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: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/7c7d858905364e5e804309b27d44663f |
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| Sumario: | 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. |
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