HAL-BRGM, les publications scientifiques en libre accès du BRGM
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Considérer pleinement les sols dans les processus de planification et d'aménagement: exemples réplicables de Nantes Métropole (France)
No full textInternational audienceSoils are often considered from a risk perspective (pollution, geotechnics) in the urban environment. This is particularly the case in redevelopment projects. Being part of the ecosystem, soils represent also a vital resource. They are indeed a major source of biodiversity (in and above). They also sink carbon, store and infiltrate water, transform organic matter and nutriments. In this frame, in addition to supporting human activities, soils contribute to eg. regulate the climate and the water cycle, support the production of biomass. Planning and (re)development processes must consider soils as resource and as risk, whatever the scale of approach. Several R&D projects on soils used Nantes Metropolis territory as a case study. The objective is to present some results corresponding to knowledge, methods and tools that can serve landscape architects.Excavated soils are a resource. They can be reused, from one site to another or through a valorisation platform. The TERRASS numerical tool helps to put producers and receivers (users) in contacts and to fulfil the traceability obligations. In case of low to moderate contamination, which is frequent in the urban environment, the local geochemical background serves to establish threshold values to facilitate the reuse of excavated materials at district to territorial scale . Soil refunctionalisation or construction can also be operated, using eg. excavated soils and organic waste. This can compensate for the scarcity of topsoil.Soil multifunctionality helps taking the soil resource into account in planning and development. The MUSE method considers four estimated indicators of soil ecological functions (carbon storage, biodiversity storage, infiltration capacity, agronomic value) at territorial scale, and their combination. The maps can be used to preserve some good or patrimonial soils or anticipate soil restoration needs. Methodological adaptations at project (eg Ris-Orangis ) to territorial scale (eg. Rennes Metropolis ) aim to gain precision. Larger sets of indicators and more detailed soil maps allow adapting their use according to their quality, including multifunctionality as well as contamination.Soil pollution risks are linked to different sources. Urban historical inventories map the former industrial and service activities potentially source of soil pollution, and can guide on associated potential pollutants. Anthropogenic deposits may also be a source of pollutants, according to the nature of materials they contain . Further research efforts are in progress within the GSEU and PERMEPOLIS projects. Agriculture, like market gardening, and green spaces maintenance may also have generated diffuse soil contamination with pesticides . That is why, soil contamination risk needs to be checked before any project. In this frame, the high demand for urban gardening appears as a real challenge needing a careful check of soil quality(16).Soils (including resource and risk) are fully considered in the DésiVille17 method to map their potential for desealing, additional development being in progress within the PERMEPOLIS15 project. Other inspiring examples exist at national to EU scale. The webtool in progress (SPADES18) aims to help the users (including landscape architects) to find the right soil concept/methods/tool to answer their needs in the planning, developments and/or design process.</p
Estimation of the potential resources of geothermal Lithium in the Upper Rhine Graben (URG)
No full textInternational audienceAfter the different Research and Development works carried out in Soultz-sous-Forêts (Alsace, France) between 1987 and 2013, which led to the commissioning of one of the world’s first Enhanced Geothermal System (EGS) power plants (1.5 MWe) generating electricity since 2016, several other EGS sites have sprung up in the Upper Rhine Graben (URG), such as Rittershoffen in France, to produce industrial heat, or Insheim and Landau in Germany, to also generate electricity. More projects are planned in the near future. In addition to energy production, the geothermal brines (> 150°C) extracted from the deep boreholes at these sites (≥ 2500 m), with a relatively high salinity of around 100 g/l or more, are very rich in Lithium (Li) and other elements such as Rubidium (Rb), Caesium (Cs), Strontium (Sr), Boron (B) and others. As it happens, Lithium is currently much sought after worldwide, mainly for use in the batteries of the electric vehicles, which must be highly developed in the context of energy