<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-14T13:04:05Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/6931" metadataPrefix="etdms">https://riubu.ubu.es/oai/request</request><GetRecord><record><header><identifier>oai:riubu.ubu.es:10259/6931</identifier><datestamp>2024-05-17T09:56:38Z</datestamp><setSpec>com_10259.4_104</setSpec><setSpec>com_10259_2604</setSpec><setSpec>col_10259_6848</setSpec></header><metadata><thesis xmlns="http://www.ndltd.org/standards/metadata/etdms/1.0/" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.ndltd.org/standards/metadata/etdms/1.0/ http://www.ndltd.org/standards/metadata/etdms/1.0/etdms.xsd">
<title>Comparison of maritime transport influence of SO2 levels in Algeciras and Alcornocales Park (Spain)</title>
<creator>Rodríguez García, Inmaculada</creator>
<creator>Moscoso López, José Antonio</creator>
<creator>Ruiz Aguilar, Juan Jesús</creator>
<creator>González Enrique, Francisco Javier</creator>
<creator>Rodríguez López, Juana Carmen</creator>
<creator>Turias Domínguez, Ignacio J.</creator>
<subject>Modelización</subject>
<subject>Simulación</subject>
<subject>Transporte marítimo</subject>
<subject>Modelling</subject>
<subject>Simulation</subject>
<subject>Maritime transport</subject>
<description>Trabajo 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 Burgos</description>
<description>The main aim of this work was to measure the influence of the volume of shipping over&#xd;
the Sulphur dioxide (SO2) concentration in the air pollution in two monitoring stations&#xd;
located at Algeciras city and Alcornocales Park developing the same analysis in these two&#xd;
locations.&#xd;
The target is to demonstrate the assumption that Algeciras is more affected by SO2&#xd;
than Alcornocales Park which is 30 km far away from Algeciras Port. A multiple&#xd;
regression approach has been applied using wind data: wind direction (degrees) and wind&#xd;
speed (km/h) recorded in two weather stations, together with the volume of the gross&#xd;
tonnage per hour (GT/h) of vessels in the Bay of Algeciras to estimate SO2&#xd;
concentration values in the two stations Algeciras and Alcornocales. The database&#xd;
contains records of hourly samples of these variables during the year 2019. Different&#xd;
artificial neural networks (ANNs) models were compared and the results showed that&#xd;
SO2 in Algeciras station could be better explained than the same pollutant in&#xd;
Alcornocales station. On the other hand, ANNs produced better results than linear&#xd;
models which means that nonlinear models fit best the data. A cross- validation&#xd;
procedure has been applied in order to assure the generalization capabilities of the&#xd;
tested models. The results showed that in Algeciras a more reliable estimation could&#xd;
be done reaching a correlation estimation between the model and the target (real) values of&#xd;
SO2. This fact highlights the major influence of maritime transport in the Bay of&#xd;
Algeciras</description>
<date>2022-09-20</date>
<date>2022-09-20</date>
<date>2021-07</date>
<type>info:eu-repo/semantics/conferenceObject</type>
<identifier>978-84-18465-12-3</identifier>
<identifier>http://hdl.handle.net/10259/6931</identifier>
<identifier>10.36443/10259/6931</identifier>
<language>eng</language>
<relation>R-Evolucionando el transporte</relation>
<relation>http://hdl.handle.net/10259/6490</relation>
<relation>https://doi.org/10.36443/9788418465123</relation>
<relation>info: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</relation>
<rights>info:eu-repo/semantics/openAccess</rights>
<publisher>Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>