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
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 a...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/7/1042 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Siwatt Pongpiachan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:23:26Z |
| format | Article |
| id | doaj.art-b1691cebc7384b1daab712b393ca8793 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T10:23:26Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-b1691cebc7384b1daab712b393ca87932023-12-01T21:52:54ZengMDPI AGAtmosphere2073-44332022-06-01137104210.3390/atmos13071042An 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 SpeciesSiwatt Pongpiachan0Qiyuan Wang1Ronbanchob Apiratikul2Danai Tipmanee3Yu Li4Li Xing5Guohui Li6Yongming Han7Junji Cao8Ronald C. Macatangay9Saran Poshyachinda10Aekkapol Aekakkararungroj11Muhammad Zaffar Hashmi12NIDA Center for Research & Development of Disaster Prevention & Management, School of Social and Environmental Development, National Institute of Development Administration (NIDA), 148 Moo 3, Sereethai Road, Klong-Chan, Bangkok 10240, ThailandState Key Laboratory of Loess and Quaternary Geology, Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, ChinaDepartment of Environmental Science, Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok 10300, ThailandFaculty of Technology and Environment, Prince of Songkla University, Phuket Campus 80 Moo 1, Vichitsongkram Road, Kathu, Phuket 83120, ThailandXi’an Institute for Innovative Earth Environment Research, Xi’an 710061, ChinaSchool of Geography and Tourism, Shaanxi Normal University, Xi’an 710062, ChinaState Key Laboratory of Loess and Quaternary Geology, Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, ChinaState Key Laboratory of Loess and Quaternary Geology, Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, ChinaState Key Laboratory of Loess and Quaternary Geology, Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, ChinaNational Astronomical Research Institute of Thailand (Public Organization), 260 Moo 4, Chiang-Mai 50180, ThailandNational Astronomical Research Institute of Thailand (Public Organization), 260 Moo 4, Chiang-Mai 50180, ThailandAsian Disaster Preparedness Center (ADPC), SM Tower 979/66 70 Phahonyothin Road, Phaya Thai, Bangkok 10400, ThailandDepartment of Chemistry, COMSATS University, Park Road, Chak Shahzad, Islamabad 44000, PakistanPrevious 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.https://www.mdpi.com/2073-4433/13/7/1042principal component analysisartificial neural networksPM<sub>2.5</sub>hotspotscarbonaceous compositionswater-soluble ionic species |
| spellingShingle | 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 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 Atmosphere principal component analysis artificial neural networks PM<sub>2.5</sub> hotspots carbonaceous compositions water-soluble ionic species |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | principal component analysis artificial neural networks PM<sub>2.5</sub> hotspots carbonaceous compositions water-soluble ionic species |
| url | https://www.mdpi.com/2073-4433/13/7/1042 |
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