Please use this identifier to cite or link to this item: http://repositorio.lnec.pt:8080/jspui/handle/123456789/1011932
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dc.contributor.authorFernandes, J. N.pt_BR
dc.contributor.authorBarbosa, A. E.pt_BR
dc.date.accessioned2019-10-22T14:48:21Zpt_BR
dc.date.accessioned2019-10-25T10:31:18Z-
dc.date.available2019-10-22T14:48:21Zpt_BR
dc.date.available2019-10-25T10:31:18Z-
dc.date.issued2018-11pt_BR
dc.identifier.urihttps://repositorio.lnec.pt/jspui/handle/123456789/1011932-
dc.description.abstractThis report stands for the project deliverable 1.2 and concerns the results from task 1.2 of the Proper Project, namely Characterization and critical review of the tools to predict road runoff. Following the literature review conducted in task 1.1 where the most important pollutants in road runoff were identified, the aim of the present task is to evaluate the models from a theoretic point of view in order to choose the most feasible to be used by operators or road designers to predict pollution in road runoff. The selected predicting models were:  PREQUALE (Barbosa et al., 2011)  Highways Agency Water Risk Assessment Tool (HAWRAT) (Crabtree et al., 2008)  Multiple linear regression by Kayhanian et al. (2007)  Stochastic Empirical Loading and Dilution Model (SELDM) (Granato, 2013)  Multiple linear regression by Higgins (2007)  Risk Assessment of road stormwater runoff (RSS) (Gardiner et al., 2016) Each one was assessed taking into account the input data, the easiness of applicability and the consistency of the output results. These factors were classified by a score from 1 to 3 in order to have a global rating. This methodology was used to select the four models to be implemented in task 1.4 of the PROPER Project: PREQUALE; HAWRAT; Kayhanian et al. (2007) and SELDM.pt_BR
dc.language.isoengpt_BR
dc.rightsopenAccesspt_BR
dc.subjectRoad runoffpt_BR
dc.subjectPollutionpt_BR
dc.subjectPredicting modelspt_BR
dc.titlePROPER Project WP1 - Prediction of pollutant loads and concentrations in road runoff Task. 1.2. Critical review of the tools to predict road runoffpt_BR
dc.typereportpt_BR
dc.identifier.localedicaoLisboapt_BR
dc.description.commentsRelatório de Acesso Abertopt_BR
dc.description.sectorDHA/NREpt_BR
dc.identifier.proc0605/111/20989pt_BR
dc.contributor.arquivoSIMpt_BR
Appears in Collections:DHA/NRE - Relatórios Científicos

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