Black adzes in the Early Neolithic of Belgium: Contribution of the Raman microspectrometry and petrography in characterization and sourcing

Early Neolithic (Linear Pottery Culture) adzes originate from settlements and workshops accompany the neolithization of Belgium. They are made from a wide range of extraregional lithic raw materials such as metamorphic green rocks (amphibolite) and black volcanic rocks (“basalt’) beside more local or regional raw material as flints, light-coloured (sedimentary and lightly metamorphic) quartzites, black lydites (Cambrian nodular phtanite of Céroux-Mousty and Lower Namurian banded phtanites) and dark grey Lower Namurian silicified sandstones previously called “Micaceous sandstones of Horion-Hozémont’. The discovery of the workshop of Noirfontaine near the city of Liège in the 1970s and 1980s provides exceptional assemblage available for updating analytical studies. This research focuses on the multi-scale characterization, the discrimination and sourcing both Cambrian and Namurian black sedimentary rocks rich in secondary silica composing Early Neolithic adzes found in Belgium. Their black colour results from finely dispersed organic matter, but the absence of palynomorphs does not allow a biostratigraphic ascription. Additional petrographical analyses (Optical Petrography, Scanning Electron Microscope), X-ray diffraction, chemical analyses (Energy Dispersive Spectroscopy) and measuring the degree of graphitization of the organic matter through Raman microspectrometry have been decisive in identifying the geological and geographical provenances by comparing the acquired results with geological reference samples collected in the field or through reference collections. Cambrian lydites are coming from a very restricted area and were preferred to other more local rock sources

Caractérisation archéométrique et archéologique de la production briquetière de la Région de Bruxelles-Capitale entre le xive siècle et le troisième quart du xviiie siècle (Belgique)

peer reviewedBrick samples from different archaeological sites represent mostly houses in Brussels (Belgium) built between the end of the Middle ages (end of 13th-beginning 14th centuries) and the end of the Modern Period (18th century). The study gives a mineralogical–petrographical–chemical characterization of the brick samples made in the Brussels-Region and sources the raw silty material, the moulding sand and the lime-mortar. Optical microscopy, X-ray powder diffractometry, Laser Ablation Inductively Coupled Plasma Mass Spectrometry, magnetic susceptibility and scanning electron microscopic with an energy dispersive X-ray attachment were applied both to fired clay bricks and regional clayey materials. Bricks were moulded with a silica rich, non-calcareous loam gathered locally in the alluvial plain of the Senne valley. Material from gleysols and fluvisols were mined separately to shape two types of bricks. A strong chemical resemblance with the thick loessic deposits of the Belgian plateaus results from erosion and river transport and sedimentation in the wide alluvial plain of Brussels. Petrography and geochemistry show minor participation of marine Lower Palaeozoic, Cretaceous and Tertiary rocks from the alimentation area of the Senne. Although clayey tertiary layers outcrop in the Brussels Region and are cut by the numerous tributaries of the Senne valley, they were never exploited for brick making in the Brussels Capital Region. Mineralogical composition and petrography suggest the absence of mixing with river sand or local marine tertiary sands. Local sediments extracted in the valley sides as Eocene fossiliferous fine sand were used as moulding sandLes briques étudiées proviennent majoritairement d’habitations construites à Bruxelles (Belgique) entre la fin du Moyen Âge (fin xiiie-début xive siècle) et la fin des temps Modernes (xviiie siècle). L’étude fournit une caractérisation minéralogique, pétrographique et chimique des briques faites dans la Région de Bruxelles Capitale et discute des sources des matières premières silteuses, les sables de moulage et les mortiers de chaux. La microscopie optique, la diffraction des rayons X, l’ablation laser associée à la spectrométrie de masse à couplage inductif, la susceptibilité magnétique et la microscopie électronique à balayage couplée aux analyses par analyse dispersive en énergie, furent utilisées pour caractériser les briques et différentes ressources argileuses régionales. Les briques ont été moulées avec une matière première silteuse, riche en silice, non calcareuse et récoltée localement dans la plaine alluviale de la vallée de la Senne. Des gleys et des fluvisols ont été extraits séparément pour la préparation de deux types de briques. La forte ressemblance chimiqueavec les épais dépôts loessiques de plateaux résulte de l’érosion de ces derniers et de leur transport par la rivière suivi de la sédimentation dans la large plaine alluviale de Bruxelles. La pétrographie et la géochimie montrent une participation mineure de roches sédimentaires du Paléozoïque inférieur, du Crétacé et du Tertiaire issues du bassin d’alimentation de la Senne. Bien que les couches de sédiments meubles argileux du Tertiaire affleurant en Région Bruxelloise soient recoupées par les nombreux affluents de la Senne, elles n’ont jamais été utilisées pour la production de briques dans la Région de Bruxelles-Capitale. La composition minéralogique et la pétrographie suggèrent l’absence d’ajout de sables de rivières ou de sables marins tertiaires locaux. Des sables fossilifères fins d’âge Eocène sont utilisés comme sable de moulage

