ABSTRACT
The advancement of science and technology and the growth of industry have led to an escalating discharge of domestic sewage and industrial wastewater containing dyes. This surge in volume not only incurs higher costs but also exacerbates environmental burdens. However, the benefits of green and reusable catalytic reduction materials within dye processes are still uncertain. Herein, this study utilized the eco-friendly deep eutectic solvent method (DESM) and the chlorite-alkali method (CAM) to prepare a cellulose-composed wood aerogel derived from natural wood for 4-nitrophenol (4-NP) reduction. The life cycle assessment of wood aerogel preparative process showed that the wood aerogel prepared by the one-step DESM method had fewer environmental impacts. The CAM method was used innovatively to make uniform the chemical functional groups of different wood species and various wood maturities. Subsequently, palladium nanoparticles (Pd NPs) were anchored in the skeleton structure of the wood aerogel with the native chemical groups used as a reducing agent to replace external reducing agents, which reduced secondary pollution and prevented the agglomeration of nanoparticles. Results showed that the catalytic reduction efficiency of 4-NP can reach 99.8%, which shows promises for applications in wastewater treatment containing dyes. Moreover, investigation of the advantages of preparation methods of wood aerogel has important implications for helping researchers and producers choose suitable preparation strategies according to demand.
ABSTRACT
To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R2) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs.
