Innova ITFIP Magazine

Efficiency of subsurface wetlands with power bi in university wastewater treatment

Diego Edison Quiroga Rojas

Diego Armando Castro Munar

María Paula Gómez Leal

DOI: https://doi.org/10.54198/innova16.02

Keywords: Subsurface flow wetlands, water quality efficiency, wastewater, Power BI, data analysis

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Abstract

The treatment of wastewater through natural systems such as subsurface flow wetlands represents a sustainable strategy for mitigating the environmental impact generated by human activities. In this context, the present article describes the design and implementation of a Model for Efficiency Evaluation and Optimization of a Subsurface Flow Wetland bit Ornamental Species for the treatment of wastewater on the Girardot Campus of the University of Cundinamarca.

The research integrates data science tools, particularly Power BI, to develop an interactive system for monitoring, analyzing, and visualizing the efficiency in the removal of pollutants such as pH, Chemical Oxygen Demand (COD), Ammoniacal Nitrogen (NH??- /NH?-N), Phosphorus (P), and Total Suspended Solids (TSS), measured before and after treatment. 

 

The methodological process included the periodic capture of physicochemical field data, structured recording in spreadsheets, data cleaning and validation using Power Query, and transformation into a relational model under a star schema approach in Power BI. Key performance indicators (KPIs) were established, along with operational cost metrics and initial investment figures, allowing for a direct link between technical performance and the prototype's economic sustainability.

The resulting dashboard allows researchers, technicians, and students to dynamically consult the temporal evolution of indicators, compare parameters across periods, and make informed decisions for wetland management. Differentiated access levels were implemented to ensure data security and facilitase continuous data collection.

The results show high system efficiency in pollutant removal: 87.7% for COD, 65.7% for Ammoniacal Nitrogen, 58% for Phosphorus, and 91% for TSS—figures that stand out for a natural system operated in an academic setting. This information, clearly and intuitively visualized in the dashboard, has strengthened technical decision-making and encouraged student engagement in using digital tools for environmental management.

This study demonstrates that the integration of business intelligence tools and decentralized ecological solutions is key to strengthening academic and environmental research processes. The proposed model is replicable in other educational institutions interested in linking science, technology, and sustainability to improve their wastewater treatment systems.

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