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Arsenic (As) and antimony (Sb) are elements that naturally occur in the environment. They are in the same chemical group and share chemical properties. Both are toxic to biota at low, environmentally relevant concentrations. Elevated concentrations of both elements in the environment occur at mining and processing sites or can even occur naturally, i.e. due to oxidation of As and Sb containing sulphide minerals or due to reductive dissolution of As and Sb containing iron minerals. The widespread natural contamination of As in groundwater is threatening the health of millions globally.The speciation of As and Sb in the environment determines the toxicity and mobility in terrestrial and aquatic environments, therefore affecting their environmental risks. In soils, organic matter (SOM) strongly influences the speciation of As and Sb by many different underlying processes, which are not fully understood and quantified yet, restricting the use of process-based models in As and Sb risk assessment. The goal of this study is to better understand and quantify the role of soil organic matter on the mobility of As and Sb in soils at trace levels, under different conditions, i.e. in aerobic conditions, in anaerobic conditions and in aerobic conditions over prolonged contact times. It is hypothesized that increasing SOM mainly mobilises As and Sb in soils. That is related to the strong interactions between the negatively charged SOM and the positively charged surfaces of iron (Fe) and aluminium (Al) oxyhydroxides. These strong interactions result in competitive and electrostatic effects of SOM to As and Sb sorption at the surfaces of the Fe and Al minerals. Furthermore, the concentration of SOM will determine the extent of change of As and Sb mobility during waterlogging of soils because increasing SOM increases the extent of reducing conditions and mediates redox-cycling of the different As and Sb species and of their sorbents. Finally, the vertical long-term migration of As and Sb in a soil profile will be controlled by the soil organic carbon (SOC) gradient from top to subsurface soil layers. Observational, experimental and computational methods were used to unravel the effects of SOM on the sorption of As and Sb at trace levels in whole soils.First, two soil profiles in agricultural grassland were sampled by excavating 3m deep soil pits and samples were taken from different depths. Samples from the surrounding area of the soil pits and from other soil types and locations were collected and added to the analysis to expand the range of soil characteristics. In total, 29 soil samples were collected, exhibiting a natural SOC range between <0.1 - 50 g kg-1. The adsorption of arsenate (As(V), further denoted as AsO4) and antimonate (Sb(V), further denoted as Sb(OH)6) was measured in all samples and in four samples which had experimentally increased SOM concentrations with ~1 g kg-1. The adsorption data of AsO4 and Sb(OH)6 were then analysed with the CD-MUSIC model of ferrihydrite, including the competitive effect of SOM by site competition and electrostatic interaction (Chapter 2&3). The AsO4 and Sb(OH)6 sorption, relative to the concentration of Fe and Al hydroxides, in the natural soils decreased by ~1 (Sb) to 2 (As) orders of magnitude with increasing concentration of soil organic carbon (SOC). Experimentally increasing the SOM concentration decreased As and Sb sorption, up to a factor of 8 (Sb) to 15 (As). The competitive effect of SOM on the sorption of AsO4 and Sb(OH)6 was included in the geochemical model via the definition of reactive SOM, further denoted as RO-. The concentration of RO-, relative to the concentration of Fe and Al hydroxides, is related to the SOC concentration in the soil by a Langmuir-like sorption model. Saturation of binding sites for organic ligands was observed at SOC concentrations >10 g kg-1 at a molar RO- to Fe and Al oxyhydroxide concentrations of 0.34. These analyses show that SOM enhances the mobility of AsO4 and Sb(OH)6 in soils and that this can be mathematically described by including competitive and electrostatic effects of humic substances towards surface complexation of AsO4 and Sb(OH)6 at the reactive surface sites of Fe and Al hydroxides in the soils.An ageing study of added Sb(OH)6 in soils was performed to evaluate prolonged, slow sorption reactions of Sb(OH)6 in soils, and to determine the effects of soil organic matter and other soil properties on the extent of these possible ageing reactions (Chapter 4). To do so, samples from one of the sampled soil profiles were spiked with a low dose of Sb(OH)6 by means of a stock solution. Samples were aerobically incubated for 6 months and the desorption of Sb(OH)6 was measured immediately after spiking and 1 and 6 months after spiking. After six months of ageing, sorption of Sb(OH)6 increased by a factor 3-6 between soil samples. Both the Sb(OH)6 sorption as the extent of slow reactions increased with increasing Fe and Al hydroxide concentrations and with decreasing pH of the soil samples, with the soil organic carbon concentration of minor importance in determining the extent of Sb ageing. We hypothesize that the added antimonate anion adsorbs to amorphous (oxy)hydroxides with gradual diffusion into the micropores, similar to what is known for other oxyanions (e.g. phosphate and arsenate).The mobility of arsenic (As) and antimony (Sb) in soil largely changes under waterlogged condition due to the redox reactions involved. Soil organic matter affects As and Sb mobility under such condition by acting as an electron donor, as an electron shuttle and by acting as a ligand competing with the sorption of the As and Sb oxyanions. An experiment was set up to disentangle these effects of SOM when reducing conditions develop (Chapter 5). Soil samples with a natural SOM gradient, i.e. from one of the sampled soil profiles, were either or not incubated under waterlogged conditions in an anaerobic hood for 63-70 days, and glucose was either or not added to the anaerobic incubated samples as an