Spring Water Geochemistry: A Geothermal Exploration Tool in the Rhenohercynian Fold-and-Thrust Belt in Belgium

peer reviewedSpring water geochemistry is applied here to evaluate the geothermal potential in Rhenohercynian fold and thrust belt around the deepest borehole in Belgium (Havelange borehole: 5648 m MD). Fifty springs and (few) wells around Havelange borehole were chosen according to a multicriteria approach including the hydrothermal source of “Chaudfontaine” (T ≈ 36 ◦C) taken as a reference for the area. The waters sampled, except Chaudfontaine present an in-situ T range of 3.66–14.04 ◦C (mean 9.83 ◦C) and a TDS (dry residue) salinity range of 46–498 mg/L. The processing methods applied to the results are: hierarchical clustering, Piper and Stiff diagrams, TIS, heat map, boxplots, and geothermometry. Seven clusters are found and allow us to define three main water types. The first type, locally called “pouhon”, is rich in Fe and Mn. The second type contains an interesting concentration of the geothermal indicators: Li, Sr, Rb. Chaudfontaine and Moressée (≈5 km East from the borehole) belong to this group. This last locality is identified as a geothermal target for further investigations. The third group represents superficial waters with frequently high NO3 concentration. The application of conventional geothermometers in this context indicates very different reservoir temperatures. The field of applications of these geothermometers need to be review in these geological conditions.MEE

Global, local and focused geographic clustering for case-control data with residential histories

BACKGROUND: This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile. METHODS: Local, global and focused tests for residential histories are developed based on sets of matrices of nearest neighbor relationships that reflect the changing topology of cases and controls. Exposure traces are defined that account for the latency between exposure and disease manifestation, and that use exposure windows whose duration may vary. Several of the methods so derived are applied to evaluate clustering of residential histories in a case-control study of bladder cancer in south eastern Michigan. These data are still being collected and the analysis is conducted for demonstration purposes only. RESULTS: Statistically significant clustering of residential histories of cases was found but is likely due to delayed reporting of cases by one of the hospitals participating in the study. CONCLUSION: Data with residential histories are preferable when causative exposures and disease latencies occur on a long enough time span that human mobility matters. To analyze such data, methods are needed that take residential histories into account

Uncertainty quantification of medium-term heat storage from short-term geophysical experiments using Bayesian Evidential Learning

In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non-favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre- and post-field data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements. Plain Language Summary : Geothermal energy can be extracted or stored in shallow aquifers through systems called aquifer thermal energy storage (ATES). In practice, the energy efficiency of those systems is often lower than expected because of the uncertainty related to the subsurface. To assess the uncertainty, a common method in the scientific community is to generate multiple models of the subsurface fitting the available data, a process called stochastic inversion. However this process is time consuming and difficult to apply in practice for real systems. In this contribution, we develop a novel approach to avoid the inversion process called Bayesian Evidential Learning. We are still using many models of the subsurface, but we do not try to fit the available data. Instead, we use the model to learn a direct relationship between the data and the response of interest to the user. For ATES systems, this response corresponds to the energy extracted from the system. It allows to predict the amount of energy extracted with a quantification of the uncertainty. This framework makes uncertainty assessment easier and faster, a prerequisite for robust risk analysis and decision making. We demonstrate the method in a feasibility study of ATES design

