Work

Calibration of Low-Cost LoRaWAN-Based IoT Air Quality Monitors Using the Super Learner Ensemble: A Case Study for Accurate Particulate Matter Measurement

Year: 2025

Type: article

Abstract: This study calibrates an affordable, solar-powered LoRaWAN air quality monitoring prototype using the research-grade Palas Fidas Frog sensor. By leveraging the Super Learner machine learning technique, it develops cost-effective sensors for accurate PM (Particulate Matter) monitoring in low-resource settings. Data was collected by co-locating the Palas sensor and LoRaWAN devices under various climatic conditions over multiple days to ensure reliable calibration. The LoRaWAN air quality monitor integrates sensors that measure PM concentrations and meteorological parameters, including temperature, pressure, and humidity. The collected data were calibrated using precise PM concentrations and particle count density measured by the Palas sensor. Various regression models were evaluated, with the stacking model (Super Learner) demonstrating superior performance. By combining simple and complex models, the stacking model achieved the most accurate predictions, with an average test R2 value of 0.96 across all target variables. Specifically, it achieved R2 values of 0.99 for PM2.5 and 0.91 for PM10.0, demonstrating near-research-grade accuracy and underscoring the robustness of the calibration. This study offers a practical and scalable solution for cost-effective air quality monitoring, with significant potential for deployment in the Dallas-Fort Worth metroplex and similar urban areas.

Source: Sensors

DOI: 10.3390/s25051614