Antibiotic-contaminated wastewater treatment and remediation by electro-advanced oxidation processes (EAOPs)

TL;DR: This paper examines the environmental consequences of antibiotic contamination, including antibiotic-resistant bacteria, and proposes innovative degradation techniques using engineered nanoparticles and biochar to mitigate the impact and preserve antibiotic effectiveness.

Abstract: The recalcitrant nature of emerging contaminants in water has raised serious concerns, and addressing their removal aligns with the aims of SDG 6. This has necessitated research on photocatalysis, but its cost-intensive nature requires thorough optimization, which is a tedious manual process. Hence, in this study, different machine learning (ML) models (Elastic-Net, Lasso, XGBoost, Gradient Boosting (GB), Random Forest (RF), and Artificial Neural Network (ANN)) have been used to model the photocatalytic degradation of sulfamethoxazole, 17β-Estradiol, and carbamazepine in the presence of M-Al@ZnO. The training dataset included removal of the 3 contaminants, with pH varied from 2-10 (M-Al@ZnO dose = 0.5 g/L and contaminant concentration = 1000 μg/L), M-Al@ZnO dose varied from 0.1-1 g/L (pH = 8, and contaminant concentration = 1000 μg/L), and contaminant concentrations varied from 500 to 2000 μg/L (pH = 8 and M-Al@ZnO dose = 0.5 g/L). Across all the models, GB exhibited the most promising results (R2: 0.9648 and RMSE: 3.9581). SHAP analysis revealed that irradiation time (∼60-85%) was the dominant factor, followed by pH (∼20-45%) and dose (∼5-15%), with pollutant concentrations having minimal or negative impact on removal. The models were then used to predict data at non-experimented points (pH varied between 2-10, dose varied between 0.1-1/g/L, and time varied between 20-120 mins). Contour plots generated using GB-predicted data explained the interactive effects of dependent variables and hinted that at optimum conditions (pH=8, Dose=0.7g/L), the system can effectively remove 90% of contaminants within 90 mins.

TL;DR: This review brings up important questions that are still open, and addresses some significant issues which must be tackled in the future for a better understanding of the behavior of antibiotics in the environment, as well as the risks associated with their occurrence.

TL;DR: In this paper, a review of the electrochemical methods used at lab and pilot plant scale to decontaminate synthetic and real effluents containing dyes, considering the period from 2009 to 2013, as an update of our previous review up to 2008.

TL;DR: The first global map (228 countries) of antibiotic consumption in livestock is presented and it is projected that antimicrobial consumption will rise by 67% by 2030, and nearly double in Brazil, Russia, India, China, and South Africa.

TL;DR: Electro-Fenton (EF) Process 6585 4.2.1.