Recent developments in hazardous pollutants removal from wastewater and water reuse within a circular economy

Data availability: The authors declare that the data supporting the findings of this study are available within the manuscript. Further data can be requested (if need be) by contacting the corresponding author.Copyright © The Author(s) 2022. Recent advances in wastewater treatment processes have resulted in high removal efficiencies for various hazardous pollutants. Nevertheless, some technologies are more suitable for targeting specific contaminants than others. We comprehensively reviewed the recent advances in removing hazardous pollutants from industrial wastewater through membrane technologies, adsorption, Fenton-based processes, advanced oxidation processes (AOP), and hybrid systems such as electrically-enhanced membrane bioreactors (eMBRs), and integrated eMBR-adsorption system. Each technology’s key features are compared, and recent modifications to the conventional treatment approaches and limitations of advanced treatment systems are highlighted. The removal of emerging contaminants such as pharmaceuticals from wastewater is also discussed.Khalifa University through the Center for Membranes and Advanced Water Technology (CMAT), under grant number RC2-2018-009

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. Highlights The effectiveness of existing river water quality monitoring network is assessed Significance of seasonal redesign of the monitoring network is demonstrated Rationalization of water quality parameters is performed in a statistical framework