Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin
Thorough examination of various aspects related to the distribution of air pollutants in a specific region and the factors contributing to high concentrations is essential, as these elevated levels can be detrimental. To accomplish this, the development and improvement of a digital twin that encompa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/14/7/1078 |
| _version_ | 1827734007321722880 |
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| author | Venelin Todorov Ivan Dimov |
| author_facet | Venelin Todorov Ivan Dimov |
| author_sort | Venelin Todorov |
| collection | DOAJ |
| description | Thorough examination of various aspects related to the distribution of air pollutants in a specific region and the factors contributing to high concentrations is essential, as these elevated levels can be detrimental. To accomplish this, the development and improvement of a digital twin that encompasses all relevant physical processes in the atmosphere is necessary. This tool, known as DIGITAL AIR, has been created, and it is now necessary to extend it with precise sensitivity analysis. DIGITAL AIR is gaining popularity due to its effectiveness in addressing complex problems that arise in intricate environments; this motivates our further investigations. In this paper, we focus on the preparation and further investigation of DIGITAL AIR through sensitivity analysis with improved stochastic approaches for investigating high-level air pollutants. We discuss and test the utilization of this digital tool in tackling the issue. The unified Danish Eulerian model (UNI-DEM) plays a crucial role within DIGITAL AIR. This mathematical model, UNI-DEM, is highly versatile and can be applied to various studies concerning the adverse effects caused by elevated air pollution levels. |
| first_indexed | 2024-03-11T01:19:27Z |
| format | Article |
| id | doaj.art-508576b1628240d1814b5b08b91200de |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T01:19:27Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-508576b1628240d1814b5b08b91200de2023-11-18T18:15:10ZengMDPI AGAtmosphere2073-44332023-06-01147107810.3390/atmos14071078Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital TwinVenelin Todorov0Ivan Dimov1Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G̃. Bonchev Str. Bl. 25A, 1113 Sofia, BulgariaInstitute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G̃. Bonchev Str. Bl. 25A, 1113 Sofia, BulgariaThorough examination of various aspects related to the distribution of air pollutants in a specific region and the factors contributing to high concentrations is essential, as these elevated levels can be detrimental. To accomplish this, the development and improvement of a digital twin that encompasses all relevant physical processes in the atmosphere is necessary. This tool, known as DIGITAL AIR, has been created, and it is now necessary to extend it with precise sensitivity analysis. DIGITAL AIR is gaining popularity due to its effectiveness in addressing complex problems that arise in intricate environments; this motivates our further investigations. In this paper, we focus on the preparation and further investigation of DIGITAL AIR through sensitivity analysis with improved stochastic approaches for investigating high-level air pollutants. We discuss and test the utilization of this digital tool in tackling the issue. The unified Danish Eulerian model (UNI-DEM) plays a crucial role within DIGITAL AIR. This mathematical model, UNI-DEM, is highly versatile and can be applied to various studies concerning the adverse effects caused by elevated air pollution levels.https://www.mdpi.com/2073-4433/14/7/1078air pollution modelingsensitivity analysismultidimensional integralsMonte Carlo methodsdigital sequences |
| spellingShingle | Venelin Todorov Ivan Dimov Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin Atmosphere air pollution modeling sensitivity analysis multidimensional integrals Monte Carlo methods digital sequences |
| title | Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin |
| title_full | Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin |
| title_fullStr | Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin |
| title_full_unstemmed | Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin |
| title_short | Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin |
| title_sort | unveiling the power of stochastic methods advancements in air pollution sensitivity analysis of the digital twin |
| topic | air pollution modeling sensitivity analysis multidimensional integrals Monte Carlo methods digital sequences |
| url | https://www.mdpi.com/2073-4433/14/7/1078 |
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