Integrated Governance Model for Monitoring Potable Water Quality and Laboratory Effluents in Universities

This study proposes and analyzes an integrated framework for monitoring potable water quality and laboratory effluent management in universities, with emphasis on its practical application in a Brazilian public institution. Adopting a qualitative and documentary approach, the research was based on high-impact scientific publications, institutional reports, and environmental databases. The results demonstrate that effective water and effluent governance depends on the interaction of three core dimensions: regulatory compliance, technological innovation, and institutional governance. These elements operate synergistically to ensure transparency, risk prevention, and environmental accountability. The proposed University Laboratory Water Monitoring Framework (UL-WMF) illustrates how universities can transform water control into a managerial and educational tool aligned with sustainability goals. The illustrative institutional application revealed potential for integrating Internet of Things (IoT) and Laboratory Information Management System (LIMS) technologies into environmental management routines, reinforcing universities’ strategic role in achieving global sustainability objectives. Despite relying on secondary data, this study provides a scalable foundation for decision support systems and future empirical validation. The novelty of the University Laboratory Water Management Framework (UL-WMF) lies in its integration of potable water monitoring and laboratory effluent governance into a single operational framework, addressing a gap in the existing literature and offering a model specifically tailored to the context of universities in developing countries. The applied component of the study consists of an illustrative institutional case constructed exclusively from publicly available environmental and governance reports. This illustration serves to demonstrate the operational relevance of the proposed framework, without implying field measurements or primary data collection.