Occurrence and characteristics of microplastics in South African beverages.

This study presents the first comprehensive assessment of microplastics (MPs) in alcoholic (AB) and non-alcohol (NAB) beverages in South Africa. Beverages in various packaging materials, specifically glass, aluminium, and polyethylene terephthalate (PET) were tested for MP content. The samples were filtered and digested, then stained with Rose Bengal dye to facilitate particle identification, followed by physical and chemical characterisation using stereomicroscopy and micro-Raman spectroscopy, respectively.

Detection and characterisation of microplastics in tap water from Gauteng, South Africa.

This study reports the presence, concentration, and characteristics of microplastics (MPs) in tap water in three suburbs in Gauteng Province in South Africa. Physical characterisation was conducted using stereomicroscopy and scanning electron microscopy following staining of MPs with the Rose Bengal dye. The concentrations of MPs in all samples ranged from 4.

Advancements in sustainable platinum and palladium recovery: unveiling superior adsorption efficiency and selectivity with a novel silica-anchored acylthiourea adsorbent.

This study addresses the pressing issue of depleting natural resources of platinum group metals (PGMs), driven by their widespread use in modern applications and increasing demand for renewable energy technologies. With conventional sources dwindling, the search for economically viable recovery methods from alternative sources has become crucial. Our focus was on innovating efficient recovery strategies, leading to the development of two novel silica-anchored adsorbents: DTMSP-BT-SG, a highly efficient acylthiourea adsorbent, and BTMSPA-SG, a silica-anchored amine adsorbent.

Comparison of individual and ensemble machine learning models for prediction of sulphate levels in untreated and treated Acid Mine Drainage.

Machine learning was used to provide data for further evaluation of potential extraction of octathiocane (S), a commercially useful by-product, from Acid Mine Drainage (AMD) by predicting sulphate levels in an AMD water quality dataset. Individual ML regressor models, namely: Linear Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge (RD), Elastic Net (EN), K-Nearest Neighbours (KNN), Support Vector Regression (SVR), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multi-Layer Perceptron Artificial Neural Network (MLP) and Stacking Ensemble (SE-ML) combinations of these models were successfully used to predict sulphate levels. A SE-ML regressor trained on untreated AMD which stacked seven of the best-performing individual models and fed them to a LR meta-learner model was found to be the best-performing model with a Mean Squared Error (MSE) of 0.

Improving acid mine drainage treatment by combining treatment technologies: A review.

The mining and processing of some minerals and coal result in the production of acid mine drainage (AMD) which contains elevated levels of sulfate and metals, which tend to pose serious environmental issues. There are different technologies that have been developed for the treatment of wastewater or AMD. However, there is no "one-size-fits-all" solution, hence a combination of available technologies should be considered to achieve effective treatment.