Exploring the Potential of Mathematical Self-Purification Models Used for Evaluating Water Quality in Rivers

The quality of water in rivers and their self-purification capacity are critical for maintaining healthy aquatic ecosystems. This study aims to analyze and compare various mathematical models of self-purification, assessing their applicability in restoring water quality and proposing recommendations for their improved use. A systematic review of the scientific literature was conducted following PRISMA 2020 guidelines to ensure a rigorous approach. Research questions were framed using the PICO model, which includes Population, Intervention, Comparison, and Outcomes. Relevant studies published between 2015 and 2024 regarding mathematical models of river self-purification were selected. Inclusion and exclusion criteria were applied, and a critical analysis of findings was performed, highlighting methodologies and results. The results indicate that the effectiveness of self-purification models varies significantly depending on environmental and geographic characteristics. A need for more specific models and the integration of local variables was identified as a research gap that requires attention in future studies. Furthermore, recommendations were made to enhance model calibration and validation, as well as to incorporate innovative approaches for optimizing water quality management in rivers. These mathematical models are essential tools for managing river water quality, promoting public health, and contributing to the achievement of Sustainable Development Goal 6 (SDG 6).