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Full-Coverage High-Resolution Daily PM(sub 2.5) Estimation using MAIAC AOD in the Yangtze River Delta of ChinaSatellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM (sub 2.5)). The emerging high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction(MAIAC), provides a valuable opportunity to characterize local-scale PM(sub 2.5) at 1-km resolution. However, non-random missing AOD due to cloud snow cover or high surface reflectance makes this task challenging. Previous studies filled the data gap by spatially interpolating neighboring PM(sub 2.5) measurements or predictions. This strategy ignored the effect of cloud cover on aerosol loadings and has been shown to exhibit poor performance when monitoring stations are sparse or when there is seasonal large-scale missngness. Using the Yangtze River Delta of China as an example, we present a Multiple Imputation (MI) method that combines the MAIAC high-resolution satellite retrievals with chemical transport model (CTM) simulations to fill missing AOD. A two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM(sub 2.5) concentrations in 2013 and 2014 at 1 km resolution with complete coverage in space and time. The daily MI models have an average R(exp 2) of 0.77, with an inter-quartile range of 0.71 to 0.82 across days. The overall Ml model 10-fold cross-validation R(exp 2) (root mean square error) were 0.81 (25 gm(exp 3)) and 0.73 (18 gm(exp 3)) for year 2013 and 2014, respectively. Predictions with only observational AOD or only imputed AOD showed similar accuracy.Comparing with previous gap-filling methods, our MI method presented in this study performed bette rwith higher coverage, higher accuracy, and the ability to fill missing PM(sub 2.5) predictions without ground PM(sub 2.5) measurements. This method can provide reliable PM(sub 2.5)predictions with complete coverage that can reduce biasin exposure assessment in air pollution and health studies.
Document ID
20170009396
Document Type
Reprint (Version printed in journal)
Authors
Xiao, Qingyang (Emory Univ. Atlanta, GA, United States)
Wang, Yujie (Maryland Univ. Baltimore County Baltimore, MD, United States)
Chang, Howard H. (Emory Univ. Atlanta, GA, United States)
Meng, Xia (Emory Univ. Atlanta, GA, United States)
Geng, Guannan (Emory Univ. Atlanta, GA, United States)
Lyapustin, Alexei Ivanovich (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Liu, Yang (Emory Univ. Atlanta, GA, United States)
Date Acquired
October 3, 2017
Publication Date
August 9, 2017
Publication Information
Publication: Remote Sensing of Environment
Volume: 199
ISSN: 0034-4257
Subject Category
Geosciences (General)
Report/Patent Number
GSFC-E-DAA-TN45199
Funding Number(s)
CONTRACT_GRANT: JPL-1363692
CONTRACT_GRANT: NNX15AT34A
CONTRACT_GRANT: NNX14AG01G
CONTRACT_GRANT: EPA 83586901
Distribution Limits
Public
Copyright
Other
Keywords
PM2.5
MAIAC
cloud fraction