L. Coquelin, N. Fischer, L. Brusquet, Gilles Fleury, C. Motzkus, F. Gensdarmes
{"title":"Aerosol size distribution measurement using a SMPS: Scanning mode and uncertainty analysis","authors":"L. Coquelin, N. Fischer, L. Brusquet, Gilles Fleury, C. Motzkus, F. Gensdarmes","doi":"10.1109/I2MTC.2012.6229546","DOIUrl":null,"url":null,"abstract":"A model to simulate SMPS (Scanning Mobility Particle Sizer) measurement and the associated uncertainty analysis when axial DMA (Differential Mobility Analyser) classifier operates under scanning mode conditions is described. Starting from simulated SMPS raw data, a fast estimation of aerosol size distribution measurement using regularization technique is performed. Then, global sensitivity analysis is used to discriminate significant parameters of the system and, as a preliminary result, a 95% confidence region is obtained by Monte Carlo simulations on an atmospheric aerosol size distribution.","PeriodicalId":387839,"journal":{"name":"2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2MTC.2012.6229546","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A model to simulate SMPS (Scanning Mobility Particle Sizer) measurement and the associated uncertainty analysis when axial DMA (Differential Mobility Analyser) classifier operates under scanning mode conditions is described. Starting from simulated SMPS raw data, a fast estimation of aerosol size distribution measurement using regularization technique is performed. Then, global sensitivity analysis is used to discriminate significant parameters of the system and, as a preliminary result, a 95% confidence region is obtained by Monte Carlo simulations on an atmospheric aerosol size distribution.