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

## TL;DR

This study uses low-cost solar-powered sensors and machine learning to accurately measure air quality, offering a scalable solution for cities.

## Contribution

The novel integration of Super Learner calibration with LoRaWAN technology enables high-accuracy, low-cost air quality monitoring.

## Key findings

- The Super Learner model achieved an average test R2 value of 0.96 for particulate matter measurements.
- The system demonstrated high accuracy for PM2.5 (R2=0.99) and PM10.0 (R2=0.91).
- The hybrid network is feasible for urban deployment, such as in the Dallas-Fort Worth metroplex.

## Abstract

This study calibrates an affordable, solar-powered LoRaWAN air quality monitoring prototype using the research-grade Palas Fidas Frog sensor. Motivated by the need for sustainable air quality monitoring in smart city initiatives, this work integrates low-cost, self-sustaining sensors with research-grade instruments, creating a cost-effective hybrid network that enhances both spatial coverage and measurement accuracy. To improve calibration precision, the study leverages the Super Learner machine learning technique, which optimally combines multiple models to achieve robust PM (Particulate Matter) monitoring in low-resource settings. Data was collected by co-locating the Palas sensor and LoRaWAN devices under various climatic conditions to ensure reliability. The LoRaWAN monitor measures PM concentrations alongside meteorological parameters such as temperature, pressure, and humidity. The collected data were calibrated against precise PM concentrations and particle count densities from the Palas sensor. Various regression models were evaluated, with the stacking-based Super Learner model outperforming traditional approaches, achieving an average test R2 value of 0.96 across all target variables, including 0.99 for PM2.5 and 0.91 for PM10.0. This study presents a novel approach by integrating Super Learner-based calibration with LoRaWAN technology, offering a scalable solution for low-cost, high-accuracy air quality monitoring. The findings demonstrate the feasibility of deploying these sensors in urban areas such as the Dallas-Fort Worth metroplex, providing a valuable tool for researchers and policymakers to address air pollution challenges effectively.