Abstract: Batch and column experiments were conducted to determine whether zerovalent iron (ZVI) and sulfate reducing bacteria (SRB) can function synergistically and accelerate pollutant removal. Batch experiments suggest that combining ZVI with SRB can enhance the removal of U(?) synergistically. The removal rate of U(?) in the ZVI+SRB combining system is obviously higher than the total rate of ZVI system and SRB system with a difference of 13.4% at t=2 h and 29.9% at t=4 h. Column experiments indicate that the reactor filled with both ZVI and SRB biofilms is of better performance than the SRB bioreactor in wastewater basification, desulfurization and U(?) fixation. The results imply that the ZVI+SRB permeable reactive barrier may be a promising method for treating subsurface uranium contamination.

Abstract: The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.

Abstract: The environmental risks from shale gas extraction through the unconventional method of ‘fracking’ are considerable and impact on water supplies below and above ground. Since 2010 the recovery of natural shale gas through fracking has been proposed in parts of the fragile semi-arid ecosystems that make up the Karoo biome in South Africa. These unique ecosystems are heavily reliant on underground water, intermittent and ephemeral springs, which are at great risk of contamination by fracking processes. Diatoms are present in all water bodies and reflect aspects of the environment in which they are located. As the possibility of fracking has not been removed, the aim of the project was to determine if diatoms could be used for rapid biomonitoring of underground and surface waters in the Karoo. Over a period of 24 months, water samples and diatom species were collected simultaneously from 65 sites. A total of 388 diatom taxa were identified from 290 samples with seasonal and substrate variation affecting species composition but not the environmental information. Species diversity information, on the other hand, often varied significantly between substrates within a single sample. Analysis using CCA established that the diatom composition was affected by lithium, oxidized nitrogen, electrical conductivity, and sulphate levels in the sampled water. We conclude that changes in diatom community composition in the Karoo do reflect the water chemistry and could be useful as bioindicators.

Abstract: As an ancient underground hydraulic engineering facility, the Qanat system has been used to draw groundwater from arid regions. A qanat is a horizontal tunnel with a slight incline that draws groundwater from a higher location and delivers it to lower agricultural land. During long-term water delivery, the qanat tunnel has experienced different degrees of aging and collapse, which may result in the significant ground settlement and even disasters. This paper developed a two-dimensional laboratory system to investigate the influence of progressive failure on the stability of deeply buried qanat tunnels. The developed system is fully instrumented with a particle image velocimetry (PIV) system and earth pressure and displacement monitoring. A special cylindrical membrane tube is designed and connected to an advanced pressure–volume controller to simulate the step-wise failure process of the tunnel. Three model tests were conducted on a dry sand considering the buried qanat tunnels at three different depths. Experimental results clearly show the progressive evolution of soil arching effect in the dry sand associated with the progressive failure of the tunnels. The failure of the Qanat ground starts from the vault and develops upwards, which is closely related to the evolution of stress contour at three consecutive stages. Ground surface settlement and volume loss corresponding to three burial depths were compared. A deeply buried qanat tunnel has a small effect on surface settlement. Earth pressure evolution on the 2D plane shows the load redistribution when the qanat collapses. The maximum arch and the initial point of the limit state correspond to a volume loss of 12.5 % and 50 %, respectively. For the collapse of the deep buried qanat tunnel, ground earth pressure evolution can be divided into a stress-increasing region, stress-decreasing region, and no redistribution region. Furthermore, a multi trap-door model considering soil expansion is proposed to describe the progressive failure behavior and its effects.

Abstract: Kalahari sands which cover a large part of Southern Africa and extend into Central Africa are infertile and marginal soils for intensive agriculture. Therefore, high nitrogen fertilisation rates may degrade ecosystems of rivers with catchments covered by the Kalahari sands. A study on Mashare Farm located in the Okavango River basin showed that irrigated Kalahari sandy soils had a nitrate retention capacity, which enabled the soil to resist nitrate leaching in water saturated conditions. The irrigated soils were modified by agricultural activities; hence this study investigated if uncultivated and cultivated Kalahari sand soils had similar nitrate retention properties. The elementary composition of the soils was investigated for obtaining an insight into chemical properties that may be causing the nitrate retention capacity. A permeameter was used to leach out nitrates from irrigated and uncultivated soil samples, and nitrate concentrations were measured on the leaching effluent from the permeameter. Elemental analysis was done on the cultivated and the uncultivated soil samples using a Scanning Electron Microscope, a portable X-Ray Fluorescence analyzer, and an X-Ray Diffraction machine, and the later was also used for crystalline structure analyses. Sieve analyses confirmed that the Mashare’s cultivated and uncultivated topsoils were similar, and both were similar to Botswana Kalahari topsoil. The irrigated and cultivated subsoil had a higher average nitrate retention capacity of 76% compared to 73% for the uncultivated subsoil. Both samples had the same elements, although the proportions were different. Both soil samples were dominated by a quartz mineral, but the field soil had traces of palygorskite. The presence of aluminum and transition metals outside the minerals structure, but as coatings on the quartz sand grains enhanced nitrate retention capacity properties of the Kalahari sand soils.