An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like Environment
Identifying unknown sources of air pollutants is vital for protecting public health, especially in cases involving the emission of toxic substances. The efficiency of this process depends highly on the accuracy of Source Term Estimation (STE) methods and the availability of robust measurements. Ther...
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MDPI AG
2024-12-01
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| author | Panagiotis Gkirmpas Fotios Barmpas George Tsegas George Efthimiou Paul Tremper Till Riedel Christos Vlachokostas Nicolas Moussiopoulos |
| author_facet | Panagiotis Gkirmpas Fotios Barmpas George Tsegas George Efthimiou Paul Tremper Till Riedel Christos Vlachokostas Nicolas Moussiopoulos |
| author_sort | Panagiotis Gkirmpas |
| collection | DOAJ |
| description | Identifying unknown sources of air pollutants is vital for protecting public health, especially in cases involving the emission of toxic substances. The efficiency of this process depends highly on the accuracy of Source Term Estimation (STE) methods and the availability of robust measurements. Therefore, it is important to examine how sensor network characteristics affect STE accuracy. This study investigates the impact of different sensor configurations on STE results for a stationary point source in a complex, urban-like environment. The STE methodology employs the Metropolis–Hastings Markov Chain Monte Carlo (MCMC) algorithm alongside numerical simulations of a Computational Fluid Dynamics (CFD) model. The STE algorithm is applied across several sensor configurations in three distinct release scenarios and real sensor observations from the Michelstadt wind tunnel experiment, assessing both the number of sensors used and the agreement between measured and modeled concentrations. In general, the results indicate that increasing the number of sensors and the model’s accuracy improves the source parameters estimations. However, there is a specific number of sensors in each release scenario where STE outcomes from randomly selected, high-accuracy, and low-accuracy sensors converge to similar solutions. Overall, the findings provide valuable information for designing sensor configurations in urban areas. |
| format | Article |
| id | doaj-art-acc3271e5498417bbd39202f99a5f007 |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-acc3271e5498417bbd39202f99a5f0072025-08-20T02:53:27ZengMDPI AGAtmosphere2073-44332024-12-011512151210.3390/atmos15121512An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like EnvironmentPanagiotis Gkirmpas0Fotios Barmpas1George Tsegas2George Efthimiou3Paul Tremper4Till Riedel5Christos Vlachokostas6Nicolas Moussiopoulos7Sustainability Engineering Laboratory, Aristotle University, GR-54124 Thessaloniki, GreeceSustainability Engineering Laboratory, Aristotle University, GR-54124 Thessaloniki, GreeceSustainability Engineering Laboratory, Aristotle University, GR-54124 Thessaloniki, GreeceChemical Process and Energy Resources Institute, Centre for Research & Technology Hellas, 57001 Thessaloniki, GreeceTECO/Pervasive Computing Systems, Karlsruhe Institute of Technology, 76131 Karlsruhe, GermanyTECO/Pervasive Computing Systems, Karlsruhe Institute of Technology, 76131 Karlsruhe, GermanySustainability Engineering Laboratory, Aristotle University, GR-54124 Thessaloniki, GreeceMain Campus, Aristotle University, GR-54124 Thessaloniki, GreeceIdentifying unknown sources of air pollutants is vital for protecting public health, especially in cases involving the emission of toxic substances. The efficiency of this process depends highly on the accuracy of Source Term Estimation (STE) methods and the availability of robust measurements. Therefore, it is important to examine how sensor network characteristics affect STE accuracy. This study investigates the impact of different sensor configurations on STE results for a stationary point source in a complex, urban-like environment. The STE methodology employs the Metropolis–Hastings Markov Chain Monte Carlo (MCMC) algorithm alongside numerical simulations of a Computational Fluid Dynamics (CFD) model. The STE algorithm is applied across several sensor configurations in three distinct release scenarios and real sensor observations from the Michelstadt wind tunnel experiment, assessing both the number of sensors used and the agreement between measured and modeled concentrations. In general, the results indicate that increasing the number of sensors and the model’s accuracy improves the source parameters estimations. However, there is a specific number of sensors in each release scenario where STE outcomes from randomly selected, high-accuracy, and low-accuracy sensors converge to similar solutions. Overall, the findings provide valuable information for designing sensor configurations in urban areas.https://www.mdpi.com/2073-4433/15/12/1512sensors configuration analysissource term estimationBayesian inferencecomputational fluid dynamicsurban geometry |
| spellingShingle | Panagiotis Gkirmpas Fotios Barmpas George Tsegas George Efthimiou Paul Tremper Till Riedel Christos Vlachokostas Nicolas Moussiopoulos An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like Environment Atmosphere sensors configuration analysis source term estimation Bayesian inference computational fluid dynamics urban geometry |
| title | An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like Environment |
| title_full | An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like Environment |
| title_fullStr | An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like Environment |
| title_full_unstemmed | An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like Environment |
| title_short | An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like Environment |
| title_sort | evaluation of the sensitivity of a source term estimation methodology of sensor configuration in an urban like environment |
| topic | sensors configuration analysis source term estimation Bayesian inference computational fluid dynamics urban geometry |
| url | https://www.mdpi.com/2073-4433/15/12/1512 |
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