transition. This new type of resource therefore makes the exploitation of the URG's EGS sites much more attractive, but it is also important to be able to estimate it. The aim of this work is to provide an initial estimate of potential Lithium resources from the URG's deep geothermal brines. It takes up work carried out by BRGM in 1991 while adapting them to new knowledge and data acquired today. The mean value obtained for the Li concentration of the deep geothermal brines from the updated data is 174±16 mg/l, X=30 (instead of 155 mg/ in 1991). The narrow ranges of value variations for Li concentrations and isotope values in the URG geothermal brines (Delta7Li = 1.45±0.42‰, X=12), as well as for B ([B] = 40±5 mg/l, X=28, and Delta11B = 2.27±0.62‰, X=7) and Rb concentrations (25.1±2.8 mg/l, X=19), suggest that the source and the processes of concentration of these elements are similar. The latter seem to be dominated by interaction processes between the geothermal brines and reservoir rocks reaching a chemical and isotope equilibrium at 225±25°C, in which the dissolution of micas would be the main source of Li, B and Rb in the brines, and the illite that precipitates could be the mineral controlling the Li and B concentrations in solution. Other differences from BRGM's 1991 work mainly concern the surface areas of the considered geothermal zones (2400 km2 instead of 900 km2) and the involved formations. For lack of data, the Munschelkalk formation was not been considered in our work, while the granitic basement has been added, compared with 1991 first estimate. The previous BRGM's work gave estimates ranging from to 300 to 2200 kilotons of Li metal, with an average value of 1000 kilotons of Li metal. In our current work, these values are from 1044 to 15952 kilotons of Li metal, with an average value of 6243 kilotons of Li metal. Despite possible major uncertainties, these results confirm the URG as one of the most promising areas of geothermal Li production in the world, which could be comparable to the Salton Sea geothermal area, in California, USA. These resources are significant compared to the world production of Li in 2022 and 2023 (146 and 180 kilotons of Li, respectively). Within the framework of the EuGeLi project (founded by the EIT Raw Materials), it was estimated that the current and short-term planned geothermal projects of the URG would generate a Li discharge from geothermal water of 4.5 to 6 kilotons/year and could cover around 17% - 21% of the Li required for the need for the development of the European automotive battery manufacturing industry at the 2025 horizon. However, one of the main obstacles holding back the development of the geothermal Li production is the difficulty to estimate the really extractable resources at the scale of each targeted geothermal area and the corresponding risks of failure, as the circulation of the deep brines still remains poorly known and understood in fractured environment. It is clear that the potential resources depend on the different characteristics of each geothermal area (circulation path, fluid flow-rates, reservoir porosity and permeability, fracturation and stress state, reservoir volume and recharge…) and may be relatively variable between areas. It is obvious that additional investigations are required to more precisely assess these different parameters
Surface water and flood-based agricultural systems : mapping and modelling long-term variability in the Senegal river floodplain
No full textInternational audienceIn the alluvial plains of large rivers, annual flooding is essential for numerous ecosystem services, including flood-based agriculture, biodiversity and groundwater recharge. Remote sensing provides increased opportunities to monitor surface water dynamics across large floodplains that are currently poorly captured by local hydrological monitoring and modelling due to data scarcity and the flat, heterogeneous topography. Combining the advances in earth observations with hydrological modelling and extensive in situ fieldwork, this research seeks to improve our understanding of surface water dynamics and associated agricultural practices in the Senegal river floodplain. 