Seasonal Influence on the Performance of Low-Cost NO₂ Sensor Calibrations.

Area of Science:

• Environmental Science
• Sensor Technology
• Atmospheric Chemistry

Background:

• Low-cost sensor technology offers potential to supplement official air quality monitoring networks.
• Current electrochemical nitrogen dioxide (NO2) sensors face technical challenges impacting data reliability.

Purpose of the Study:

• To evaluate the performance enhancement of an Alphasense B43F electrochemical NO2 sensor using advanced calibration models.
• To assess the added value of multiple linear regression, including cross-terms, for sensor calibration.

Main Methods:

• Duplicate sensor data collected over one year at four reference sites in the Netherlands.
• Calibration models incorporating NO2 reference measurements, ozone (O3), and temperature were developed and tested.
• Performance evaluated using R-squared (R2) values and comparison against EU Data Quality Objectives.

Main Results:

• Calibration with O3 and temperature significantly improved NO2 prediction accuracy, increasing R2 from <0.5 to 0.69-0.84.
• Calibrated sensor uncertainty met EU indicative method Data Quality Objectives in specific instances.
• Sensor signal remained a crucial predictor within multilinear regression models.

Conclusions:

• Advanced calibration models substantially improve the performance of low-cost NO2 sensors.
• The study highlights the impact of calibration period length on sensor data quality, offering insights for short-period calibration strategies.