Publicação
Tools to Predict Road Runoff Pollution
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia do Ambiente | pt_PT |
| dc.contributor.advisor | Fernandes, João | |
| dc.contributor.advisor | Coelho, Pedro | |
| dc.contributor.author | Galhardo, Duarte Gonçalves | |
| dc.date.accessioned | 2019-07-16T09:07:50Z | |
| dc.date.available | 2019-07-16T09:07:50Z | |
| dc.date.issued | 2018-12 | |
| dc.date.submitted | 2018 | |
| dc.description.abstract | The concern about the environment is constantly increasing. The conservation of water resources is among the major issues. If previously the major concern with this resource was only the quantitative level, nowadays the qualitative level is a concern equally important. Taking into account this major concern and due to the increasing urban development, road runoff has become a growing issue since it is a potential form of diffuse pollution. Because of the relevance of this source of pollution, road operators and environmental agencies have developed new models for road runoff prediction. In this dissertation, four of these models were assessed: PREQUALE (Portugal), Highways Agency Water Risk Assessment Tool (HAWRAT - UK), Kayhanian’s model (USA) and Stochastic Empirical Loading and Dilution Model (SELDM - USA). After a literature review on this subject, the study started with the collection of monitored data from 20 roads in six European countries. For each road, the Site Mean Concentrations (SMC) were calculated for total suspended solids (TSS), copper, zinc, lead and cadmium. From these the SMC of TSS were above the emission limit declared by the Portuguese regulation (Decree-Law 236/98, from 01 of August) for some of the roads. The second step was the evaluation of the four prediction models, through the comparison between monitored data and model results. Together with the visual observation, four error indices were calculated to check which model was best adapted to the European monitored data. It was verified that none of the models presents sufficiently robust values to be used like a general model of application to the whole Europe. Furthermore, a new prediction equation was developed. This equation was calibrated with data from all Europe, unlike the four previous models, which were calibrated for a country or region. As whole data were used to calibrate the model, the results agree with the data. Nevertheless, its use in real and different roads should be carefully assessed. | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/75597 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Road runoff | pt_PT |
| dc.subject | PREQUALE | pt_PT |
| dc.subject | HAWRAT | pt_PT |
| dc.subject | Kayhanian’s model | pt_PT |
| dc.subject | SELDM | pt_PT |
| dc.subject | SMC | pt_PT |
| dc.title | Tools to Predict Road Runoff Pollution | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Master Degree in Environmental Engineering | pt_PT |