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An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM<sub>2.5</sub>-Bound Carbonaceous Compositions and Water-Soluble Ionic Species
oleh: Siwatt Pongpiachan, Qiyuan Wang, Ronbanchob Apiratikul, Danai Tipmanee, Yu Li, Li Xing, Guohui Li, Yongming Han, Junji Cao, Ronald C. Macatangay, Saran Poshyachinda, Aekkapol Aekakkararungroj, Muhammad Zaffar Hashmi
| Format: | Article |
|---|---|
| Diterbitkan: | MDPI AG 2022-06-01 |
Deskripsi
Previous studies have determined biomass burning as a major source of air pollutants in the ambient air in Thailand. To analyse the impacts of meteorological parameters on the variation of carbonaceous aerosols and water-soluble ionic species (WSIS), numerous statistical models, including a source apportionment analysis with the assistance of principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs), were employed in this study. A total of 191 sets of PM<sub>2.5</sub> samples were collected from the three monitoring stations in Chiang-Mai, Bangkok, and Phuket from July 2020 to June 2021. Hotspot numbers and other meteorological parameters were obtained using NOAA-20 weather satellites coupled with the Global Land Data Assimilation System. Although PCA revealed that crop residue burning and wildfires are the two main sources of PM<sub>2.5</sub>, ANNs highlighted the importance of wet deposition as the main depletion mechanism of particulate WSIS and carbonaceous aerosols. Additionally, Mg<sup>2+</sup> and Ca<sup>2+</sup> were deeply connected with albedo, plausibly owing to their strong hygroscopicity as the CCNs responsible for cloud formation.