2813 mosaics from Landsat, MODIS and Sentinel-2 earth observations are created to map and monitor surface water variations using a site specific MNDWI classification adapted to complex, wetland environments. Validated against ground truth data, the approach is upscaled using cloud computing across this 2250 km 2 floodplain over 1999-2022. Statistical regression models are then developed to estimate flooded and cultivated areas based on upstream flow values since 1950 and analyse trends and exceedance probabilities over time. Results reveal extreme interannual variations in peak flooded areas, ranging from 30,000 ha and 720,000 ha between 1950 and 2022, while annual water modules fluctuate between 210 and 1460 m 3 /s. After 1994, flooded areas show partial recovery, with 95th percentile reaching 89,000 ha during 1994-2022 compared to 37,000 ha in 1972-1993. Flood-based agricultural practices cover between 13,000 ha and 133,000 ha over the same period, highlighting the pronounced variability faced by local rural communities. Occurrence maps and predictive models for annual flooded and cultivated areas based on upstream flows can support early warning tools, helping to prepare for extreme floods and droughts. These outputs are crucial to assess the impact of future climatic and anthropic changes in the region, including planned dams, on the amplitude of annual floods and their associated environmental benefits
Development of a multi-technical dendrochemical approach for the analysis of chemical elements in tree rings on polluted sites
No full textInternational audienceDendrochemistry is a tool for dating the pollution present on an industrial or mining site. This environmental analysis method is based on the absorption of nutrients and certain pollutants by trees, whether by their roots for soil pollution or by their leaves for atmospheric pollution. The GESIPOL ARGOS project, funded by ADEME, was set up to develop this tool. The main objective of this study is to set up a multi-technique approach using several analysis processes that have been developed as part of the ARGOS project. With regard to dating samples, which is difficult for the targeted species (Salicaceae among others), microtomographic analysis of samples has been developed. This makes it possible to obtain very precise images and thus to distinguish annual rings. In addition, the use of AI to automate the dating of the images obtained is currently being developed.In order to obtain the content of major and trace elements present in the annual rings, two analysis techniques have been developed and used: microEDXRF (for Energy Dispersive X-Ray Fluorescence spectrometry) and LA-ICP-MS (for Laser Ablation Inductively Coupled Plasma Mass Spectrometry). These two analytical techniques provide highly sensitive quantitative elemental maps for the elements of interest (K, Ca, Zn, Cd, Cu, Ni, As, etc.). The different chemical elements quantified and the sensitivity of the quantification of these elements depends on the analysis technique used.Finally, in order to process the various data resulting from the analyses (quantification, visualisation, statistical processing, etc.), a programme has been and is still being developed in Python language. To test and validate the dendrochemical analysis method developed, two study sites with known and referenced pollution (metallic, organic) have been and will be sampled as part of this project
Insight into PFOA defluorination using DMSO/NaOH
No full textInternational audiencePer- and polyfluoroalkyl substances, PFAS, are a large group of around 5,000 synthetic chemical compounds widely used in industrial and consumer applications since the 1950s, most usually where extremely low surface energy or surface tension and/or durable water- and oil-repellence is needed, i.e. fire-fighting foams, surface treatment of textiles. Their persistent nature results in diffuse pollution issues in the environment and adverse health effects. To address this issue, the low-temperature thermal treatment process proposed by Trang et al.1 was experimented, using perfluorooctanoic acid (PFOA) as reference analyte with the final goal of proposing a practical solution for addressing perfluorocarboxylic acids environmental contamination. The advanced oxidation of 893 mg/L of PFOA used dimethyl sulfoxide (DMSO), NaOH and H2O. The degradation kinetics was performed over 6 days at 120°C. PFOA defluorination process was investigated by Ultra-high-pressure liquid chromatograph coupled with a mass spectrometer (UPLC-MS), potentiometric titration and SEM-EDS. UPLC-MS analysis was used to quantify PFOA, perfluoroheptanoic acid (PFHPa), perfluorohexanoic acid (PFHxA), perfluoropentanoic acid (PFPeA), perfluorobutanoic acid (PFBA) and qualify by-products of the following masses: 325± 0.5, 307 ± 0.5, 275 ± 0.5, 257 ± 0.5, 229 ± 0.5, 225 ± 0.5, 207 ± 0.5, 175 ± 0.5 and 157 ± 0.5. Fluoride ion specific electrode was used for F- ions quantification. SEM-EDS allowed us to characterize NaF on a dried carbon surface of a 20 µL drop of DMSO/NaOH/H2O after interaction with PFOA.The PFOA removal reaction follows a first order kinetics. Experiments showed the necessity of maintaining at least four times larger the DMSO/H2O volume and sixty times to one higher the NaOH/PFOA molar ratio to ensure a complete PFAS defluorination (≥ 96 % of fluoride) while minimizing