DOI: 10.1016/j.atmosenv.2018.02.001
Scopus记录号: 2-s2.0-85041509732
论文题名: Mapping nighttime PM2.5 from VIIRS DNB using a linear mixed-effect model
作者: Fu D ; , Xia X ; , Duan M ; , Zhang X ; , Li X ; , Wang J ; , Liu J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 178 起始页码: 214
结束页码: 222
语种: 英语
英文关键词: Mixed effect model
; Nighttime PM2.5
; VIIRS/DNB
Scopus关键词: Air quality
; Infrared imaging
; Thermography (imaging)
; Aerodynamic diameters
; Determination coefficients
; Linear correlation coefficient
; Mixed-effect models
; Nighttime PM2.5
; Radiance measurement
; VIIRS/DNB
; Visible infrared imaging radiometer suites
; Remote sensing
; aerodynamics
; aerosol
; air quality
; concentration (composition)
; diurnal variation
; estimation method
; mapping method
; model validation
; particle size
; particulate matter
; performance assessment
; remote sensing
; temporal variation
; VIIRS
; air quality
; Article
; autumn
; circadian rhythm
; concentration (parameters)
; particulate matter
; priority journal
; remote sensing
; seasonal variation
; spring
; summer
; winter
; Beijing [China]
; China
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: 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, 0.87 ± 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. © 2018 Elsevier Ltd
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82986
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; College of Earth Sciences, University of Chinese Academy of Sciences, Beijing, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China; School of Atmospheric Sciences, Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu, China; National Satellite Meteorological Center, China Meteorological Administration, Beijing, China; Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Studies, and Informatics Initiative, The University of Iowa, Iowa City, IA, United States; Beijing Meteorological Bureau, Beijing, China
Recommended Citation:
Fu D,, Xia X,, Duan M,et al. Mapping nighttime PM2.5 from VIIRS DNB using a linear mixed-effect model[J]. Atmospheric Environment,2018-01-01,178