Advanced granulometric characterization of shredded waste printed circuit boards for sampling.

Whether it be to measure their value before a trade, to calculate yields and optimize the recycling process or to check for the presence of harmful substances, Waste Electronic and Electric Equipments (WEEE) need to be characterized. Sampling can give an accurate assessment of the grade of a batch of WEEE, but quantifying the uncertainty around this estimate can be delicate. Pierre Gy's sampling theory of particulate matter studies how the latter is affected by the physical and chemical properties of the studied objects. However, its application requires a deep understanding of the correlations existing between their size, shape, volume, density, mass and grade, which are still unclear for WEEE fragments. Although average information is typically available on batches of WEEE, a more detailed description would be necessary to gain insight into such relationships. To start filling the gap, this paper focuses on the fine characterization of two different batches of waste printed circuit boards, crushed into pieces of about 10 mm. One by one, over 5,000 fragments were sampled, photographed and analyzed. Their individual mass, density, volume, thickness, surface, width and length were all measured separately. Based on their appearance, they were also sorted into four heuristic categories: plastic, metal, circuit boards and electronic components. Descriptive statistics of this novel granulometric database are shown here, throwing light on the unique correlations between the studied parameters and exhibiting a peculiar mass-size law. They point to new avenues on how to adapt Gy's sampling model to WEEE.

Discussion on "Competition on Spatial Statistics for Large Datasets".

We discuss the methods and results of the RESSTE team in the competition on spatial statistics for large datasets. In the first sub-competition, we implemented block approaches both for the estimation of the covariance parameters and for prediction using ordinary kriging. In the second sub-competition, a two-stage procedure was adopted. In the first stage, the marginal distribution is estimated neglecting spatial dependence, either according to the flexible Tuckey g and h distribution or nonparametrically. In the second stage, estimation of the covariance parameters and prediction are performed using Kriging. Vecchias's approximation implemented in the GpGp package proved to be very efficient. We then make some propositions for future competitions. SUPPLEMENTARY INFORMATION: The online version supplementary material available at 10.1007/s13253-021-00462-2.

Oxygen data assimilation for estimating micro-organism communities' parameters in river systems.

The coupling of high frequency data of water quality with physically based models of river systems is of great interest for the management of urban socio-ecosystems. One approach to exploit high frequency data is data assimilation which has received an increasing attention in the field of hydrology, but not for water quality modeling so far. We present here a first implementation of a particle filtering algorithm into a community-centered hydro-biogeochemical model to assimilate high frequency dissolved oxygen data and to estimate metabolism parameters in the Seine River system. The procedure is designed based on the results of a former sensitivity analysis of the model (Wang et al., 2018) that allows for the identification of the twelve most sensible parameters all over the year. Those parameters are both physical and related to micro-organisms (reaeration coefficient, photosynthetic parameters, growth rates, respiration rates and optimal temperature). The performances of the approach are assessed on a synthetic case study that mimics 66 km of the Seine River. Virtual dissolved oxygen data are generated using time varying parameters. This paper aims at retrieving the predefined parameters by assimilating those data. The simulated dissolved oxygen concentrations match the reference concentrations. The identification of the parameters depends on the hydrological and trophic contexts and more surprisingly on the thermal state of the river. The physical, bacterial and phytoplanktonic parameters can be retrieved properly, leading to the differentiation of two successive algal blooms by comparing the estimated posterior distribution of the optimal temperature for phytoplankton growth. Finally, photosynthetic parameters' distributions following circadian cycles during algal blooms are discussed.

Time-dependent global sensitivity analysis of the C-RIVE biogeochemical model in contrasted hydrological and trophic contexts.

Dissolved oxygen within water column is a key variable to characterize the water quality. Water quality modeling has been extensively developed for decades. However, complex biogeochemical cycles are described using a high number of parameters. Hence, parameters' uncertainty constitutes a major problem in the application of these models. Sensitivity analysis allows the identification of the most influential parameters in a model and a better understanding of the governing processes. This paper presents a time-dependent sensitivity analysis for dissolved oxygen using Morris and Sobol methods combined with a functional principal components analysis for dimension reduction. The aim of this study is to identify the most important parameters of C-RIVE model in different trophic contexts and to understand the biogeochemical functioning of river systems. The results indicate that the maintenance respiration of phytoplankton and the photosynthetic parameters (i.e. photosynthetic capacity, the maximal photosynthesis rate and light extinction coefficients) are the most influential parameters during algal blooms. When the river system becomes heterotrophic, the bacterial activities (moderate and high temperature) and the reaeration coefficients (low temperature) affect the most the dissolved oxygen concentration in the water column. An anthropogenic effect (ship navigation) on variation of dissolved oxygen concentration has been identified and the role of this anthropogenic effect evolves with hydrological and trophic conditions.