electron donor that does not act as a competing ligand, nor as an electron shuttle. Aerobically incubated samples were included as a control. The mobility of As was enhanced by up to 3 orders of magnitude upon waterlogging while the mobility of added Sb decreased up to a factor of 20 upon waterlogging. These changes were, however, only found in high SOM soils or in presence of glucose. Moreover, the net effects of change in sorption of both elements upon waterlogging were similar in samples containing natural organic matter or in samples amended with glucose. This showed that the SOM dependent changes in As and Sb mobility upon waterlogging soil are primarily related to the role of SOM as electron donor. The sorption of As in anaerobic subsoils, low in native soil organic matter, was still stronger than in anaerobic topsoil indicating that competition reactions of SOM on As retention are also controlling the As mobility in anaerobic conditions.Taken together, our study shows that increasing soil organic matter increases the As and Sb mobility in aerobic soils and that this is mainly dominated by competitive and electrostatic effects between organic ligands and the As and Sb species to the Fe and Al hydroxides. A solute transport model was included illustrating that As and Sb migration in polluted soil is retarded in low SOM containing subsoils. Increasing soil organic matter accelerates reduced conditions upon waterlogging which promotes As mobility but reduces Sb mobility. It is concluded that fate models on As and Sb migration in the environment must include the striking role of soil organic matter on the mobility of these metalloids. This can now be done using the calibrated values of reactive organic matter in geochemical codes derived in this work.

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Liquid biofuels are considered as a strategy for achieving energy security, stimulating rural development and mitigating climate change. Low-income countries largely focus on biofuel production from non-food crops and trees grown on underutilized lands, in order to minimize food, fuel and environmental trade-offs. Unfounded claims on the potential of such alternative biofuel crops led to large-scale investments, especially in jatropha (Jatropha curcas) monoculture plantations. Productivity and economic outcomes proved to be poor, however, which subsequently led to a global downturn in research and investment, leaving the potential of other species and approaches unexplored. In Chapter 1, we hypothesize that agroforestry-based approaches that carefully integrate a mix of native biofuel tree species into the existing farming system, offer unique opportunities in comparison with jatropha monocultures, including limited risk, increased by-product valorization, and expanded ecosystem services provision. This PhD study aims at evaluating the bio-economic potential of agroforestry-based biofuel systems by quantifying three critical success factors: farmer perception and adoption, oilseed yield, and economic impact. The in-depth empirical analysis is based on a mature agroforestry-based biofuel program in Hassan district, South India. Since 2007 this program stimulates the cultivation of native oilseed tree mixtures on farms through a range of extension and implementation activities.In Chapter 2 we assess farmer perception and adoption of oilseed trees and the biofuel value chain in Hassan district. To this end, cross-sectional survey data of 396 farm-households in Hassan district are collected. We find that although 60% of the farmers cultivate oilseed trees, oilseed collection rates are generally low (13%), as are oil expeller use (<1%) and biofuel marketing (<1%). To examine the impact of the biofuel program on adoption, we use regression analyses addressing various forms of selection bias. We find that various activities of the biofuel program stimulate oilseed tree cultivation but not oilseed collection. Low seed prices, high opportunity costs of labour, and value chain underdevelopment are major factors impeding households to collect seeds from planted or wild oilseed species.In Chapter 3 we use a labelled choice experiment to assess the same farmers' preferences for alternative production systems, value chain organisations and market developments. This allows predicting the extent to which hypothetical changes in these characteristics could change the likelihood of adoption. Our results demonstrate that biofuel programs can benefit from ex ante analyses to improve their design. We find that most farmers (71%) are likely to adopt biofuel trees in most scenarios, especially species with relatively high yields, low labour requirements and high oilseed prices. Nevertheless, value chain reorganization through contracting and labour provision proves to be the key lever to stimulate adoption. This calls for further research on effective contract design and implementation, and for developing alternative business models.Chapters 2 and 3 indicate that pongamia (Millettia pinnata) is the species with highest adoption potential. However, yield levels and dynamics of this undomesticated species remain poorly understood, despite of the fundamental role they play for its biofuel potential. In Chapter 4 we address the critical lack of scientific evidence by collecting primary seed and oil yield data from 81 pongamia trees in South India, and explore which factors might determine yields using empirical models. Our results indicate that annual seed and oil yields for pongamia trees vary widely, but generally remain below 2500 kg/ha and 1000 liter/ha, respectively. This current field performance is substantially lower than commonly reported figures in the literature. Furthermore, our results suggest that a complex interplay between genotype, environment and agronomy leads to large spatiotemporal variation in yields, and that this interplay remains poorly understood. Long-term yield monitoring is required to get better insights into yield mechanisms, and to assess the actual potential of pongamia as a reliable and significant