Spatial variation and socio-economic determinants of Plasmodium falciparum infection in northeastern Tanzania

<p>Abstract</p> <p>Background</p> <p>Malaria due to <it>Plasmodium falciparum </it>is the leading cause of morbidity and mortality in Tanzania. According to health statistics, malaria accounts for about 30% and 15% of hospital admissions and deaths, respectively. The risk of <it>P. falciparum </it>infection varies across the country. This study describes the spatial variation and socio-economic determinants of <it>P. falciparum </it>infection in northeastern Tanzania.</p> <p>Methods</p> <p>The study was conducted in 14 villages located in highland, lowland and urban areas of Korogwe district. Four cross-sectional malaria surveys involving individuals aged 0-19 years were conducted during short (Nov-Dec) and long (May-Jun) rainy seasons from November 2005 to June 2007. Household socio-economic status (SES) data were collected between Jan-April 2006 and household's geographical positions were collected using hand-held geographical positioning system (GPS) unit. The effects of risk factors were determined using generalized estimating equation and spatial risk of <it>P. falciparum </it>infection was modelled using a kernel (non-parametric) method.</p> <p>Results</p> <p>There was a significant spatial variation of <it>P. falciparum </it>infection, and urban areas were at lower risk. Adjusting for covariates, high risk of <it>P. falciparum </it>infection was identified in rural areas of lowland and highland. Bed net coverage levels were independently associated with reduced risk of <it>P. falciparum </it>by 19.1% (95%CI: 8.9-28.2, p < 0.001) and by 39.3% (95%CI: 28.9-48.2, p < 0.001) in households with low and high coverage, respectively, compared to those without bed nets. Households with moderate and lower SES had risk of infection higher than 60% compared to those with higher SES; while inhabitants of houses built of mud walls were at 15.5% (95%CI: 0.1 - 33.3, p < 0.048) higher risk compared to those living in houses built by bricks. Individuals in houses with thatched roof had an excess risk of 17.3% (95%CI: 4.1 - 32.2, p < 0.009) compared to those living in houses roofed with iron sheet.</p> <p>Conclusions</p> <p>There was high spatial variation of risk of <it>P. falciparum </it>infection and urban area was at the lowest risk. High bed net coverage, better SES and good housing were among the important risk factors associated with low risk of <it>P. falciparum </it>infection.</p

Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom‐up control hypothesis using high‐resolution topographic data

The depth to bedrock controls a myriad of processes by influencing subsurface flow paths, erosion rates, soil moisture, and water uptake by plant roots. As hillslope interiors are very difficult and costly to illuminate and access, the topography of the bedrock surface is largely unknown. This essay is concerned with the prediction of spatial patterns in the depth to bedrock (DTB) using high‐resolution topographic data, numerical modeling, and Bayesian analysis. Our DTB model builds on the bottom‐up control on fresh‐bedrock topography hypothesis of Rempe and Dietrich (2014) and includes a mass movement and bedrock‐valley morphology term to extent the usefulness and general applicability of the model. We reconcile the DTB model with field observations using Bayesian analysis with the DREAM algorithm. We investigate explicitly the benefits of using spatially distributed parameter values to account implicitly, and in a relatively simple way, for rock mass heterogeneities that are very difficult, if not impossible, to characterize adequately in the field. We illustrate our method using an artificial data set of bedrock depth observations and then evaluate our DTB model with real‐world data collected at the Papagaio river basin in Rio de Janeiro, Brazil. Our results demonstrate that the DTB model predicts accurately the observed bedrock depth data. The posterior mean DTB simulation is shown to be in good agreement with the measured data. The posterior prediction uncertainty of the DTB model can be propagated forward through hydromechanical models to derive probabilistic estimates of factors of safety.Key Points:We introduce an analytic formulation for the spatial distribution of the bedrock depthBayesian analysis reconciles our model with field data and quantifies prediction and parameter uncertaintyThe use of a distributed parameterization recognizes geologic heterogeneitiesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137555/1/wrcr22005.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137555/2/wrcr22005_am.pd