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dc.contributor.authorLópez de Ipiña, J.M.
dc.contributor.authorVaquero-Moralejo, Celina
dc.contributor.authorGutierrez-Cañas, C.
dc.contributor.authorPui, D.Y.H.
dc.date.accessioned2016-11-15T11:28:06Z
dc.date.available2016-11-15T11:28:06Z
dc.date.issued2015
dc.identifier.citation4th International Conference on Safe Production and Use of Nanomaterials (Nanosafe2014) IOP Publishing, Journal of Physics: Conference Series 617 (2015) 012003, doi:10.1088/1742-6596/617/1/012003en
dc.identifier.issn1742-6588en
dc.identifier.urihttp://hdl.handle.net/11556/331
dc.description.abstractIn multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzersin industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed.en
dc.description.sponsorshipResearch carried out by projects SCAFFOLD and EHS Advance were made possible thanks to funding from European Commission through FP7 (GA 319092) and Basque Country Government through ETORTEK Programme.en
dc.language.isoengen
dc.publisherIOP PUBLISHING LTD, DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLANDen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleAnalysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)en
dc.typeconferenceObjecten
dc.identifier.doi10.1088/1742-6596/617/1/012003en
dc.isiYesen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/319092/EU/Establishing a process and a platform to support standardization for nanotechnologies implementing the STAIR approach/NANOSTAIRen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/280535/EU/Innovative strategies, methods and tools for occupational risks management of manufactured nanomaterials (MNMs) in the construction industry/SCAFFOLDen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsNOAAen
dc.subject.keywordsMultisource industrial scenariosen
dc.journal.titleJournal of Physics: Conference Seriesen
dc.volume.number617en


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