source of biofuel feedstock.In Chapter 5 we address common methodological shortcomings in the literature on profitability of novel biofuels, by developing a sound framework for quantifying the long-term financial performance of agroforestry-based biofuel value chains. The framework is applied to calculate profitability of pongamia cultivation and processing in Hassan district. The results show that pongamia cultivation has limited financial potential, and is only profitable in small-scale settings, in the middle to long term and for a subset of farmers. If biodiesel is envisaged as the end product, the value chain requires substantial fiscal and marketing support to be economically viable. For current prices, financial performance is much higher if the seed oil is marketed instead of processed to biodiesel. These findings are case-specific, while the developed framework opens the door to comprehensive investigation of the financial performance of other oilseed tree species and in other regions.The interdisciplinary framework allows concluding that the potential of agroforestry-based biofuel systems as sources of energy, income and employment is currently very limited. The program in South India succeeds as an agroforestry program but not as a biofuel program. Low profitability impedes local farmers to collect oilseeds, while processors have no financial incentive to convert oil to biodiesel. We find that similar challenges pertain to small-scale agroforestry systems as to jatropha-based plantation systems, although the former are a Low-Risk High-Diversity approach to build feedstock for the future. The wider validity of our findings should be further explored to determine in which niches alternative biofuel crops may still have potential.

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Urbanization presents one of the major challenges to humankind in the current century. Our cities are true drivers of global environmental change, but at the same time also represent the most susceptible areas to be suffering from the local impacts of these ongoing changes (e.g. through heat waves, flooding and air pollution). Sustainable urban management and development therefore focuses on safeguarding the local quality of life by reducing both the local and global impacts of urbanization. Urban green is increasingly recognized as a valuable means in this respect, given the many benefits (or ecosystem services) it may provide to human society. Mapping these services in a quantitative and spatially-explicit way, would enable urban planners and managers to identify those zones within a city's boundaries that should be prioritized in future urban development and would allow for a critical and objective evaluation of different urban planning scenarios. Generating such ecosystem service maps however requires detailed information about urban land cover composition in general, and concerning the specific type, properties and state of urban green in particular. By measuring the interaction of solar radiation with the earth's surface in high spatial and spectral detail, airborne hyperspectral remote sensing in theory allows for the detailed characterization of the urban environment. However, the high spatial and spectral complexity of urban areas still actively impedes such detailed assessments. In this PhD dissertation, we therefore looked into the concept of data fusion to overcome specific remaining issues in this respect. Our study area comprises the eastern part of the Brussels Capital Region (Belgium), which has been covered by 2 m resolution hyperspectral data in Summer 2015.The highly heterogeneous nature of our cities lead to the phenomenon of mixed pixels in remotely sensed imagery. Several spectral unmixing approaches have therefore been proposed in the past, aiming to determine the true composition of individual image pixels. Multiple Endmember Spectral Mixture Analysis (MESMA) is amongst the most commonly used techniques and relies on a spectral library (i.e. a collection of pure material spectra, or endmembers), ideally covering all endmember variability present within a given scene. Despite the advent of several automated endmember extraction techniques, building such libraries represents a time-consuming, yet crucial task. In Chapter 2, we therefore proposed fusing already existing urban spectral libraries as the basis for an alternative solution. As this generic urban spectral library is expected to contain a high proportion of irrelevant spectra with regard to any particular image to be processed, we developed an automated endmember selection technique (AMUSES) allowing to refine a given spectral library in function of a given image. Several experiments on simulated and real hyperspectral imagery with libraries of increasing generic nature confirmed the potential of AMUSES in this respect and have additionally shown a significant increase in subsequent mapping accuracies compared to more traditional library pruning techniques. Despite these improvements, considerable classification errors were still observed, which could be attributed to the high spectral similarity between land cover classes (e.g. roof versus pavement and grass versus tree). In Chapter 3, we therefore integrated height information extracted from airborne LiDAR data into the MESMA algorithm and successfully reduced confusion between spectrally similar, but structurally different land cover classes. In particular, height distribution information within single image pixels was employed as an additional endmember selection constraint and as fraction constraints during the unmixing procedure, thereby also reducing computation times by up to 85 %. Band selection (i.e. using different, relevant subsets of spectral bands for each individual land cover class) did not further improve classification results, but resulted in an additional decrease in computation times by 50 %. The added value of the proposed techniques for processing imagery featuring lower spectral and/or spatial resolution has been demonstrated for both the library pruning technique