V3I8P8

Advanced Catalytic Oxidative Processes and Multivariate Physicochemical Analysis integrated with Machine Learning and IoT for Wastewater Treatment

Shalini Bhatnagar¹, Arun Sharma²*, Tanvi Thakur³, Omveer Singh⁴, Sharda Soni⁵

Abstract

High level of anthropogenic pollution in the groundwater can negatively impact both aquatic organisms and human health. Worldwide population relies on groundwater and poor waste disposal practices and industrial effluents worsen this vulnerability. This article will focus on the assessment of advanced catalytic oxidative processes (AOPs) and multivariate (MV) physicochemical analysis for wastewater management. The combination of multivariate analysis tools and ACOPs like ozonation, UV oxidation, and catalytic oxidation offers a comprehensive strategy to address water quality issues. Among the key areas that require further research to enhance the impact and effectiveness of treatment, the critical aspects that stand out are catalyst preparation and optimization techniques. However, several technical and financial issues require more policy intervention and effective management. The potential areas that can open the way for the enhancement of the processes and the achievement of the sustainable management of wastewater include machine learning, IoT and other modern technologies. The integration of machine learning (ML), Internet of Things (IoT), multivariate (MV) physicochemical analysis, and advanced catalytic oxidative processes (ACOPs) enables continuous monitoring of wastewater quality predictive identification of pollutant behaviour, intelligent optimization of treatment conditions, and efficient degradation of emerging contaminants. Collectively, these technologies improve treatment efficiency, reduce energy and chemical consumption, minimize anthropogenic pollution, and support sustainable environmental and public health protection. Hence there is a need for a comprehensive, evidence-based strategy to address the growing and concerning issue of groundwater pollution along with the formulation of well-structured strategies for sustainable management of the resource.

Keywords:

Waste water remediation; Multivariate analysis; Advanced oxidation; Catalytic processes; Machine Learning; IoT and Sustainability