Estimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

Estimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

The purpose of this study is to estimate the particulate matter (PM<sub>2.5</sub> and PM<sub>10</sub>) 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, t...

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Autores principales: Yuxin Sun, Yong Xue, Xingxing Jiang, Chunlin Jin, Shuhui Wu, Xiran Zhou
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
PM<sub>2.5</sub>
PM<sub>10</sub>
AOD
FY-4A
IGTWR
Science
Q
Acceso en línea:https://doaj.org/article/7c7d858905364e5e804309b27d44663f
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spelling oai:doaj.org-article:7c7d858905364e5e804309b27d44663f2021-11-11T18:52:38ZEstimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data10.3390/rs132142762072-4292https://doaj.org/article/7c7d858905364e5e804309b27d44663f2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4276https://doaj.org/toc/2072-4292The purpose of this study is to estimate the particulate matter (PM<sub>2.5</sub> and PM<sub>10</sub>) 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 PM<sub>2.5</sub> and PM<sub>10</sub>. The main processes of this study are as follows: firstly, the feasibility of the AOD data from FY-4A in estimating PM<sub>2.5</sub> and PM<sub>10</sub> mass concentrations were analysed and confirmed by randomly selecting 5–6 and 9–10 June 2020 as an example. Secondly, hourly concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> are estimated between 00:00 and 09:00 (UTC) each day. Specifically, the model estimates that the correlation coefficient R<sup>2</sup> of PM<sub>2.5</sub> is 0.909 and the root mean squared error (RMSE) is 5.802 μg/m<sup>3</sup>, while the estimated R<sup>2</sup> of PM<sub>10</sub> is 0.915, and the RMSE is 12.939 μg/m<sup>3</sup>. Our high temporal resolution results reveal the spatial and temporal characteristics of hourly PM<sub>2.5</sub> and PM<sub>10</sub> 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 PM<sub>2.5</sub> and PM<sub>10</sub> is feasible, and replacing land use classification data with NDVI facilitates model improvement.Yuxin SunYong XueXingxing JiangChunlin JinShuhui WuXiran ZhouMDPI AGarticlePM<sub>2.5</sub>PM<sub>10</sub>AODFY-4AIGTWRScienceQENRemote Sensing, Vol 13, Iss 4276, p 4276 (2021)
institution DOAJ
collection DOAJ
language EN
topic PM<sub>2.5</sub>
PM<sub>10</sub>
AOD
FY-4A
IGTWR
Science
Q
spellingShingle PM<sub>2.5</sub>
PM<sub>10</sub>
AOD
FY-4A
IGTWR
Science
Q
Yuxin Sun
Yong Xue
Xingxing Jiang
Chunlin Jin
Shuhui Wu
Xiran Zhou
Estimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data
description The purpose of this study is to estimate the particulate matter (PM<sub>2.5</sub> and PM<sub>10</sub>) 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 PM<sub>2.5</sub> and PM<sub>10</sub>. The main processes of this study are as follows: firstly, the feasibility of the AOD data from FY-4A in estimating PM<sub>2.5</sub> and PM<sub>10</sub> mass concentrations were analysed and confirmed by randomly selecting 5–6 and 9–10 June 2020 as an example. Secondly, hourly concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> are estimated between 00:00 and 09:00 (UTC) each day. Specifically, the model estimates that the correlation coefficient R<sup>2</sup> of PM<sub>2.5</sub> is 0.909 and the root mean squared error (RMSE) is 5.802 μg/m<sup>3</sup>, while the estimated R<sup>2</sup> of PM<sub>10</sub> is 0.915, and the RMSE is 12.939 μg/m<sup>3</sup>. Our high temporal resolution results reveal the spatial and temporal characteristics of hourly PM<sub>2.5</sub> and PM<sub>10</sub> 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 PM<sub>2.5</sub> and PM<sub>10</sub> is feasible, and replacing land use classification data with NDVI facilitates model improvement.
format article
author Yuxin Sun
Yong Xue
Xingxing Jiang
Chunlin Jin
Shuhui Wu
Xiran Zhou
author_facet Yuxin Sun
Yong Xue
Xingxing Jiang
Chunlin Jin
Shuhui Wu
Xiran Zhou
author_sort Yuxin Sun
title Estimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data
title_short Estimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data
title_full Estimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data
title_fullStr Estimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data
title_full_unstemmed Estimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data
title_sort estimation of the pm<sub>2.5</sub> and pm<sub>10</sub> mass concentration over land from fy-4a aerosol optical depth data
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/7c7d858905364e5e804309b27d44663f
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AT yongxue estimationofthepmsub25subandpmsub10submassconcentrationoverlandfromfy4aaerosolopticaldepthdata
AT xingxingjiang estimationofthepmsub25subandpmsub10submassconcentrationoverlandfromfy4aaerosolopticaldepthdata
AT chunlinjin estimationofthepmsub25subandpmsub10submassconcentrationoverlandfromfy4aaerosolopticaldepthdata
AT shuhuiwu estimationofthepmsub25subandpmsub10submassconcentrationoverlandfromfy4aaerosolopticaldepthdata
AT xiranzhou estimationofthepmsub25subandpmsub10submassconcentrationoverlandfromfy4aaerosolopticaldepthdata
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