{"title":"气味分散模型的敏感性分析:LAPMOD评价及与CALPUFF的比较","authors":"Francesca Tagliaferri, Alessandra Rota, Marzio Invernizzi","doi":"10.1007/s11869-025-01721-8","DOIUrl":null,"url":null,"abstract":"<div><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></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 5","pages":"1447 - 1461"},"PeriodicalIF":2.9000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-025-01721-8.pdf","citationCount":"0","resultStr":"{\"title\":\"Sensitivity analysis for odour dispersion modelling: LAPMOD evaluation and comparison with CALPUFF\",\"authors\":\"Francesca Tagliaferri, Alessandra Rota, Marzio Invernizzi\",\"doi\":\"10.1007/s11869-025-01721-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><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></div>\",\"PeriodicalId\":49109,\"journal\":{\"name\":\"Air Quality Atmosphere and Health\",\"volume\":\"18 5\",\"pages\":\"1447 - 1461\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-03-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s11869-025-01721-8.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Air Quality Atmosphere and Health\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11869-025-01721-8\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Air Quality Atmosphere and Health","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s11869-025-01721-8","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Sensitivity analysis for odour dispersion modelling: LAPMOD evaluation and comparison with CALPUFF
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.
期刊介绍:
Air Quality, Atmosphere, and Health is a multidisciplinary journal which, by its very name, illustrates the broad range of work it publishes and which focuses on atmospheric consequences of human activities and their implications for human and ecological health.
It offers research papers, critical literature reviews and commentaries, as well as special issues devoted to topical subjects or themes.
International in scope, the journal presents papers that inform and stimulate a global readership, as the topic addressed are global in their import. Consequently, we do not encourage submission of papers involving local data that relate to local problems. Unless they demonstrate wide applicability, these are better submitted to national or regional journals.
Air Quality, Atmosphere & Health addresses such topics as acid precipitation; airborne particulate matter; air quality monitoring and management; exposure assessment; risk assessment; indoor air quality; atmospheric chemistry; atmospheric modeling and prediction; air pollution climatology; climate change and air quality; air pollution measurement; atmospheric impact assessment; forest-fire emissions; atmospheric science; greenhouse gases; health and ecological effects; clean air technology; regional and global change and satellite measurements.
This journal benefits a diverse audience of researchers, public health officials and policy makers addressing problems that call for solutions based in evidence from atmospheric and exposure assessment scientists, epidemiologists, and risk assessors. Publication in the journal affords the opportunity to reach beyond defined disciplinary niches to this broader readership.