by-products. In a partial PFAS defluorination condition, with a NaOH/PFOA molar ratio of 31:1, by-products were identified. PFHpA, PFHxA, PFPeA content decreased below the limit of quantification after 30 minutes; PFBA after 12 hours, while PFPrA, TFA and by-products with masses of 325 ± 0.5, 275 ± 0.5, 229 ± 0.5, 225 ± 0.5 and 175 ± 0.5 were decreasing versus time. In parallel, by-products of the following masses 307 ± 0.5, 257 ± 0.5, 207 ± 0.5 and 157 ± 0.5 were identified as persistent. Three PFAS defluorination pathways were proposed to illustrate the PFAS defluorination mechanism in DMSO/NaOH.Ongoing experiments assess the removal efficiency of perfluorocarboxylic acids from high-concentration PFAAs wastewater, simulating organic fluorine industry effluents
La diffusiophorèse en milieu poreux pour la remédiation des eaux souterraines
No full textInternational audienceA novel approach considers the concentration gradient generated by pollutants to drive particles toward contaminated zones for remediation. This particle transport driven by concentration gradients is known as diffusiophoresis. Diffusiophoresis remains poorly studied in porous media, and existing models are largely limited to simplified cases. A key question is how to incorporate diffusiophoresis into particle transport mechanisms within porous media. We propose a pore-scale model and OpenFOAM simulations to study particle movement in porous media under solute concentration gradients. This work improves our understanding of how to model diffusiophoresis in porous media.Une nouvelle approche consiste à prendre en compte le gradient de concentration généré par les polluants pour conduire les particules vers les zones contaminées afin de les dépolluer. Ce transport de particules entraîné par des gradients de concentration est connu sous le nom de diffusiophorèse. La diffusiophoresis reste peu étudiée dans les milieux poreux et les modèles existants se limitent en grande partie à des cas simplifiés. Une question clé est de savoir comment incorporer la diffusiophorèse dans les mécanismes de transport de particules dans les milieux poreux. Nous proposons un modèle à l'échelle du pore et des simulations OpenFOAM pour étudier le mouvement des particules dans les milieux poreux sous des gradients de concentration de solutés. Ce travail améliore notre compréhension de la modélisation de la diffusiophorèse dans les milieux poreux
Evolution of microclimate following small patch de-sealing and revegetation in urban context
No full textInternational audienceThis research explores the impact of de-sealing a 64 m 2 patch (Oasis) after introducing indigenous vegetation, four trees and herbaceous species, on local microclimate conditions in an urban context. Over two years, microclimate variables were monitored and thermal comfort was evaluated with the Universal Thermal Climate Index (UTCI). The Oasis experienced a noticeable reduction in Ts compared to surrounding asphalted areas, with a daily mean difference in maximum surface temperatures of 18.4 • C the first summer and 23.0 • C the second, attributed to the development of the herbaceous layer, which covered 60 % of the Oasis three months after sowing and above 90 % from the second year. This Ts is partly responsible for improving thermal comfort during the day. Tree shading induced further local cooling, with a decrease of up to 8 • C of UTCI in shaded areas. No evidence of lower nocturnal Ta compared to sealed reference was shown, possibly due to the small size of the patch and air mixing with the surroundings. However, as the low vegetation grew, the nocturnal de-sealed patch Ta got closer to mature meadow values. Modeling using UMEP and SOLWEIG projected further improvements in thermal comfort in the coming years during the daytime considering future tree dimensions. During nighttime, the trees would induce a significant radiative trapping. This study demonstrates that small-scale de-sealing and revegetation projects can provide meaningful microclimate improvements in urban environment as early as the first year. In the following years, the development of vegetation further helps cities to adapt to climate change
Assessment of soil heavy metal pollution: a case study of the abandoned mine of Ichmoul, Algeria
