Batch and Column Arsenate Sorption Using Turbinaria ornata Seaweed Derived Biochar: Experimental Studies and Mathematical Modeling

Physiochemical Analysis of Drinking Water and Treatment with a Homemade Filter: A Case Study of Illu Abba Bor Zone, Ethiopia

The drinking water quality was evaluated in order to provide a continuous supply of clean and safe drinking water for the preservation of public health. The study area consists of three villages: Tulube, Seddo, and Serdo, all of which are located near Mettu town, which is about 550 kilometers south-west of Ethiopia's capital, Addis Ababa. The physical and chemical parameters of the collected drinking water samples were assessed, including pH, turbidity, conductivity, total suspended solids (TSS), total dissolved solids (TDS), and the presence of heavy metals. The samples were examined in the laboratory, and the findings were compared to the World Health Organization (WHO) standards. Almost all of the physiochemical indicators were safe and within the permissible limit for drinking water quality. However, lead ion concentrations were found to be above the WHO standards. An adsorbent produced from banana pseudostems was used to remove lead ions from drinking water. The equilibrium parameters were determined using the Langmuir adsorption isotherm. The drinking water was treated for 4 h in a homemade adsorption column composed of filter medium (sand, charcoal, and powder of treated banana pseudostem). The data revealed that lead ions removal was nearly 70%, but still above the WHO standards.

The potential of polyethylene microplastics to transport copper in aquatic systems: Adsorption and desorption studies

Heavy metals and microplastics are two types of general contaminants that can cause significant damage to water systems and organisms. However, the interaction of heavy metal ions with microplastic in aquatic systems received less attention compared with that of organic contaminants. This study aims to explore the interaction of copper (II) ions with microplastic (polyethylene) in aquatic systems. The adsorption experiments were performed by varying several operational parameters including equilibrium pH, initial Cu (II) concentrations, and contact times. The microplastic was characterized using X-ray diffraction, scanning electron microscopy, and Fourier transform infrared. The results confirmed the porous nature of the microplastic surface and the existence of various binding sites. The maximum Cu (II) uptake by microplastic was recorded as 1.23 mg/g at pH 5, according to the Langmuir adsorption isotherm. The experimental isotherm data exhibited a good fit to the Toth model, followed by the Langmuir and Freundlich equations, according to the correlation coefficient and %error values. The pseudo-first kinetics equation showed a better fit to copper (II) kinetics data compared with the pseudo-second kinetics equation. Elution of copper (II) ions from copper (II)-loaded microplastic was attempted using several elutants, and the results indicated that 0.01 M HNO₃ performed well with elution efficiency over 99.5%. Thus, the elution experiments furnished proof that Cu-loaded microplastic may leach Cu (II) ions under rich acidic conditions, thereby aiding the transport of Cu (II) ions into the digestive tracts of aquatic organisms. PRACTITIONER POINTS: Polyethylene microplastics showed potential to sorb copper ions. The mechanism was electrostatic interaction between microplastics and metal ions. Maximum copper adsorption by microplastic was recorded as 1.23 mg/g. Once desorbed, Cu(II) transferred into the digestive tracts of aquatic organisms.

Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural Networks

To mitigate the negative effects of pollution produced by the growing levels of pollutants in the environment, research and development of novel and more effective materials for the treatment of pollutants originating from a variety of industrial sources should be prioritized. In this research, a UV-irradiated nano-graphene oxide (UV/n-GO) was developed and studied for methylene blue (MB) adsorption. Furthermore, the batch adsorption studies were modelled using response surface modelling (RSM) and artificial neural networks (ANNs). Investigations employing FTIR, XRD, and SEM were carried out to characterize the adsorbent. The best MB removal of 95.81% was obtained at a pH of 6, a dose of 0.4 g/L, an MB concentration of 25 mg/L, and a period of 40 min. This was accomplished with a desirability score of 0.853. A three-layer backpropagation network with an ideal structure of 4-4-1 was used to create an ANN model. The R2 and MSE values determined by comparing the modelled data with the experimental data were 0.9572 and 0.00012, respectively. The % MB removal predicted by ANN was 94.76%. The kinetics of adsorption corresponded well with the pseudo-second-order model (R2 > 0.97). According to correlation coefficients, the order of adsorption isotherm models is Redlich–Peterson > Temkin > Langmuir > Freundlich. Thermodynamic investigations show that MB adsorption was both spontaneous and endothermic.