Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

Journal article


Xue, Yong 2021. Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data. Remote Sensing. 13 (21), p. 4276. https://doi.org/10.3390/rs13214276
AuthorsXue, Yong
Abstract

The purpose of this study is to estimate the particulate matter (PM2.5 and PM10) in China using the improved geographically and temporally weighted regression (IGTWR) model and Fengyun (FY-4A) aerosol optical depth (AOD) data. Based on the IGTWR model, the boundary layer height (BLH), relative humidity (RH), AOD, time, space, and normalized difference vegetation index (NDVI) data are employed to estimate the PM2.5 and PM10. The main processes of this study are as follows: firstly, the feasibility of the AOD data from FY-4A in estimating PM2.5 and PM10 mass concentrations were analysed and confirmed by randomly selecting 5–6 and 9–10 June 2020 as an example. Secondly, hourly concentrations of PM2.5 and PM10 are estimated between 00:00 and 09:00 (UTC) each day. Specifically, the model estimates that the correlation coefficient R2 of PM2.5 is 0.909 and the root mean squared error (RMSE) is 5.802 μg/m3, while the estimated R2 of PM10 is 0.915, and the RMSE is 12.939 μg/m3. Our high temporal resolution results reveal the spatial and temporal characteristics of hourly PM2.5 and PM10 concentrations on the day. The results indicate that the use of data from the FY-4A satellite and an improved time–geographically weighted regression model for estimating PM2.5 and PM10 is feasible, and replacing land use classification data with NDVI facilitates model improvement

KeywordsBid Data
Year2021
JournalRemote Sensing
Journal citation13 (21), p. 4276
PublisherMDPI AG
ISSN2072-4292
Digital Object Identifier (DOI)https://doi.org/10.3390/rs13214276
Web address (URL)http://hdl.handle.net/10545/626327
http://creativecommons.org/licenses/by-nc-nd/4.0/
hdl:10545/626327
Publication dates24 Oct 2021
Publication process dates
Deposited04 Mar 2022, 15:38
Accepted18 Oct 2021
Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

ContributorsUniversity of Derby
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