Mass flow of nutrients from innumerous latrines and septic tanks was assessed to best describe the groundwater quality situation in the urban environment of Yaounde. 37 groundwater samples were taken at the end of dry season 2012 and analysed for nutrient related (NO3(-), NH4(+), NO2(-), K(+), Cl(-), HPO4(2-) and TOC) and physico-chemical ambient parameters. A survey on waste water discharge close to water points constrained point sources from sanitation. The results showed that the median of nitrate concentration exceeds the WHO limit. We realized that EC increases from the geogenic background to very high levels in the urban area within short distance, suggesting anthropogenic input. Dug wells showed nitrate and ammonium in equivalent concentrations, indicating incomplete nitrification and mandating their inclusion into water type classification. The mass turnover of nutrients in urban groundwater scales high in comparison to national statistical figures on fertilizer import for 2012. A mass N,K balance for infiltration water overestimates observed concentrations by a factor of 4.5. The marked balance gap is attributed to dynamic non-equilibrium between input and output. Unresolved questions like a) urban sanitation, b) hygiene & health and c) environmental protection urgently call for closing the nutrient cycle. In the light of Cameroonian strategies on rural development, tackling the groundwater nutrient, urban agriculture, food--NEXUS might partially restore urban and periurban ecosystem services under economical constraints and thus improve living conditions.

In this study, we investigated the distribution and the principal sources of macronutrients (N, P, K, S) in groundwater in agro-pastorally dominated North Cameroon. Graphical methods and multivariate statistics were used to understand the impact of land use and regional geology on nutrient concentrations (K⁺, NO₃⁻, SO₄²⁻, PO₄³⁻). Groundwater was acidic (median pH 6.8) and more mineralized (EC range = 35–2500 µS/cm, median = 376 µS/cm) than surface water (EC range = 50–222 µS/cm, median = 81 µS/cm). The median concentration of nutrients was (in mg/L) in the order NO₃⁻ (41.3) > SO₄²⁻ (7.7) > K⁺ (5.2) > PO₄³⁻ (0.1) in groundwater and rather K⁺ (3.3) > NO₃⁻ (0.3) > SO₄²⁻ (0.2) > PO₄³⁻ (0.2) in surface water. Using hierarchical cluster analysis and Schoeller diagram, we identified three distinct water quality groups. Group I, which contained cluster 1 and 2 were generally less mineralized and characterised as Na + K − HCO₃ water type found in the urban area such as Garoua and Lagdo. Group II, containing cluster 3 was much higher mineralized and characterised as the Na + K − HCO₃ water type with elevated nitrate. The occurrence of these groups is linked to the outcrops of cretaceous terrestrial sediments of the Garoua basin. Group III, which contained cluster 4 were heavily mineralized and characterised as the Ca + Mg − NO₃ water type. The occurrence was controlled by the outcrop of the crystalline Proterozoic basement. The nitrate median (41 mg/L) found in groundwater demonstrates the influence exerted by anthropogenic activities. Management strategies are required for the whole study area to limit further deterioration of groundwater resources in the wake of the planned expansion of agricultural productivity in the North Region of Cameroon.

State-of-the-art hydrogeological investigations use transient calibrated numerical flow and transport models for multiple scenario analyses. However, the transient calibration of numerical flow and transport models still requires consistent long-term groundwater time series, which are often not available or contain data gaps, thus reducing the robustness and confidence of the numerical model. This study presents a data-driven approach for the reconstruction and prediction of gaps in a discontinuous groundwater level time series at a monitoring station in the Allertal (Saxony-Anhalt, Germany). Deep Learning and classical machine learning (ML) approaches (artificial neural networks (TensorFlow, PyTorch), the ensemble method (Random Forest), boosting method (eXtreme gradient boosting (XGBoost)), and Multiple Linear Regression) are used. Precipitation and groundwater level time series from two neighboring monitoring stations serve as input data for the prediction and reconstruction. A comparative analysis shows that the input data from one measuring station enable the reconstruction and prediction of the missing groundwater levels with good to satisfactory accuracy. Due to a higher correlation between this station and the station to be predicted, its input data lead to better adapted models than those of the second station. If the time series of the second station are used as model inputs, the results show slightly lower correlations for training, testing and, prediction. All machine learning models show a similar qualitative behavior with lower fluctuations during the hydrological summer months. The successfully reconstructed and predicted time series can be used for transient calibration of numerical flow and transport models in the Allertal (e.g., for the overlying rocks of the Morsleben Nuclear Waste Repository). This could lead to greater acceptance, reliability, and confidence in further numerical studies, potentially addressing the influence of the overburden acting as a barrier to radioactive substances.

