Wind energy resource modelling in Portugal and its future large-scale alteration due to anthropogenic induced climate changes

The high dependence of Portugal from foreign energy sources (mainly fossil fuels), together with the international commitments assumed by Portugal and the national strategy in terms of energy policy, as well as resources sustainability and climate change issues, inevitably force Portugal to invest in its energetic self-sufficiency. The 20/20/20 Strategy defined by the European Union defines that in 2020 60% of the total electricity consumption must come from renewable energy sources. Wind energy is currently a major source of electricity generation in Portugal, producing about 23% of the national total electricity consumption in 2013. The National Energy Strategy 2020 (ENE2020), which aims to ensure the national compliance of the European Strategy 20/20/20, states that about half of this 60% target will be provided by wind energy. This work aims to implement and optimise a numerical weather prediction model in the simulation and modelling of the wind energy resource in Portugal, both in offshore and onshore areas. The numerical model optimisation consisted in the determination of which initial and boundary conditions and planetary boundary layer physical parameterizations options provide wind power flux (or energy density), wind speed and direction simulations closest to in situ measured wind data. Specifically for offshore areas, it is also intended to evaluate if the numerical model, once optimised, is able to produce power flux, wind speed and direction simulations more consistent with in situ measured data than wind measurements collected by satellites. This work also aims to study and analyse possible impacts that anthropogenic climate changes may have on the future wind energetic resource in Europe. The results show that the ECMWF reanalysis ERA-Interim are those that, among all the forcing databases currently available to drive numerical weather prediction models, allow wind power flux, wind speed and direction simulations more consistent with in situ wind measurements. It was also found that the Pleim-Xiu and ACM2 planetary boundary layer parameterizations are the ones that showed the best performance in terms of wind power flux, wind speed and direction simulations. This model optimisation allowed a significant reduction of the wind power flux, wind speed and direction simulations errors and, specifically for offshore areas, wind power flux, wind speed and direction simulations more consistent with in situ wind measurements than data obtained from satellites, which is a very valuable and interesting achievement. This work also revealed that future anthropogenic climate changes can negatively impact future European wind energy resource, due to tendencies towards a reduction in future wind speeds especially by the end of the current century and under stronger radiative forcing conditions.

The impact of biofuels for road traffic on air quality : a modelling approach

The selection of the energy source to power the transport sector is one of the main current concerns, not only relative with the energy paradigm but also due to the strong influence of road traffic in urban areas, which highly affects human exposure to air pollutants and human health and quality of life. Due to current important technical limitations of advanced energy sources for transportation purposes, biofuels are seen as an alternative way to power the world’s motor vehicles in a near-future, helping to reduce GHG emissions while at the same time stimulating rural development. Motivated by European strategies, Portugal, has been betting on biofuels to meet the Directive 2009/28/CE goals for road transports using biofuels, especially biodiesel, even though, there is unawareness regarding its impacts on air quality. In this sense, this work intends to clarify this issue by trying to answer the following question: can biodiesel use contribute to a better air quality over Portugal, particularly over urban areas? The first step of this work consisted on the characterization of the national biodiesel supply chain, which allows verifying that the biodiesel chain has problems of sustainability as it depends on raw materials importation, therefore not contributing to reduce the external energy dependence. Next, atmospheric pollutant emissions and air quality impacts associated to the biodiesel use on road transports were assessed, over Portugal and in particular over the Porto urban area, making use of the WRF-EURAD mesoscale numerical modelling system. For that, two emission scenarios were defined: a reference situation without biodiesel use and a scenario reflecting the use of a B20 fuel. Through the comparison of both scenarios, it was verified that the use of B20 fuels helps in controlling air pollution, promoting reductions on PM10, PM2.5, CO and total NMVOC concentrations. It was also verified that NO2 concentrations decrease over the mainland Portugal, but increase in the Porto urban area, as well as formaldehyde, acetaldehyde and acrolein emissions in the both case studies. However, the use of pure diesel is more injurious for human health due to its dominant VOC which have higher chronic hazard quotients and hazard indices when compared to B20.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.