Kamarul Zaman, Nurul Amalin Fatihah and Kanniah, Kasturi Devi and Kaskaoutis, Dimitris G. and Mohamad Fadzil, Nurul Asyiqin and Latif, Mohd. Talib (2023) Fine particulate matters mapping in the maritime region of Malaysia using aerosols and pollutant gases derived from satellite remote sensing. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, 16 July 2023 - 21 July 2023, Pasadena, California, USA.
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Official URL: http://dx.doi.org/10.1109/IGARSS52108.2023.1028157...
Abstract
Fine particulate matters (PM2.5) have been identified as a major air pollutant that can affect population health. Nevertheless, PM2.5 data covering the entire region is not sufficient due to financial constraint to install ground monitoring stations. This study developed empirical models to estimate PM2.5 based on data derived from satellite and using a machine learning technique. The accuracy of the developed model is high with R2 = 0.67, RMSE = 13.36 μg m-3, and NSE = 0.645. Although the seasonal PM2.5 underestimated about 4% when compared to the ground based PM2.5, but missing AOD data hinder a seamless seasonal PM2.5 mapping. Usage of a small number of samples affect the model training and also reduced the PM2.5 estimation accuracy.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Himawari-8; machine learning; PM2.5; Sentinel 5p. |
| Subjects: | T Technology > TH Building construction > TH434-437 Quantity surveying |
| Divisions: | Built Environment |
| ID Code: | 108064 |
| Deposited By: | Muhamad Idham Sulong |
| Deposited On: | 17 Oct 2024 06:07 |
| Last Modified: | 17 Oct 2024 06:07 |
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