Adsorption and desorption behavior of arsenite and arsenate at river sediment-water interface.

The adsorption of inorganic arsenic (As) plays an important role in the mobility and transport of As in the river environment. In this work, the adsorption and desorption of arsenite [As(III)] and arsenate [As(V)] on river sediment were conducted under different pH, initial As concentrations, river water and sediment composition to assess As adsorption behavior and mechanism. Both adsorption kinetics and equilibrium results showed higher adsorption capacity of sediment for As(V) than As(III). Adsorption of As(III) and As(V) on river sediment was favored in acidic to neutral conditions and on finer sediment particles, while sediment organic matter marginally reduced adsorption capacity. In addition, higher adsorption affinity of As(III) and As(V) in river sediment was observed in deionised water than in river water. For the release process, the desorption of both As(III) and As(V) followed nonlinear kinetic models well, showing higher amount of As(III) release from sediment than As(V). Adsorption isotherm was well described by both Langmuir and Freundlich models, demonstrating higher maximum adsorption capacity of As(V) at 298.7 mg/kg than As(III) at 263.3 mg/kg in deionised water, and higher maximum adsorption capacity of As(III) of 234.3 mg/kg than As(V) of 206.2 mg/kg in river water. The XRD showed the changes in the peaks of mineral groups of sediment whilst FTIR results revealed the changes related to surface functional groups before and after adsorption, indicating that Fe-O/Fe-OH, Si(Al)-O, hydroxyl and carboxyl functional groups were predominantly involved in As(III) and As(V) adsorption on sediment surface. XPS analysis evidenced the transformation between these As species in river sediment after adsorption, whilst SEM-EDS revealed higher amount of As(V) in river sediment than As(III) due to the lower signal of Al.

Photocatalytic Degradation of Pharmaceutical Residues from Water and Sewage Effluent Using Different TiO2 Nanomaterials.

Pharmaceuticals are widely used and often discharged without metabolism into the aquatic systems. The photocatalytic degradation of pharmaceutical compounds propranolol, mebeverine, and carbamazepine was studied using different titanium dioxide nanostructures suspended in water under UV and UV-visible irradiation. Among three different photocatalysts, the degradation was most effective by using Degussa P25 TiO2, followed by Hombikat UV100 and Aldrich TiO2. The photocatalytic performance was dependent on photocatalyst dosage, with an optimum concentration of 150 mg L-1. The natural aquatic colloids were shown to enhance the extent of photocatalysis, and the effect was correlated with their aromatic carbon content. In addition, the photocatalysis of pharmaceuticals was enhanced by the presence of nitrate, but inhibited by the presence of 2-propanol, indicating the importance of hydroxyl radicals. Under optimum conditions, the pharmaceuticals were rapidly degraded, with a half-life of 1.9 min, 2.1 min, and 3.2 min for propranolol, mebeverine, and carbamazepine, respectively. In treating sewage effluent samples, the photocatalytic rate constants for propranolol (0.28 min-1), mebeverine (0.21 min-1), and carbamazepine (0.15 min-1) were similar to those in water samples, demonstrating the potential of photocatalysis as a clean technology for the effective removal of pharmaceuticals from sewage effluent.

Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts.

Pesticide pollution has been posing a significant risk to human and ecosystems, and photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) emerges as a powerful method for modeling complex water treatment processes. For the first time, this study developed novel ML models that improved the estimation of the photocatalytic degradation of various pesticides using ZnO-based photocatalysts. The input parameters encompassed the source of light, mass proportion of dopants to Zn, initial pesticide concentration (C0), pH of the solution, catalyst dosage and irradiation time. Additionally, physicochemical properties such as the molecular weight of the dopants and pesticides, as well as the water solubility of both dopants and pesticides, were considered. Notably, the numerical data were extracted from the literature via relevant tables (directly) or graphs (indirectly) using the web-based tool WebPlotDigitizer. Four ML models including multi-layer perceptron artificial neural network (MLP-ANN), particle swarm optimization-adaptive neuro fuzzy inference system (PSO-ANFIS), radial basis function (RBF), and coupled simulated annealing-least squares support vector machine (CSA-LSSVM) were developed. In comparison, RBF showed the best accuracy of modeling among all models, with the highest determination coefficient (R2) of 0.978 and average absolute relative deviation (AARD) of 4.80%. RBF model was effective in estimating the photocatalytic degradation of pesticides except for 2-chlorophenol, triclopyr and lambda-cyhalothrin, where CSA-LSSVM model demonstrated superior performance. Dichlorvos was completely degraded by ZnO photocatalyst under visible light. The sensitivity analysis by relevancy factor exhibited that light irradiation time and initial pesticide concentration were the most important parameters influencing photocatalytic degradation of pesticides positively and negatively, respectively. The new ML models provide a powerful tool for predicting pesticide degradation in wastewater treatment, which will reduce photochemical experiments and promote sustainable development.