Representative preliminary safety analyses in the site selection procedure for high-level radioactive waste repositories or the subsequent safety assessments require the analysis of the transport regime of radionuclides in e.g. the geological and geotechnical barrier. This can be done (i) using sophisticated, but time consuming numerical models on unstructured grids representing complex geological systems or (ii) using more efficient simple models such as analytical, process- or grid-simplified numerical models that are e.g. calculating in a lower-dimensional space, or simple equations based on e.g. dimensionless numbers.The present study employs an analytical model of the solute transport equation with linear sorption and decay and a numerical simulator in order to reproduce physical through-diffusion experiments. The same set of transport parameters are used to predict temporal and spatial scales of radionuclide diffusion in low-permeability porous media. The results are then used to develop surrogate models for the estimation of timescales and maximum breakthrough distances of radionuclides. We demonstrate that an expression based on the 2nd Damköhler number can be used for the calculation of the maximum breakthrough distance for the non-sorbing radionuclides. This expression is calculated using the effective diffusion coefficient, the diffusion-effective porosity, the physical half-life and a dimensionless number. Further, we show that the timescale for reaching the maximum breakthrough distance can be roughly estimated and is two orders of magnitude larger than the physical half-life.

Understanding the movement of radionuclides over time is crucial for assessing the integrity of geological formations as barrier for a radionuclide waste repository. Long-term groundwater potential time series enable the modelling of flow and transport scenarios, which help to predict how radionuclides may migrate from the repository through the overburden into the biosphere, if the overburden as geological barrier should fail. The accuracy of numerical flow and transport models depend on the availability of reliable input data, such that long-term groundwater potential time series help to ensure that numerical flow and transport scenarios accurately represent the complex hydrogeological processes occurring over time.However, in practice it is very common that, due to financial constraints, vandalism of measurement devices, and other logistical problems result in shorter and/or longer gaps in the ideally continuous groundwater monitoring time series. These gaps can significantly hinder the reliability and completeness of the dataset, making it challenging to perform accurate analyses.In response to these challenges, we use machine-learning methods with monthly precipitation data from the German meteorological service (DWD), monthly groundwater recharge data generated from the hydrological model RUBINFLUX and continuous groundwater time series from state run monitoring wells as inputs to predict the missing gaps in the groundwater potential time series in the overburden of the radioactive waste repository Morsleben (ERAM).This approach highlights the importance of continuity in the dataset for further studies, modelling, and safety assessments for radioactive waste repositories. Using machine learning techniques can help to reconstruct the missing data and provide a more comprehensive and continuous dataset for validating and calibrating numerical flow and transport models. References:Bear, J., 1972. Dynamics of Fluids in Porous Media. American Elsevier, New York.Langkutsch, U., Käbel, H., Margane, A., & Schwamm, G. (1998). Planfeststellungsverfahren zur Stillegung des Endlagers für radioaktive Abfälle Morsleben. 457. Peche, A., Kringel, R., Orilski, J., & Skiba, P. (2021). Hydrogeologische Modellbildung des ERA Morsleben. In Zwischenbericht Bundesanstalt für Geowissenschaften und Rohstoffe (BGR) im Auftrag der Bundesgesellschaft für Endlagerung (BGE).Hölting, B., & Coldewey, W. G. (2013). Hydrogeologie. In Hydrogeologie. Spektrum Akademischer Verlag. https://doi.org/10.1007/978-3-8274-2354-2Zepp, H., König, C., Kranl, J., Becker, M., Werth, B., & Rathje, M. (2017). Implizite Berechnung der Grundwasserneubildung (RUBINFLUX) im instationären Grundwasserströmungsmodell SPRING. Eine neue Methodik für regionale, räumlich hochaufgelöste Anwendungen. Grundwasser, 22(2), 113–126. https://doi.org/10.1007/S00767-017-0354-3

Background: Stable water isotopes $$\delta ^{18}$$ δ 18 O and $$\delta ^2$$ δ 2 H in the water molecule are ideal tracers of the hydrological cycle to understand mixing processes between different aquifers, groundwater-surface water interactions or groundwater recharge. To date, no centralized access to standardized groundwater isotope data is available in Europe. This study addresses this gap in Germany by assembling existing historical data, completing the spatial coverage with new samplings and presenting the gained data in the form of spatially interpolated maps, so-called isoscapes. Results: Collected data from more than 8 000 groundwater sampling stations cover the entire German territory and span the last 50 years. We successfully implemented co-kriging interpolation algorithms taking into account elevation and temperature. This produced several isoscapes for 15 years periods to accurately represent typical young groundwater residence times. Here we present two representative $$\delta ^{18}$$ δ 18 O isoscapes for the periods 1965-1980 and 2015-present. These examples allow assessments of this commonly used interpolation method. The emerging trends correspond to similar observations in precipitation. Despite the large discrepancies in data quality and density, the absolute error of the isoscapes remained under 3.1 $$\permil$$ ‰ for $$\delta ^{18}$$ δ 18 O. Major limitations are the extrapolation to regions with no primary data as well as boundaries between data-rich and data-scarce areas. Conclusions: With this work, we provide the basis for further comprehensive large-scale isotope research, as well as reliable background values for local investigations in Germany. We also formulate recommendations to work with heterogeneous and scarce data, to ensure the plausibility of isotope values and to identify unknown deep groundwater samples using additional information such as tritium activities. These strategies could be applied to develop similar initiatives in other European countries and across national borders.