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Projeto de investigação
Centre of Geographical Studies
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North African dust intrusions and increased risk of respiratory diseases in Southern Portugal
Publication . Silva, T.; Fragoso, M.; Almendra, R.; Vasconcelos, J.; Lopes, A.; Faleh, A.
The study of dust intrusions in Portugal is still a subject on which little investigation has been made, especially in terms of their effects. Thus, this work aims to achieve two goals: firstly, to characterize the dust intrusions in the study area; and secondly, to evaluate the possible statistical association between the dust intrusion days and hospital admissions due to respiratory diseases. Dust intrusions in Portugal are prevalent during the summer season. During this season, the dust plumes tend to cover broader areas than in the other seasons and they have origin in the North African countries. In the study area for the period between 2005 and 2015, the relative risk of urgent hospitalizations due to respiratory diseases was 12.6% higher during dust intrusion days. In order to obtain this statistical association, a Distributed Lag Nonlinear Model was developed. With this work, we expect to help the development of further studies regarding North African dust intrusions in Portugal, more precisely their effects on human health.
SoResilere—A Social Resilience Index Applied to Portuguese Flood Disaster-Affected Municipalities
Publication . Jacinto, Rita; Sebastião, Fernando; Reis, Eusébio; Ferrão, João
Decades of academic discussion on social resilience have led to the development of indicators, indexes, and different approaches to assessing it at national and local levels. The need to show real-world applications of such assessments is evident since resilience became a political and disaster risk reduction governance component. This article gives a full description of the methodology used to develop SoResilere, a new social resilience index applied to flood disaster-affected Portuguese municipalities. Study cases were selected according to historical databases, academic sources and governmental entities. Statistical methods for data dimension reduction, such as Factor Analysis (through Principal Component Analysis), were applied to the quantitative data and Optimal Scaling to the categorical data. SoResilere results were analyzed. Since SoResilere is a new tool, component weighting was applied to compare results with no weighting, although it did not affect the SoResilere status in 55.5% of the study cases. There is a tendency to look at the improvement of SoResilere results with component weighting due mainly to the quantitative subindex. There is no evidence of the benefits of component weighting, as no logical association or spatial pattern was found to support SoResilere status improvement in 22.22% of the study cases.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDP/00295/2020