Apoyo al desarrollo de BackEnd de Cálculo de estadísticas de la red de monitores de ruido ambiental en Bogotá (RMRAB)

Abstract

The city of Bogotá faces high levels of environmental noise mainly caused by vehicular traffic, industrial activities, and social and commercial dynamics. In this context, the Environmental Noise Monitoring Network of Bogotá (RMRAB) provides continuous hourly records; however, the analysis of this information has been limited by the lack of automated tools for calculating acoustic indicators. This internship aimed to develop backend solutions in Python to automate the calculation of environmental noise indicators, specifically Noise Climate (NC) and LDN (Day-Night Noise Level), based on data provided by the RMRAB. Two main scripts were designed and implemented using the Pandas and NumPy libraries. These tools allow verification, cleaning, processing, and classification of the data, as well as conversion between linear and logarithmic scales. The developed algorithms apply temporal grouping methodologies (by hour, day, day type, and month) and generate CSV output files with statistical summaries and consistency validations. The automated system contributes to reducing human error, optimizing processing time, and ensuring reproducibility of results, thereby strengthening the technical support for the Secretariat of Environment of Bogotá (SDA) in the preparation and updating of the city’s Strategic Noise Maps (MER). The results confirm the feasibility of using scientific programming tools for environmental management, demonstrating the effectiveness of automated processing in evidence-based decision-making.