Water is all around us and is vital for all aspects of life. Studying the various compounds and life forms that inhabit natural waters lets us better understand the world around us.Remote sensing... Show moreWater is all around us and is vital for all aspects of life. Studying the various compounds and life forms that inhabit natural waters lets us better understand the world around us.Remote sensing enables global measurements with rapid response and high consistency. Citizen science provides new knowledge and greatly increases the scientific and social impact of research.In this thesis, we investigate several aspects of citizen science and remote sensing of water, with a focus on uncertainty and accessibility. We improve existing techniques and develop new methods to use smartphone cameras for accessible remote sensing of water. Show less
LCA has become an important method to study environmental impacts of human activities. Still, there are several methodological issues in LCA that can adversely affect the results reliability.... Show moreLCA has become an important method to study environmental impacts of human activities. Still, there are several methodological issues in LCA that can adversely affect the results reliability. Three of these issues relate to a) allocation, b) the representation of the time dimension and c) the interpretation of results in LCA. Uncertainties play a fundamental and underlying role for these issues. It is widely-agreed that correctly dealing with these different uncertainty sources is a vital step towards increasing the usefulness and reliability of LCA results. Practical ways to deal with uncertainty are needed. The aim of this thesis is to deepen the understanding of the uncertainty dimension of current LCA. By means of addressing different sources of uncertainty not yet addressed, with new methods, a clearer picture of the implications of different sources of uncertainty in LCA is provided. This thesis departed from broad domains of uncertainty including risk, uncertainty as conventionally described, ignorance and indeterminacies. The selected sources of uncertainty are in the domains of risk and conventional uncertainty i.e. those due to incomplete scientific knowledge and that are to some extent quantifiable. This does not mean that all can be known or quantified as ignorance and indeterminacies exist. Show less
Mendoza Beltran, M.A.; Heijungs, R.; Guinée, J.B.; Tukker, A. 2015
Purpose Despite efforts to treat uncertainty due to methodological choices in life cycle assessment (LCA) such as standardization, one-at-a-time (OAT) sensitivity analysis, and analytical and... Show morePurpose Despite efforts to treat uncertainty due to methodological choices in life cycle assessment (LCA) such as standardization, one-at-a-time (OAT) sensitivity analysis, and analytical and statistical methods, no method exists that propagate this source of uncertainty for all relevant processes simultaneously with data uncertainty through LCA. This study aims to develop, implement, and test such a method, for the particular example of the choice of partitioning methods for allocation in LCA, to be used in LCA calculations and software. Methods Monte Carlo simulations were used jointly with the CMLCA software for propagating into distributions of LCA results, uncertainty due to the choice of allocation method together with uncertainty of unit process data. In this study, a methodological preference is assigned to each partitioning method, applicable to multi-functional processes in the system. The allocation methods are sampled per process according to these preferences. A case study on rapeseed oil focusing on three greenhouse gas (GHG) emissions and their global warming impacts is presented to illustrate the method developed. The results of the developed method are compared with those for the same case similarly quantifying uncertainty of unit process data but accompanied by separate scenarios for the different partitioning choices. Results and discussion The median of the inventory flows (emissions) for separate scenarios varies due to the partitioning choices and unit process data uncertainties. Inventory variations are reflected in the global warming results. Results for the approach of this study vary with the methodological preference assigned to the different allocation methods per multi-functional process and with the continuous distribution of unit process data. The method proved feasible and implementable. However, absolute uncertainties only further increased. Therefore, it should be further researched to reflect relative uncertainties, more relevant for comparative LCAs. Conclusions Propagation of uncertainties due to the choice of partitioning methods and to unit process data into LCA results is enabled by the proposed method, while capturing variability due to both sources. It is a practical proposal to tackle unresolved debates about partitioning choices increasing robustness and transparency of LCA results. Assigning a methodological preference to each allocation method of multi-functional processes in the system enables pseudo-statistical propagation of uncertainty due to allocation. Involving stakeholders in determining these methodological preferences allows for participatory approaches. Eventually, this method could be expanded to also cover other ways of dealing with allocation and to other methodological choices in LCA. Show less
This thesis aims to evaluate the environmental sustainability of European imports of farmed aquatic food products from Asia, using life cycle assessment (LCA). Farming of Asian tiger prawn,... Show moreThis thesis aims to evaluate the environmental sustainability of European imports of farmed aquatic food products from Asia, using life cycle assessment (LCA). Farming of Asian tiger prawn, whiteleg shrimp, freshwater prawn, tilapia and pangasius catfish in Bangladesh, China, Thailand and Vietnam were chosen as representatives of the Asian aquaculture industry. Initial research revealed large discrepancies among LCA results driven by methodological choices and data sourcing. A protocol for quantifying dispersions around unit process data was therefore developed, characterising inherent uncertainty, spread (variability) and unrepresentativeness as the three major sources driving overall discrepancies. Results, propagated using Monte Carlo simulations, highlighted that the uncertainty related to LCA results could range with over an order of magnitude. For comparative purposes, however, only relative uncertainties are of relevance. Defining a hypothesis and using dependent sampling therefore allowed for several significant conclusions to be identified. Among these were significantly lower environmental impacts of Asian tiger shrimp farming in western Bangladesh, tilapia in Guangdong and pangasius in large-scale farms. Common environmental hot-spots included aqua-feeds, eutrophying effluents from farms, the use of benzalkonium chloride and other chlorine releasing compounds as disinfectants, and extensive use of paddle-wheels on shrimp farms. The research identified discrepancies Show less
Simoes, S.; Fortes, P.; Seixas, J.; Huppes, G. 2014
Real-world (black-box) optimization problems often involve various types of uncertainties and noise emerging in different parts of the optimization problem. When this is not accounted for,... Show moreReal-world (black-box) optimization problems often involve various types of uncertainties and noise emerging in different parts of the optimization problem. When this is not accounted for, optimization may fail or may yield solutions that are optimal in the classical strict notion of optimality, but fail in practice. Robust optimization is the practice of optimization that actively accounts for uncertainties and/or noise. Evolutionary Algorithms form a class of optimization algorithms that use the principle of evolution to find good solutions to optimization problems. Because uncertainty and noise are indispensable parts of nature, this class of optimization algorithms seems to be a logical choice for robust optimization scenarios. This thesis provides a clear definition of the term robust optimization and a comparison and practical guidelines on how Evolution Strategies, a subclass of Evolutionary Algorithms for real-parameter optimization problems, should be adapted for such scenarios. Show less
