The presence of plastic waste in water bodies has led to a growing concern due to the impact on human health. Among the most important plastic additive molecules is bisphenol-A (BPA). On the other hand, micro-pollutant removal processes that make use of agro-industrial waste have aroused the interest of researchers. This is because the accumulation of lignocellulosic materials is also an environmental problem. The objective of this work is to use corncob agro-industrial residue (CC) as an adsorbent in the removal of BPA in aqueous matrix. The adsorption studies were carried out in reaction media containing the ground vegetable fiber and BPA solution in concentrations of 10, 25 and 50 mg L-1, as well as at pHs 3, 5, 7 and 9. Isotherms models and kinetic models were evaluated. The results showed that CC is a potent BPA adsorbent in aqueous solution, leading to about 90% of removal. The adsorption kinetics followed the pseudo-second order model and isotherm that best suited was the Lagmuir model, and the maximum adsorption capacity was 51.25 mgper gram of adsorbent fiber, after 20 min of contact. The thermogravimetric analysis allowed us to conclude that CC is a material resistant to high temperatures, due to the presence of a considerable amount of lignin. Finally, the best results of BPA removal efficiency by CC compared to activated carbon corroborate its high biosorption capacity.

Due to the ability to catalyze the oxidation of several substrates, the enzyme laccase has been used in several applications, among them the bioremediation of emerging pollutants. This work evaluate the capacity of the cocoa agro-industrial residue to induce the production of Pycnoporus sanguineus laccase and to apply the enzymatic extract in the biodegradation of 17-α-ethinylestradiol (EE2). The macro and micronutrient levels of the cocoa residue were previously evaluated to establish correlations with induction of enzymatic production. The fungus was cultured for 7 days at 28 °C and shaking at 150 rpm. For the biodegradation, the enzymatic extract of laccase was added to the solution of EE2 and the percentage of removal was evaluated. The production of enzyme extract was notably increased by the addition of only 1% (m v−1) of agro-industrial cocoa residue. Laccase activity reached 3920 U mL−1 on the 7th day of cultivation, being superior to the positive control (511 U mL−1). The percentage of EE2 removal was 63% after 4 h, and after 8 h of reaction the EE2 concentration was below the limit of detection (LOD, 0.35 µg mL−1). Analysis showed the presence of kojic acid in the enzyme extract, constituting a powerful redox mediator, capable of enhancing the action of laccase.

This study assesses the performance and robustness of the groundwater potential (GWP) maps produced by the UNICEF model for deep groundwater exploration in Ethiopia. The UNICEF model is a weighted linear combination of hydrogeological parameters including permeability, slope, recharge, and lineament density, which has been calibrated using the expert judgements of local hydrogeologists. In order to assess the performance and robustness of the model, three techniques were employed: the analytic hierarchy process (AHP), logistic regression (LR), and artificial neural networks (ANNs). Three study areas (Dallol, Halaba and Shinelle) were selected on the basis of climatic and geological variation, in addition to the availability of well data pertaining to depth and yield. The performance of the UNICEF model in predicting outcomes of the well data included in the study was assessed by computing the receiver operating characteristic (ROC) curve. The solutions produced by the AHP and ANN were more accurate than the UNICEF model in determining the productivity of deep wells in the study data, whilst the LR model was less accurate than the UNICEF model. The groundwater productivity maps produced by the AHP and ANNs showed clear correlation with the maps produced by the UNICEF model, despite moderate (AHP) and severe (ANN) parameter perturbation, demonstrating the robustness of the UNICEF model. Whilst the AHP and ANN models demonstrated higher accuracy than the UNICEF model, this must be considered against the well data used to assess accuracy, which were drawn from a small sample of non-ideal distribution. Although this study focuses on case studies in Ethiopia the key findings are applicable internationally, namely, that the use of the AHP in data-scarce environments provides robust models, and that with the addition of easily obtainable well data the accuracy of modelling can be significantly increased through the application of ANNs.