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

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. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1234Dimensions.Open Alex.