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Title: PROPER Project WP1 - Prediction of pollutant loads and concentrations in road runoff Task. 1.2. Critical review of the tools to predict road runoff
Authors: Fernandes, J. N.
Barbosa, A. E.
Keywords: Road runoff;Pollution;Predicting models
Issue Date: Nov-2018
Abstract: This 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.
Appears in Collections:DHA/NRE - Relatórios Científicos

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