Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment
For environmental odor nuisance, it is extremely important to identify the instantaneous concentration statistics. In this work, a Fluctuating Plume Model for different statistical moments is proposed. It provides data in terms of mean concentrations, variance, and intensity of concentration. The 90...
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/8/3310 |
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| author | Marzio Invernizzi Federica Capra Roberto Sozzi Laura Capelli Selena Sironi |
| author_facet | Marzio Invernizzi Federica Capra Roberto Sozzi Laura Capelli Selena Sironi |
| author_sort | Marzio Invernizzi |
| collection | DOAJ |
| description | For environmental odor nuisance, it is extremely important to identify the instantaneous concentration statistics. In this work, a Fluctuating Plume Model for different statistical moments is proposed. It provides data in terms of mean concentrations, variance, and intensity of concentration. The 90th percentile peak-to-mean factor, <i>R</i><sub>90</sub>, was tested here by comparing it with the experimental results (Uttenweiler field experiment), considering different Probability Distribution Functions (PDFs): Gamma and the Modified Weibull. Seventy-two percent of the simulated mean concentration values fell within a factor 2 compared to the experimental ones: the model was judged acceptable. Both the modelled results for standard deviation, <i>σ<sub>C</sub></i>, and concentration intensity, <i>I<sub>c</sub></i>, overestimate the experimental data. This evidence can be due to the non-ideality of the measurement system. The propagation of those errors to the estimation of <i>R</i><sub>90</sub> is complex, but the ranges covered are quite repeatable: the obtained values are 1–3 for the Gamma, 1.5–4 for Modified Weibull PDF, and experimental ones from 1.4 to 3.6. |
| first_indexed | 2024-03-10T12:33:08Z |
| format | Article |
| id | doaj.art-b713ca2e1c5448adb5eccbc467f37f39 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:33:08Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b713ca2e1c5448adb5eccbc467f37f392023-11-21T14:31:02ZengMDPI AGApplied Sciences2076-34172021-04-01118331010.3390/app11083310Development and Evaluation of a Fluctuating Plume Model for Odor Impact AssessmentMarzio Invernizzi0Federica Capra1Roberto Sozzi2Laura Capelli3Selena Sironi4Department 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, ItalyIndependent Researcher, 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, ItalyFor environmental odor nuisance, it is extremely important to identify the instantaneous concentration statistics. In this work, a Fluctuating Plume Model for different statistical moments is proposed. It provides data in terms of mean concentrations, variance, and intensity of concentration. The 90th percentile peak-to-mean factor, <i>R</i><sub>90</sub>, was tested here by comparing it with the experimental results (Uttenweiler field experiment), considering different Probability Distribution Functions (PDFs): Gamma and the Modified Weibull. Seventy-two percent of the simulated mean concentration values fell within a factor 2 compared to the experimental ones: the model was judged acceptable. Both the modelled results for standard deviation, <i>σ<sub>C</sub></i>, and concentration intensity, <i>I<sub>c</sub></i>, overestimate the experimental data. This evidence can be due to the non-ideality of the measurement system. The propagation of those errors to the estimation of <i>R</i><sub>90</sub> is complex, but the ranges covered are quite repeatable: the obtained values are 1–3 for the Gamma, 1.5–4 for Modified Weibull PDF, and experimental ones from 1.4 to 3.6.https://www.mdpi.com/2076-3417/11/8/3310odor impact assessmentdispersion modellingpeak-to-meanconcentration fluctuationfluctuating plume model |
| spellingShingle | Marzio Invernizzi Federica Capra Roberto Sozzi Laura Capelli Selena Sironi Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment Applied Sciences odor impact assessment dispersion modelling peak-to-mean concentration fluctuation fluctuating plume model |
| title | Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment |
| title_full | Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment |
| title_fullStr | Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment |
| title_full_unstemmed | Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment |
| title_short | Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment |
| title_sort | development and evaluation of a fluctuating plume model for odor impact assessment |
| topic | odor impact assessment dispersion modelling peak-to-mean concentration fluctuation fluctuating plume model |
| url | https://www.mdpi.com/2076-3417/11/8/3310 |
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