Designing a machine learning-calibrated IOT sensor network for real-time air quality assessment

Abstract

Existing air quality monitoring infrastructure in Kazakhstan provides limited spatial coverage, particularly in cities with extreme continental climates and coal-dominated PM2.5 emissions. This thesis presents the design, deployment, and evaluation of an IoT-based sensor network for real-time PM2.5 monitoring in Astana. Four ESP32-based sensor nodes with PMS5003 sensors were deployed across the city, collecting 14,444 measurements over 28 days (February–March 2026) with 89.95% data completeness at temperatures down to −26.8 °C. Colocation with the Kazhydromet-14 reference station enabled machine learning calibration, with Random Forest achieving the highest accuracy (R² = 0.84, RMSE = 3.80 µg/m³), satisfying the EPA performance criterion. Age-based calibration analysis revealed that linear model coefficients degrade by 76.8% within one week during seasonal transitions, while Random Forest maintains stable performance (R² = 0.93–0.99), leading to a weekly retraining recommendation. For 7-day PM2.5 forecasting, LSTM was identified as the best model (R² = 0.23, RMSE = 11.62 µg/m³).