyba @ Serial 1373
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Author Fu, D.; Xia, X.; Duan, M.; Zhang, X.; Li, X.; Wang, J.; Liu, J.
Title Mapping nighttime PM 2.5 from VIIRS DNB using a linear mixed-effect model Type Journal Article
Year 2018 Publication (up) Atmospheric Environment Abbreviated Journal Atmospheric Environment
Volume 178 Issue Pages 214-222
Keywords Remote Sensing
Abstract Estimation of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) from daytime satellite aerosol products is widely reported in the literature; however, remote sensing of nighttime surface PM2.5 from space is very limited. PM2.5 shows a distinct diurnal cycle and PM2.5 concentration at 1:00 local standard time (LST) has a linear correlation coefficient (R) of 0.80 with daily-mean PM2.5. Therefore, estimation of nighttime PM2.5 is required toward an improved understanding of temporal variation of PM2.5 and its effects on air quality. Using data from the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) and hourly PM2.5 data at 35 stations in Beijing, a mixed-effect model is developed here to estimate nighttime PM2.5 from nighttime light radiance measurements based on the assumption that the DNB-PM2.5 relationship is constant spatially but varies temporally. Cross-validation showed that the model developed using all stations predict daily PM2.5 with mean determination coefficient (R2) of 0.87 ±± 0.12, 0.83 ±0.10±0.10, 0.87 ±± 0.09, 0.83 ±± 0.10 in spring, summer, autumn and winter. Further analysis showed that the best model performance was achieved in urban stations with average cross-validation R2 of 0.92. In rural stations, DNB light signal is weak and was likely smeared by lunar illuminance that resulted in relatively poor estimation of PM2.5. The fixed and random parameters of the mixed-effect model in urban stations differed from those in suburban stations, which indicated that the assumption of the mixed-effect model should be carefully evaluated when used at a regional scale.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1352-2310 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number LoNNe @ kyba @ Serial 1814
Permanent link to this record