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.