{"title":"Area source emissions: a validation study of CALPUFF and LAPMOD models","authors":"Francesca Tagliaferri, Alessandra Rota, Marzio Invernizzi","doi":"10.1016/j.aeaoa.2025.100348","DOIUrl":null,"url":null,"abstract":"<div><div>Dispersion models are essential for predicting pollutant behavior in the atmosphere, but discrepancies between models can introduce uncertainties. Validating models with real data is crucial to ensuring accuracy. Previous studies have highlighted differences between CALPUFF and the particle model LAPMOD: while both yield relatively similar results for point sources, significant discrepancies arise for area sources. This study assesses the performance of both models using experimental datasets. The analysis shows that CALPUFF estimates higher concentrations than LAPMOD and performs better against observed values, meeting all validation criteria. LAPMOD is less consistent, with a non-optimal FAC2 and high VG due to outliers caused by receptor arrangement. However, both models align well with experimental data under ideal conditions. In conclusion, CALPUFF proves more reliable, whereas LAPMOD, despite its tendency to underestimate, provides useful results once outliers are excluded.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"27 ","pages":"Article 100348"},"PeriodicalIF":3.4000,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Environment: X","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590162125000383","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Dispersion models are essential for predicting pollutant behavior in the atmosphere, but discrepancies between models can introduce uncertainties. Validating models with real data is crucial to ensuring accuracy. Previous studies have highlighted differences between CALPUFF and the particle model LAPMOD: while both yield relatively similar results for point sources, significant discrepancies arise for area sources. This study assesses the performance of both models using experimental datasets. The analysis shows that CALPUFF estimates higher concentrations than LAPMOD and performs better against observed values, meeting all validation criteria. LAPMOD is less consistent, with a non-optimal FAC2 and high VG due to outliers caused by receptor arrangement. However, both models align well with experimental data under ideal conditions. In conclusion, CALPUFF proves more reliable, whereas LAPMOD, despite its tendency to underestimate, provides useful results once outliers are excluded.