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dc.contributor.authorCARNEVALE C.en_GB
dc.contributor.authorFINZI G.en_GB
dc.contributor.authorPISONI ENRICOen_GB
dc.contributor.authorPEDERZOLI Annaen_GB
dc.contributor.authorTURRINI Enricoen_GB
dc.contributor.authorVOLTA Luisaen_GB
dc.date.accessioned2014-04-04T00:03:21Z-
dc.date.available2014-04-03en_GB
dc.date.available2014-04-04T00:03:21Z-
dc.date.created2014-04-01en_GB
dc.date.issued2014en_GB
dc.date.submitted2014-01-28en_GB
dc.identifier.citation9th International Conference on Air Quality - Science and Applicationen_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC88690-
dc.description.abstractArtificial Neural Networks (ANNs) are used to link PM10 concentration from a deterministic air quality model and precursor scenario emissions to the PM10 concentration of a base case re-analyzed using PM10 surface observations. A case study over Northern Italy is presented. The goal is to show that ANNs are capable to model the nonlinear relationship between precursor emissions and PM10 reanalyzed concentrations, so they can be used for reducing the under-prediction of this pollutant by deterministic air quality models in scenario simulations.en_GB
dc.description.sponsorshipJRC.H.2-Air and Climateen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherUniversity of Hertfordshire and Institute of Meteorology and Climate Researchen_GB
dc.relation.ispartofseriesJRC88690en_GB
dc.titleUse of artificial neural networks for PM10 re-analysis over northern Italyen_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Sustainable Resources

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