Emerging pollutants-Part II: Treatment

Design of a new fountain reactor for contamination degradation using advanced oxidation processes with hybrid techniques and modeling evaluation

Due to the water and energy crises, wastewater treatment systems that are more energy efficient and capable of large volume degradation are a priority. Photochemical decomposition methods have a significant impact on pollutant treatment. The use of these methods in conjunction with a novel designed reactor and hybridization processes can result in considerable treatment results. This research used a fountain system in a UV/H2O2 process to generate a belt-type liquid film with a low thickness and high mixing to remove methyl orange as a model pollutant. The flow rate, H2O2 concentration, temperature, and UV intensity were the parameters evaluated in this series of tests. After 90 minutes under optimum conditions, the maximum degradation of methyl orange was 99.73 percent. The efficiency of the purification process was increased to 99 percent in 75 minutes by using the optimum state of hybridization of UV/US/H2O2 processes. Two deep neural network models and a pseudo-first-order kinetic model were created to fit the experimental data. The results reveal a good fit between the experimental data and the model prediction. The discovered synergistic factor (1.168) and energy yield (2.65 g/kWh) demonstrated the high efficiency of the hybridization process and the outstanding function of the designed system, respectively.

Adsorption and Photocatalytic Degradation of Methylene Blue on TiO2 Thin Films Impregnated with Anderson-Evans Al-Polyoxometalates: Experimental and DFT Study

In this work, we fabricated a TiO2 thin film, and the same film was modified with an Anderson aluminum polyoxometalate (TiO2-AlPOM). Physical-chemical characterization of the catalysts showed a significant change in morphological and optical properties of the TiO2 thin films after surface modification. We applied the kinetic and isothermal models to the methylene blue (MB) adsorption process on both catalysts. The pseudo-second order model was the best fitting model for the kinetic results; qe (mg/g) was 11.9 for TiO2 thin films and 14.6 for TiO2-AlPOM thin films, and k2 (g mg-1 min-1) was 16.3 × 10-2 for TiO2 thin films and 28.2 × 10-2 for TiO2-AlPOM thin films. Furthermore, the Freundlich model was suitable to describe the isothermal behavior of TiO2, KF (5.42 mg/g), and 1/n (0.312). The kinetics of photocatalytic degradation was fitted using the Langmuir-Hinshelwood model; kap was 7 × 10-4 min-1 for TiO2 and 13 × 10-4 min-1 for TiO2-AlPOM. The comparative study showed that TiO2 thin films reach a 19.6% MB degradation under UV irradiation and 9.1% MB adsorption, while the TiO2-AlPOM thin films reach a 32.6% MB degradation and 12.2% MB adsorption on their surface. The surface modification improves the morphological, optical, and photocatalytic properties of the thin films. Finally, the DFT study supports all the previously shown results.

Application of gas chromatographic data and 2D molecular descriptors for accurate global mobility potential prediction

Mobility is a key feature affecting the environmental fate, which is of particular importance in the case of persistent organic pollutants (POPs) and emerging pollutants (EPs). In this study, the global mobility classification artificial neural networks-based models employing GC retention times (RT) and 2D molecular descriptors were constructed and validated. The high usability of RT was confirmed based on the feature selection step performed using the multivariate adaptive regression splines (MARS) tool. Although RT was found to be the most important, according to Kruskal-Wallis ANOVA analysis, it is insufficient to build a robust model, which justifies the need to expand the input layer with 2D descriptors. Therefore the following molecular descriptors: MPC10, WTPT-2, AATS8s, minaaCH, GATS7c, RotBtFrac, ATSC7v and ATSC1p, which were characterized by a high predicting potential were used to improve the classification performance. As a result of machine learning procedure ten of the most accurate neural networks were selected. The external validation showed that the final models are characterized by a high general accuracy score (85.71-96.43%). The high predicting abilities were also confirmed by the micro-averaged Matthews correlation coefficient (MAMCC) (0.73-0.88). To evaluate the applicability of the models, new retention times of selected POPs and EPs including pesticides, polycyclic aromatic hydrocarbons, pharmaceuticals, fragrances and personal care products were measured and used for mobility prediction. Further, the classifiers were used for photodegradation and chlorination products of two popular sunscreen agents, 2-ethyl-hexyl-4-methoxycinnamate and 2-ethylhexyl 4-(dimethylamino)benzoate.

Sequential ClO2-UV/chlorine process for micropollutant removal and disinfection byproduct control

This study systematically revealed the feasibility of the sequential ClO₂-UV/chlorine process for micropollutant removal and disinfection byproduct (DBP) control. The results demonstrated that the sequential ClO₂-UV/chlorine process was effective for the removal of 12 micropollutants. ClO₂ pre-treatment reduced the formation of disinfect byproducts (DBPs) in the UV/chlorine process. Compared to the UV/chlorine process, ClO₂ pre-treatment (1.0 mg L^-1) decreased the formation of the 6 DBPs by 25.1-72.2%; and decreased the formation potential of the 6 DBPs by 13.9-51.8%. Moreover, ClO₂ pre-treatment reduced the concentration of total organic chlorine by 19.8%. ClO₂ pre-treatment affected the UV/chlorine process in different ways. Firstly, ClO₂ pre-treatment generated chlorite, which dominantly served as a scavenger of chlorine radical (Cl) and hydroxyl radical (HO). Secondly, ClO₂ pre-treatment decreased the reactivity of natural organic matter (NOM) towards radicals. Finally, ClO₂ pre-treatment altered the properties of NOM, in terms of reducing the electron-donating capacity and aromaticity of NOM (SUVA₂₅₄), and slightly reducing the average molecular weight of NOM. Overall, ClO₂ pre-treatment effectively controlled the formation of DBPs in the UV/chlorine process. This study confirmed the sequential ClO₂-UV/chlorine process was an alternative strategy to balancing the micropollutant removal and DBP control.

Bacterial nanocellulose papers with high porosity for optimized permeance and rejection of nm-sized pollutants

Access to clean potable water is increasingly becoming a struggle for whole humankind, thus water treatment to remediate wastewater and fresh water sources is an important task. Pollutants in the nanoscale, such as viruses and macromolecules, are usually removed by means of membrane filtration processes, predominantly nanofiltration or ultrafiltration. Cellulose nanopapers, prepared from renewable resources and manufactured by papermaking, have recently been demonstrated to be versatile alternatives to polymer membranes in this domain. Unfortunately, so far nanopaper filters suffer from limited permeance and thus efficiency. We here present nanopapers made from bacterial cellulose dispersed in water or different types of low surface tension organic liquids (alcohol, ketone, ether) through a simple papermaking process. Nanopapers prepared from organic liquids (BC-org) exhibited 40 times higher permeance, caused by a lower paper density hence increased porosity, compared to conventional nanopapers produced from aqueous dispersions, ultimately enhancing the efficiency of bacterial cellulose nanopaper membranes. Despite their higher porosity, BC-org nanopapers still have pore sizes of 15-20 nm similar to BC nanopapers made from aqueous dispersions, thus enabling removal of contaminants the size of viruses by a size-exclusion mechanism at high permeance.