Publicação
Objective assessment of microplastic contamination of environmental areas
| datacite.subject.fos | Ciências Naturais::Ciências Químicas | pt_PT |
| dc.contributor.advisor | Silva, Ricardo Jorge Neves Bettencourt da | |
| dc.contributor.advisor | Palma, Carla Maria Ferreira Mesquita | |
| dc.contributor.author | Morgado, Vanessa | |
| dc.date.accessioned | 2025-02-18T15:18:07Z | |
| dc.date.available | 2025-02-18T15:18:07Z | |
| dc.date.issued | 2024-02-15 | |
| dc.date.submitted | 2023-07-20 | |
| dc.description.abstract | The widespread presence of microplastics and their impact on the environment and human health has led to increased attention to this type of contamination. The community has been monitoring microplastic contamination in various aquatic systems to assess its relevance and identify its sources, enabling the design of effective strategies to mitigate its impact. However, understanding the impact of microplastics in aquatic systems requires an objective characterisation of this contamination, which is achieved by collecting data with known uncertainty. The main goal of this work was to develop strategies for the objective assessment of microplastic contamination levels and trends in relevant environmental areas, specifically focusing on rias, rivers, and a coastal area in Portugal. A total of 83 samples were collected and analysed using a microscope coupled to a Fourier transform infrared spectrometer (micro-FTIR) after implementing a suitable sample collection and preparation procedure. The initial research focused on developing automated techniques for the identification of microplastics in environmental samples using FTIR. This involved determining robust spectral match and match values thresholds, ensuring adequately low false result rates. Subsequently, methodologies were developed for the objective quantification of microplastic contamination in large environmental areas. This work involved a pioneering detailed simulation of the mean contamination of a large environmental area using the Monte Carlo method. This simulation considered all relevant and correlated uncertainty components, including systematic and random effects affecting sample analysis and GPS coordinates of collected samples. The simulation produced contamination levels and trends evaluations with uncertainty as a function of these components, the number and distribution of collected samples, and spatial contamination distribution. The collected data objectively proved that PET is the most abundant polymer among the studied microplastics, and observed contaminations did not significantly vary after one year. This work proposes a revolutionary way of assessing microplastic contamination in the environment, allowing for an objective and binding understanding of the impact of different plastic waste management policies in the protection of ecosystems. | pt_PT |
| dc.identifier.tid | 101661720 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.5/98524 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Microplastics | pt_PT |
| dc.subject | Sediments | pt_PT |
| dc.subject | Uncertainty | pt_PT |
| dc.subject | Spectral match | pt_PT |
| dc.subject | Spatial distribution | pt_PT |
| dc.subject | Microplásticos | pt_PT |
| dc.subject | Sedimentos | pt_PT |
| dc.subject | Incerteza | pt_PT |
| dc.subject | Correlação espetral | pt_PT |
| dc.subject | Distribuição espacial | pt_PT |
| dc.title | Objective assessment of microplastic contamination of environmental areas | pt_PT |
| dc.type | doctoral thesis | |
| dspace.entity.type | Publication | |
| person.familyName | Morgado | |
| person.givenName | Vanessa | |
| person.identifier | AAF-6058-2021 | |
| person.identifier.ciencia-id | 9D17-43B6-57F7 | |
| person.identifier.orcid | 0000-0002-3888-9154 | |
| person.identifier.scopus-author-id | 57203956062 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | doctoralThesis | pt_PT |
| relation.isAuthorOfPublication | c7481217-5f26-4f37-8ea1-dd453290aa74 | |
| relation.isAuthorOfPublication.latestForDiscovery | c7481217-5f26-4f37-8ea1-dd453290aa74 | |
| thesis.degree.name | Tese de doutoramento, Química (Química Analítica), Universidade de Lisboa, Faculdade de Ciências, 2024 | pt_PT |