No full textInternational audienceThis study aimed to assess and evaluate heavy metal contamination in the soil and sediment surrounding the Ichemoul lead mine northeast of Algeria. Soil and sediment samples were analyzed to determine the pH, particle size, organic matter (OM) content, and heavy metal (HM) concentration. The total HM concentration was determined by digestion in a mixture of strong acids. Flame atomic absorption spectrophotometry (FAAS) was used to determine the copper (Cu), lead (Pb), and zinc (Zn) contents in the obtained solutions. Major elements were analyzed by X-ray fluorescence spectrometry (XRF). X-ray diffraction (XRD) was used to determine the mineralogy of processing tailings, lead concentrates inside the abandoned plant, and the soil surrounding the mine. The potential environmental contamination was assessed by comparing the concentrations of Cu, Zn, and Pb with the geochemical background and using the following pollution indices: enrichment factor (EF), geoaccumulation index (Igeo), and Nemerow pollution index (NPI). Most soil samples had Cu, Pb, and Zn concentrations significantly exceeding local and regional background values. Spearman correlation, variance coefficient (VC), and HM spatial distribution suggested anthropic contamination in this area due to the storage of ore-rich sulfide minerals and ore extraction and processing. The EF showed that the soil was significantly enriched in Pb. The Igeo and NPI showed that the soil near the old abandoned plant was severely contaminated. The mineralogical and chemical composition of the concentrate showed the presence of galena, anglesite, barite, and chalcopyrite, with 78% of the lead as a product of the flotation processes prevalent at that time. Its presence under weathering processes has contributed significantly to the soil contamination surrounding the treatment plant with heavy metals, especially Pb. The chemical composition of ore processing waste indicated a deficiency in heavy metals, so it does not provide an environmental risk. The spatial interpolation results of the HMs indicate that high concentrations of these elements are closer to sources of contamination. The hotspots with high HMs concentrations are limited and localized due to the carbonate environment, neutral to alkaline pH, and fine soil fraction
Prediction of Induced Seismicity: a Machine Learning approach
No full textInternational audienceIt is well known that earthquakes can be induced by the injection of fluids into the ground, during operations such as waste water disposal or the extraction of geothermal resources. In this work, we investigate the correlation between induced seismicity and the fluid injection parameters (flow rate, pressure, etc...). We use a machine learning approach, which is particularly well suited to processing large volumes of data and extracting thecomplex link between injection parameters and seismicity. By training our model on time series characterising injection parameters and seismicity, we show that it is possible to estimate the number of earthquakes occurring in the future, on a fixed time scale.We propose to focus on two main applications having caused induced seismicity at different spatial and temporal scales. First, we estimate the future number of induced earthquakes in central Oklahoma (United States), which has been subject to extensive waste water disposal since 2010. Analysis of data shows that seismicity rate and injected volums are well correlated with a delay of 9 months approximately. Secondly, we focus on the seismicity during the stimulation phase of the Soultz-SousForêts (France) enhanced geothermal system between 2000 and 2010. The aim here is to test whether training the model over one stimulation period can help predict the seismicity rate for another stimulation cycle. Finally, this work paves the way for another future application: real-time prediction of induced seismicity during continuous fluid injection
A Novel Machine Learning-based Method for Groundwater Modelling involving Aquifer Rainfall Time Response Analysis and Clustering of Groundwater Wells
No full textInternational audienceClustering of groundwater level data is crucial for water resource management, as it increases the efficiency of models in distinctly predicting specific hydrogeological patterns in aquifer systems. Traditional methods mostly rely on spatial or time series distance metrics, neglecting the impact of external inputs (rainfall, evapotranspiration, etc.) on aquifer systems. This study introduces an innovative machine learning-based approach to model aquifer systems at the piezometer level. While our flexible methodology accommodates any model and input, we selected a random forest model for its lightweight nature and interpretability. This model-based technique enables the clustering of similar aquifers based on model parameters. By leveraging the decision trees feature importances, we derive the rainfall response time distribution of the aquifer at the piezometer level, facilitating a quantitative analysis of the local aquifer dynamics. Additionally, we demonstrate that, by selecting analogous distributions using a simple similarity measure, the predictive performance of groundwater level global forecasting models is significantly enhanced