Designing an indoor air quality system to ensure occupational health in Mexico
DOI:
https://doi.org/10.4114/inteletica.vol3iss5pp67-92Palabras clave:
Internet of Things, Machine Learning, Public HealthResumen
Air pollution in Mexico represents a significant public health concern, particularly in indoor environments where pollutant concentrations may reach critical levels. Urban growth, vehicular activity, and industrial emissions contribute to indoor exposure to carbon dioxide (CO₂), volatile organic compounds (VOCs), and particulate matter (PM2.5 and PM10), which have been associated with adverse health effects. Previous studies have documented outcomes such as fatigue, drowsiness, cardiovascular alterations linked to elevated CO₂ levels, central nervous system impairments from prolonged VOC exposure, and respiratory diseases related to particulate matter. In this context, this paper defines a technical proposal for monitoring indoor environments in Mexico through a systematic literature review and experimentation with data scenarios from Morelos and Puebla. These elements represent initial steps toward the development of an indoor air quality system for the Mexican context. The proposal integrates engineering-based indicators, health impact analysis, and data-driven experimentation to identify environmental risk conditions. It also considers continuous monitoring, data analysis, and risk forecasting as core components for the early detection of hazardous indoor conditions. Designed as a flexible and scalable approach, the proposed system may be adapted to residential, educational, and office settings, as well as future local infrastructure deployments. Overall, this work provides a technical foundation for designing an indoor air quality monitoring system aimed at supporting healthier indoor environments in Mexico.
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Derechos de autor 2026 Ariel Isaac Posada Barrera, Laura Margarita Rodriguez Peralta, Karen Lizet Rosales Portillo (Autor/a)

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Open Access Journal.
Edita: IBERAMIA. Sociedad Iberoamericana de Inteligencia Artificial (www.iberamia.org).
