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Proceedings Paper

Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China: a model approach
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Paper Abstract

Fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) has been widely considered to be one of the main pollutant threating human health. Ground-level PM2.5 monitoring can provide accurate point data, but its value is hard to scale up to large scale. In this respects, satellite data with large coverage areas and long term range, could enhance our ability to estimate PM2.5 concentration. In this study, a Multilinear correlation model (MLC) based on MODIS AOD level 2 data was developed to estimate PM2.5 concentration in Northeastern China from 2013-2016, then ground-level PM2.5 monitoring data from 15 stations covering study area were used for validation. Results showed that 1) the annual PM2.5 is 63.98μg/m2, AOD values agreed well with estimated PM2.5 concentration, 2) the spatial variations of PM2.5 were not clear, while the temporal dynamic of PM2.5 were observed, the highest values were observed in winter, opposite to what were observed in fall. 3) the MLC model coupled with meteorological data could improve the precision of PM2.5 estimations. Therefore, we suggest that the developed MLC model is useful for the PM2.5 estimations in northeastern China.

Paper Details

Date Published: 1 September 2017
PDF: 10 pages
Proc. SPIE 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV, 104050U (1 September 2017); doi: 10.1117/12.2272937
Show Author Affiliations
Xiaoli Wei, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Joint Research Institute for New Energy and the Environment (China)
Runhe Shi, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Joint Research Institute for New Energy and the Environment (China)
Wei Gao, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation, China, Joint Research Institute f (China)
USDA UV-B Monitoring and Research Program and Colorado State Univ. (United States)
Deying Zhang, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Joint Research Institute for New Energy and the Environment (China)


Published in SPIE Proceedings Vol. 10405:
Remote Sensing and Modeling of Ecosystems for Sustainability XIV
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

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