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1. Data Calibration Methodology

How Calibration is Performed​

AirQo monitors undergo rigorous calibration protocols to ensure data accuracy and reliability. Our calibration approach includes:

  • Co-location with Reference-Grade Monitors — AirQo low-cost sensors are periodically co-located with BAM (Beta Attenuation Monitor) and other reference-grade instruments to establish calibration relationships.
  • Localised Calibration Models — We deploy city-specific calibration models using reference monitors in individual urban areas. This approach accounts for local atmospheric conditions and pollution characteristics.
  • Machine Learning Calibration — Advanced algorithms are applied to sensor data to correct for environmental factors (temperature, humidity) and sensor drift over time.
  • Continuous Quality Assurance — Regular validation studies ensure our data maintains correlation with high-end reference-grade monitors.

Understanding Raw vs. Calibrated Data​

On the AirQo platform, you will find two data streams:

  • Raw Data — Unprocessed sensor readings directly from the monitors.
  • Calibrated Data — Processed data that has undergone our calibration pipeline.
Recommendation

For research purposes, always use calibrated data. It provides the most accurate representation of ambient air quality conditions.

Technical Reference​

For detailed methodology on our calibration approach, refer to our published research:

This peer-reviewed publication provides comprehensive technical details on our sensor calibration, validation protocols, and performance evaluation.