and the integration with LiDAR. Given the increasing availability of (hyper-)spectral data and the associated need for highly automated and efficient processing algorithms, both chapters are expected to contribute towards the development of more universal urban mapping workflows.In order to facilitate the mapping of ecosystem services provided by urban green, a functional urban green typology, covering 23 distinct types, was established in Chapter 4. Given the high spectral and structural similarities between the proposed types, object-based image analysis, combined with Random Forest classification, was employed as a more advanced image fusion technique to further explore the complementarity between airborne hyperspectral and LiDAR data. Height and intensity derived from LiDAR data were found to be the most important features overall, but required additional spectral information to accomplish good classification results at a thematically detailed level. In this respect, hyperspectral data was found to be more useful compared to multispectral data, although the latter did feature a higher spatial resolution. Despite these encouraging results, spatially continuous mapping of urban green still was severely impeded by shadow and adjacency effects, resulting in class-wise kappa values below 0.5 for detailed shrub and herbaceous vegetation types. Additionally incorporating phenological information and adopting multi-scale segmentation approaches, is expected to further increase the potential of remote sensing for detailed urban green mapping.Finally, in Chapter 5, chlorophyll concentration and Leaf Area Index (LAI) of urban trees were determined using hyperspectral and LiDAR data, and were subsequently combined into an objective estimation of tree health. Similar to the findings in Chapter 4, mixed pixel effects significantly complicated the analysis. As a result, Partial Least Squares regression, being able to learn from local calibration data and employing the full hyperspectral signal, highly outperformed existing spectral indices. As our tree health assessment showed good agreement with visual tree assessment data obtained on the ground, the proposed workflow could be used as a basis for further research focusing on revealing the drivers of urban tree health. Further efforts should additionally be devoted to the early detection of stress in urban trees in order to optimize the utility of this tool for urban green managers.Although the concept of ecosystem services has certainly increased the awareness on the importance of urban green for safeguarding the quality of life in our future cities, implementation of these concepts into urban management and design is still lagging behind. The research conducted throughout this PhD dissertation has confirmed the potential of remote sensing data and technology to contribute to the detailed assessment of urban ecosystem services, as such providing an important stepping stone towards their operational use. Due to the typically high spatial and spectral complexity of our cities, the urban remote sensing community is highly encouraged to continue the search for complementary information derived from both new (e.g. social media, sensor networks) and existing data sources in order to optimize the workflows proposed here.

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The first objective of the PhD is to investigate structures of water fluxes at the landscape scale. Whether the structure or spatial distribution of water fluxes and water residence times in the system can be linkedto the spatial structure of biogeochemical processes or soil formation processes in the system will be a key question. At the landscape scale, non-invasive geophysical techniques can be used to characterize the structure of the subsurface that controls the water fluxes in the system. Wewill make surveys of the subsurface structure and derive the topographyof subsurface layers using methods like ground penetrating radar (GPR),electro magnetic induction (EMI) (Lavoue et al., 2010, Mester et al., 2011), and electrical resistivity tomography (ERT) (Vanderborght et al., 2013). In order to characterize lateral flow pathways and networks and monitor their appearance, we will use ERT. A comparison between observed networks and networks that are modeled based on a DEM of the impeding layers will be made. At the identified locations of subsurface flow paths,soil and water samples will be taken for biogeochemical analyses. A combination of structure information obtained with geophysical methods and time series of tracer concentrations (e.g. isotope tracers) in surface water provides crucial information that is needed to link process areas with process dynamics at these scales (Koch et al., 2009).The second objective is to simulate water fluxes (infiltration, lateral runoff and lateral subsurface groundwater and interflow) at specific times during the evolution or development of a landscape. The identified flux controlling structures will be used to set up a 3-D flow and transport model at the catchment scale. The PARFLOW model (Kollet and Maxwell, 2006) that describes 3-D variably saturated water flow, solute transport, soil-atmosphere interactions, and surface runoff will be used. The model is parallelized and tailored to massively parallel supercomputer architectures sothat it can be used to simulate water fluxes at landscape scale with a resolution that is required to resolve effect of structural features on flow and transport processes (Kollet et al., 2010). The PARFLOW simulations will be carried out using supercomputing infrastructures at the Forschungszentrum Jülich.The study will be carried out in cooperation with other partners in the SOGLO IUAP project. To investigate the impact of landuse on flow path structures and landscape development, two catchments in Brazil were selected by the project partners. The catchment differ in terms of land use with one of the catchments being deforestated andused for agriculture whereas the other is forrested.

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