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Laboratory-based diffraction contrast tomography (LabDCT) is a novel technique used to resolve grain orientations and shapes in three dimensions at the micrometre scale using laboratory X-ray sources, allowing the user to overcome the constraint of limited access to synchrotron facilities. To foster the development of this technique, the implementation of LabDCT is illustrated in detail using a conventional laboratory-based X-ray tomography setup, and it is shown that such implementation is possible with the two most common types of detectors: CCD and flat panel. As a benchmark, LabDCT projections were acquired on an AlCu alloy sample using the two types of detectors at different exposure times. Grain maps were subsequently reconstructed using the open-source grain reconstruction method reported in the authors' previous work. To characterize the detection limit and the spatial resolution for the current implementation, the reconstructed LabDCT grain maps were compared with the map obtained from a synchrotron measurement, which is considered as ground truth. The results show that the final grain maps from measurements by the CCD and flat panel detector are similar and show comparable quality, while the CCD gives a much better contrast-to-noise ratio than the flat panel. The analysis of the grain maps reconstructed from measurements with different exposure times suggests that a grain map of comparable quality could be obtained in less than 1 h total acquisition time without a significant loss of grain reconstruction quality and indicates a clear potential for time-lapse LabDCT experiments. The current implementation is suggested to promote the generic use of the LabDCT technique for grain mapping on conventional tomography setups.

X-ray-based non-destructive 3D grain mapping techniques are well established at synchrotron facilities. To facilitate everyday access to grain mapping instruments, laboratory diffraction contrast tomography (LabDCT), using a laboratory-based conical polychromatic X-ray beam, has been developed and commercialized. Yet the currently available LabDCT grain reconstruction methods are either ill-suited for handling a large number of grains or require a commercial licence bound to a specific instrument. To promote the availability of LabDCT, grain reconstruction methods have been developed with multiple reconstruction algorithms based on both forward and back calculations. The different algorithms are presented in detail and their efficient implementation using parallel computing is described. The performance of different reconstruction methods is assessed on synthetic data. The code to implement all the described algorithms has been made publicly accessible with the intention of fostering the development of grain mapping techniques on widely available laboratory instruments.

Soils in the Gulf of Lions region (NW Mediterranean) show some of the highest copper (Cu) levels in Europe (Ballabio et al, 2018). The episodic and erosive nature of rainfall in the Mediterranean area (González-Hidalgo et al, 2007), historical intensive agricultural practices using Cu to fight the downy mildew and its relatively high solubility make the study of its transfer and consequences along the land-sea continuum a major issue. The main objective is to quantify the Cu fluxes from coastal rivers discharging in the Gulf of Lions and characterise their temporal variability highlighting the importance of floods on the transfer processes of matter and associated contaminants in the Mediterranean region.Since 2006, the SNO MOOSE, a multi-platform and multi-site observation network designed to monitor the evolution of the Mediterranean basin in a context of global change, has been carrying out. Therefore, on a monthly monitoring basis, trace metal concentrations in suspended particulate matter (SPM) have been estimated in the five main small coastal rivers of the Gulf of Lions as well as the Rhone River.These observations coupled with a sediment flux model (Sadaoui et al, 2016) allow the estimation of elemental fluxes. Small rivers have the highest average Cu content in suspended matter (80.3 µg/g) and in the soils of the catchment areas (79.7 µg/g) (approximatively a factor of 2 compared with the Rhone). Mean annual estimation of Cu fluxes are about 316T/year with an interannual variability of 36%. The Rhone River is by far the major contributor to the fluvial exports of particulate copper to the Gulf of Lion. However, although small coastal rivers account only for 6% of SPM inputs, their contribution to particulate Cu fluxes averages 9.7%.Interannual variability of fluxes is controlled by the occurrence of episodic flash floods on coastal rivers typical of the functioning of Mediterranean watercourses (Roussiez et al, 2011, 2012).The SPM transport originating from surface soil erosion and associated copper mainly takes place during these brief events for small coastal rivers (annual average of 66% of total Cu export against 19% for the Rhone). According to the number of events occurring each year their relative contribution of Cu fluxes compared to the Rhone is highly variable (from 2 % in 2012 up to 41% in 2011). Thus, the influence of coastal rivers on the global Cu budget to the Gulf of Lions is not negligible.Moreover, copper transferred in rivers from the erosion of wine-growing soils is mainly in extractable form, which is more hazardous for the environment because it is both mobile and potentially assimilable by organisms.

The Maghreb countries located in North Africa are strongly impacted by floods, causing extended damage and numerous deaths. Up to now, the lack of accessibility of river discharge data prevented regional studies on potential changes in flood hazards or the development of regional flood frequency estimation methods. A database of daily river discharge data from 55 basins located in Algeria, Morocco and Tunisia, has been recently compiled, with on average 32 years of complete records over the time period 1970-2017. A peaks-over-threshold sampling of flood events is considered, first to detect trends on the annual frequency and the magnitude of floods. The trend analysis results indicated no significant changes in flood frequency nor magnitude, with only a few spurious trends detected in cases of isolated extreme or clustered events. Then, two regional estimation methods for flood quantiles were compared, based either on spatial proximity or catchment characteristics. The regional estimation from multiple catchment characteristics (including soil types, land use, elevation, geology, extracted from global databases) was performed comparing two multiple linear regression methods, Stepwise regression and Lasso regression, and two machine learning algorithms, Random Forests and Support Vector Machines. Results indicate a better performance of the regional estimation of flood quantiles with catchments characteristics than with spatial proximity, with a mean absolute error in cross-validation close to 40%. These encouraging results open the perspective of operational applications of these methods, in particular by increasing the number of basins considered.

Plastic waste and its fragments (microplastics