Engineering a Sustainable Future: The Convergence of AI, Environmental, Chemical, Wastewater, and Mechanical Innovations

Abstract

Sustainable wastewater management requires integrated environmental, chemical, mechanical, and data-driven assessment, particularly in urban-industrial river systems such as Kanpur along the Ganga. This study evaluated river water quality and wastewater treatment performance by linking conventional pollution indicators with AI-oriented sustainability interpretation. Recent CPCB/UPPCB-based datasets and STP/CETP monitoring values were examined for Ganga monitoring points at Bithoor, Ranighat, and Jajmau Bridge, and for 130 MLD STP Jajmau, 42 MLD STP Sajari, and 36 MLD CETP Jajmau. The selected parameters included pH, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, and total suspended solids. Descriptive statistics, comparative assessment, and correlation based interpretation were applied to identify pollution patterns and treatment-performance differences. River-water results showed slightly alkaline pH across monitoring locations, while BOD increased toward Jajmau Bridge, indicating higher organic pollution pressure. Treatment-unit comparison showed that municipal STPs had lower pollution loads than the CETP. The 36 MLD CETP Jajmau recorded the highest BOD, COD, and TSS values, indicating critical treatment stress and suspended-solid removal limitations. Higher BOD was associated with reduced dissolved oxygen. The findings highlight the need for stronger effluent control, mechanical optimization, chemical monitoring, and AI-enabled anomaly detection to support real-time decision-making, compliance assessment, and Ganga River protection.