2015
DOI: 10.1016/j.rse.2014.09.015
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Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5

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Cited by 323 publications
(205 citation statements)
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“…Three black boxes denote the regions on which our study focused. [9,10,18], semi-empirical models [12,24] and statistical models [13,25,26]. Compared with the utilization of PM 2.5 /AOD conversion factors and semi-empirical methods, statistical models, particularly advanced statistical models, are well recognized as the most popular approach, yielding higher accuracy in estimating satellite-derived PM 2.5 data because of the incorporation of different meteorological factors, land use data, population distribution data, etc.…”
Section: Ground-based Measurement Datamentioning
confidence: 99%
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“…Three black boxes denote the regions on which our study focused. [9,10,18], semi-empirical models [12,24] and statistical models [13,25,26]. Compared with the utilization of PM 2.5 /AOD conversion factors and semi-empirical methods, statistical models, particularly advanced statistical models, are well recognized as the most popular approach, yielding higher accuracy in estimating satellite-derived PM 2.5 data because of the incorporation of different meteorological factors, land use data, population distribution data, etc.…”
Section: Ground-based Measurement Datamentioning
confidence: 99%
“…In this analysis, four sub-regions were extracted to further validate our method: the North China Plain (NCP, [34][35][36][37][38][39][40] • N, 112-120 • E), the Sichuan Basin (SCB, 27-33 • N, 102-110 • E), and the Pearl River Delta (PRD, [22][23][24][25] • N, 110-117 • E) (indicated by the blue boxes in Figure 1, all of which exhibited distinct regional characteristics because of different natural environments and anthropogenic or other industrial emissions. Compared with the results shown in Figure 3a,b,e,f, linear regression showed that the chemical concentrations estimated in this study were more consistent with ground measurements, with r values of 0.72 and 0.68 for SO 4 2− and NH 4 + , respectively.…”
Section: Evaluation Of Satellite-estimated Chemical Componentsmentioning
confidence: 99%
“…Terra passes over the equator at about 10:30 local time (LT) and Aqua overfl ies the equator at about 13:30 LT in the opposite direction, providing almost daily global AOD coverage [9]. The dimensionless AOD is retrieved based on sunlight attenuation by the aerosols within the vertical atmospheric column from the ground to the atmosphere's top.…”
Section: Air Pollution Datamentioning
confidence: 99%
“…The main reason for this, apart from random and systematic errors associated with satellite AOD retrievals, is that the AOD values are instantaneous (i.e., obtained at about 10:30 LT); satellite data have limited resolution, while the concentrations of air pollutants are average daily data. Second, the AOD is easily affected by meteorological and synoptic conditions [9,33]. Because these factors vary spatiotemporally, it is likely that the AOD-SO 2 /NO 2 / PM 10 relationships also vary spatiotemporally for different regions and seasons.…”
Section: Relationship Of Aod and Air Pollution Concentrationsmentioning
confidence: 99%
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