Prediction of Co, Co2, and Particulate Matter Using Random Forest: Implementation of a Smart Monitoring Prototype in Nuevo Leon, Mexico

Authors

  • Luis Alejandro Reynoso-Guajardo Tecnológico Nacional de México/IT de Nuevo León
  • Axel Roberto-Perez Tecnológico Nacional de México/IT de Nuevo León
  • Carlos Hernandez-Santos Tecnológico Nacional de México/IT de Nuevo León https://orcid.org/0000-0003-1751-1096
  • Amadeo Hernandez Tecnológico Nacional de México/IT de Pachuca https://orcid.org/0009-0004-1266-0759
  • José Isidro Hernández-Vega Tecnológico Nacional de México/IT de Nuevo León https://orcid.org/0000-0002-2634-8828
  • Mario Carlos Gallardo-Morales Tecnológico Nacional de México/IT de Nuevo León
  • Nain de la Cruz Centro de Investigación y de Estudios Avanzados del IPN, Unidad Monterrey
  • María Ernestina Macias-Arias Tecnológico Nacional de México/IT de Nuevo León
  • Jorge Eduardo Ortega-Lopez Tecnológico Nacional de México/IT de Nuevo León
  • Juan Velazquez-Coronel Tecnológico Nacional de México/IT de Nuevo León

DOI:

https://doi.org/10.61467/2007.1558.2026.v17i2.1234

Keywords:

Air quality, Air pollution, Artificial Intelligence, Random Forest, Embedded System, Pollutant detection

Abstract

This study introduces a predictive air quality monitoring system based on Random Forest machine learning models and low-cost embedded sensors. The system was designed and implemented in Guadalupe, Nuevo León, Mexico, to monitor carbon monoxide (CO), car-bon dioxide (CO2), and particulate matter (PM). Real-time data was collected using a Particle Photon 2 microcontroller with four different sensors. The data was processed using Python scripts, and the Random Forest model was trained to predict future pollutant values. Results demonstrated strong model performance, validated through statistical evaluation metrics and graphical comparisons. The proposed system shows promise for deployment in smart urban environments.

 

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Published

2026-02-16

How to Cite

Reynoso-Guajardo, L. A., Roberto-Perez, A., Hernandez-Santos, C., Hernandez, A., Hernández-Vega, J. I., Gallardo-Morales, M. C., de la Cruz, N., Macias-Arias, M. E., Ortega-Lopez, J. E., & Velazquez-Coronel, J. (2026). Prediction of Co, Co2, and Particulate Matter Using Random Forest: Implementation of a Smart Monitoring Prototype in Nuevo Leon, Mexico. International Journal of Combinatorial Optimization Problems and Informatics, 17(2), 324–340. https://doi.org/10.61467/2007.1558.2026.v17i2.1234

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Vol. 17 No. 2 (2026)

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