Mostrar el registro sencillo del ítem

dc.contributor.authorRodríguez García, Inmaculada
dc.contributor.authorMoscoso López, José Antonio
dc.contributor.authorRuiz Aguilar, Juan Jesús
dc.contributor.authorGonzález Enrique, Francisco Javier
dc.contributor.authorRodríguez López, Juana Carmen
dc.contributor.authorTurias Domínguez, Ignacio J.
dc.date.accessioned2022-09-20T06:55:00Z
dc.date.available2022-09-20T06:55:00Z
dc.date.issued2021-07
dc.identifier.isbn978-84-18465-12-3
dc.identifier.urihttp://hdl.handle.net/10259/6931
dc.descriptionTrabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgoses
dc.description.abstractThe main aim of this work was to measure the influence of the volume of shipping over the Sulphur dioxide (SO2) concentration in the air pollution in two monitoring stations located at Algeciras city and Alcornocales Park developing the same analysis in these two locations. The target is to demonstrate the assumption that Algeciras is more affected by SO2 than Alcornocales Park which is 30 km far away from Algeciras Port. A multiple regression approach has been applied using wind data: wind direction (degrees) and wind speed (km/h) recorded in two weather stations, together with the volume of the gross tonnage per hour (GT/h) of vessels in the Bay of Algeciras to estimate SO2 concentration values in the two stations Algeciras and Alcornocales. The database contains records of hourly samples of these variables during the year 2019. Different artificial neural networks (ANNs) models were compared and the results showed that SO2 in Algeciras station could be better explained than the same pollutant in Alcornocales station. On the other hand, ANNs produced better results than linear models which means that nonlinear models fit best the data. A cross- validation procedure has been applied in order to assure the generalization capabilities of the tested models. The results showed that in Algeciras a more reliable estimation could be done reaching a correlation estimation between the model and the target (real) values of SO2. This fact highlights the major influence of maritime transport in the Bay of Algecirasen
dc.description.sponsorshipThis work is part of the research project RTI2018-098160-B-I00 supported by 'MICINN’ Programa Estatal de I+D+i Orientada a 'Los Retos de la Sociedad'. Data used in this work have been kindly provided by the Algeciras Port Authority and the Andalusian Regional Government.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherUniversidad de Burgos. Servicio de Publicaciones e Imagen Institucionales
dc.relation.ispartofR-Evolucionando el transportees
dc.relation.urihttp://hdl.handle.net/10259/6490
dc.subjectModelizaciónes
dc.subjectModellingen
dc.subjectSimulaciónes
dc.subjectSimulationen
dc.subjectTransporte marítimoes
dc.subjectMaritime transporten
dc.subject.otherIngeniería civiles
dc.subject.otherCivil engineeringen
dc.subject.otherTransportees
dc.subject.otherTransportationen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleComparison of maritime transport influence of SO2 levels in Algeciras and Alcornocales Park (Spain)en
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.36443/9788418465123es
dc.identifier.doi10.36443/10259/6931
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098160-B-I00/ES/DEEP LEARNING IN AIR POLLUTION FORECASTING
dc.page.initial1409es
dc.page.final1425es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


Ficheros en este ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem