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<p>Accurate dispersion modelling of odour emissions is essential for assessing their environmental impact on citizens. In this context, the sensitivity analysis of dispersion models is crucial for identifying the factors that most influence their predictions, thereby improving accuracy and reliability in environmental assessments. This study aims to perform a sensitivity analysis of the Lagrangian particle model LAPMOD, focusing on some key parameters including turbulent parametrization, meteorological data interpolation, plume rise algorithms, and concentration prediction kernels. It also compares LAPMOD results with CALPUFF results, one of the most widely applied models for regulatory purposes and odour impact assessments, to evaluate dissimilarities in odour impact predictions for both area and point sources. The analysis reveals that the choice of concentration estimation kernel has a significant impact on LAPMOD's predictions, with the Gaussian Kernel yielding the most consistent results. All other investigated input parameters show minimal influence, leading to variations in the results always below 15%. Concerning the comparison between models, while both models show quite consistent trends for point sources, LAPMOD tends to estimate significantly lower odour impacts from area sources compared to CALPUFF, with estimated separation distances differing up to a factor of 4 between the two models.</p>

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Sensitivity analysis for odour dispersion modelling: LAPMOD evaluation and comparison with CALPUFF

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月5日

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  • Francesca Tagliaferri,
  • Alessandra Rota,
  • Marzio Invernizzi

摘要

Accurate dispersion modelling of odour emissions is essential for assessing their environmental impact on citizens. In this context, the sensitivity analysis of dispersion models is crucial for identifying the factors that most influence their predictions, thereby improving accuracy and reliability in environmental assessments. This study aims to perform a sensitivity analysis of the Lagrangian particle model LAPMOD, focusing on some key parameters including turbulent parametrization, meteorological data interpolation, plume rise algorithms, and concentration prediction kernels. It also compares LAPMOD results with CALPUFF results, one of the most widely applied models for regulatory purposes and odour impact assessments, to evaluate dissimilarities in odour impact predictions for both area and point sources. The analysis reveals that the choice of concentration estimation kernel has a significant impact on LAPMOD's predictions, with the Gaussian Kernel yielding the most consistent results. All other investigated input parameters show minimal influence, leading to variations in the results always below 15%. Concerning the comparison between models, while both models show quite consistent trends for point sources, LAPMOD tends to estimate significantly lower odour impacts from area sources compared to CALPUFF, with estimated separation distances differing up to a factor of 4 between the two models.