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Environmental pollution in urban centers presents significant challenges to public health and sustainability. In developing regions such as Central America, the lack of accessible monitoring technologies hampers the collection of spatial and temporal data necessary for effective planning. This paper presents the design, development, and evaluation of a low-cost portable IoT-based system for georeferenced monitoring of noise pollution and air quality parameters in San Salvador, El Salvador. The system integrates sound level (dB), Air Quality Index (AQI), CO2, TVOC, temperature, and humidity sensors, along with GPS and GSM-GPRS communication for real-time data transmission to a cloud platform. The field deployment was carried out via pedestrian traversals in two urban zones: the Universidad Tecnológica de El Salvador (UTEC) campus and the Historic Downtown of the city, covering seven days with three daily sessions. Data were analyzed using Python and QGIS to generate descriptive statistics, hourly trends, inferential comparisons (t-tests), and geospatial heat maps using IDW interpolation. The system offers a replicable framework for academic and municipal use in similar urban contexts throughout Latin America.

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A Low-Cost Portable MIoT System for Noise and Air Pollution Spatial Profiling: A Case Study in San Salvador

  • 会议论文

  • 发表日期：

    2026年

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  • Omar Otoniel Flores-Cortez,
  • Carlos Osmín Pocasangre Jiménez,
  • Fernando Arévalo

摘要

Environmental pollution in urban centers presents significant challenges to public health and sustainability. In developing regions such as Central America, the lack of accessible monitoring technologies hampers the collection of spatial and temporal data necessary for effective planning. This paper presents the design, development, and evaluation of a low-cost portable IoT-based system for georeferenced monitoring of noise pollution and air quality parameters in San Salvador, El Salvador. The system integrates sound level (dB), Air Quality Index (AQI), CO2, TVOC, temperature, and humidity sensors, along with GPS and GSM-GPRS communication for real-time data transmission to a cloud platform. The field deployment was carried out via pedestrian traversals in two urban zones: the Universidad Tecnológica de El Salvador (UTEC) campus and the Historic Downtown of the city, covering seven days with three daily sessions. Data were analyzed using Python and QGIS to generate descriptive statistics, hourly trends, inferential comparisons (t-tests), and geospatial heat maps using IDW interpolation. The system offers a replicable framework for academic and municipal use in similar urban contexts throughout Latin America.