Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets
Dispersion modeling is a useful tool for reproducing the spatial–temporal distribution of pollutants emitted by industrial sites, particularly in the environmental odor field. One widely used tool, accepted by regulatory agencies for environmental impact assessments, is the CALPUFF model, which requ...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2457 |
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| author | Francesca Tagliaferri Laura Facagni Marzio Invernizzi Adrian Luis Ferrer Hernández Anel Hernández-Garces Selena Sironi |
| author_facet | Francesca Tagliaferri Laura Facagni Marzio Invernizzi Adrian Luis Ferrer Hernández Anel Hernández-Garces Selena Sironi |
| author_sort | Francesca Tagliaferri |
| collection | DOAJ |
| description | Dispersion modeling is a useful tool for reproducing the spatial–temporal distribution of pollutants emitted by industrial sites, particularly in the environmental odor field. One widely used tool, accepted by regulatory agencies for environmental impact assessments, is the CALPUFF model, which requires a large number of input variables, including meteorological and orographical variables. The reliability of model results depends on the accuracy of these input variables. The present research aims to discuss a comparative study of odor dispersion modeling by initializing the CALMET meteorological processor with different input data: surface and upper air observational meteorological data, 3D prognostic data, and a blend of prognostic and measured data. Two distinct sources (a point and an area source) and two different simulation domains in Cuba and Italy are considered. The analysis of results is based on odor impact criteria enforced in some Italian regions by computing the 98th percentile of odor peak concentrations on an annual basis. For the area source, simulation results reveal that the ‘OBS’ and ‘HYBRID’ modes are largely comparable, whereas prognostic data tend to underestimate the odor concentrations, likely due to a reduced percentage of wind calms. For point sources, different input meteorological settings provide comparable results, with no significant differences. |
| first_indexed | 2024-04-24T18:35:47Z |
| format | Article |
| id | doaj.art-898c02a2422c4b399b51f1309791f59c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:35:47Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-898c02a2422c4b399b51f1309791f59c2024-03-27T13:19:45ZengMDPI AGApplied Sciences2076-34172024-03-01146245710.3390/app14062457Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological DatasetsFrancesca Tagliaferri0Laura Facagni1Marzio Invernizzi2Adrian Luis Ferrer Hernández3Anel Hernández-Garces4Selena Sironi5Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyDepartment of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyDepartment of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyCenter for Atmospheric Physics, Meteorology Institute (INSMET), Loma de Casablanca, Regla, Havana 11700, CubaFaculty of Chemical Engineering, Universidad Tecnológica de La Habana José Antonio Echeverría, Calle 114, No. 11901. e/Ciclovía y Rotonda, Marianao, Havana 19390, CubaDepartment of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyDispersion modeling is a useful tool for reproducing the spatial–temporal distribution of pollutants emitted by industrial sites, particularly in the environmental odor field. One widely used tool, accepted by regulatory agencies for environmental impact assessments, is the CALPUFF model, which requires a large number of input variables, including meteorological and orographical variables. The reliability of model results depends on the accuracy of these input variables. The present research aims to discuss a comparative study of odor dispersion modeling by initializing the CALMET meteorological processor with different input data: surface and upper air observational meteorological data, 3D prognostic data, and a blend of prognostic and measured data. Two distinct sources (a point and an area source) and two different simulation domains in Cuba and Italy are considered. The analysis of results is based on odor impact criteria enforced in some Italian regions by computing the 98th percentile of odor peak concentrations on an annual basis. For the area source, simulation results reveal that the ‘OBS’ and ‘HYBRID’ modes are largely comparable, whereas prognostic data tend to underestimate the odor concentrations, likely due to a reduced percentage of wind calms. For point sources, different input meteorological settings provide comparable results, with no significant differences.https://www.mdpi.com/2076-3417/14/6/2457dispersion modelingCALPUFF modelodor impact assessmentintercomparisonWRFmeteorological data |
| spellingShingle | Francesca Tagliaferri Laura Facagni Marzio Invernizzi Adrian Luis Ferrer Hernández Anel Hernández-Garces Selena Sironi Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets Applied Sciences dispersion modeling CALPUFF model odor impact assessment intercomparison WRF meteorological data |
| title | Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets |
| title_full | Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets |
| title_fullStr | Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets |
| title_full_unstemmed | Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets |
| title_short | Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets |
| title_sort | odor impact assessment via dispersion model comparison of different input meteorological datasets |
| topic | dispersion modeling CALPUFF model odor impact assessment intercomparison WRF meteorological data |
| url | https://www.mdpi.com/2076-3417/14/6/2457 |
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