Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO₂ sensor...

Area of Science:

• Environmental Science
• Sensor Technology
• Data Science

Background:

• Air pollution, particularly nitrogen dioxide (NO2), poses significant risks to health, environment, and economy.
• Accurate NO2 monitoring is vital for risk mitigation, but traditional methods are expensive and complex.
• Development of cost-effective NO2 sensors is ongoing, but reliability remains a challenge.

Purpose of the Study:

• To introduce a precise calibration method for cost-effective NO2 sensors.
• To improve the accuracy and reliability of low-cost NO2 monitoring solutions.
• To enable widespread deployment of affordable air quality monitoring systems.

Main Methods:

• Statistical preprocessing of low-cost sensor readings to align with reference data.
• Development of an artificial neural network (ANN) surrogate to predict sensor correction coefficients.
• Integration of environmental variables (temperature, humidity, pressure), auxiliary sensor data, and historical readings for calibration.
• Application of global data scaling techniques.

Main Results:

• The proposed calibration framework significantly enhances the accuracy of cost-effective NO2 sensors.
• Achieved a high correlation coefficient (near 0.95) with reference data.
• Demonstrated a low root-mean-square error (RMSE) below 2.4 µg/m³.
• Validated using a custom monitoring platform and public reference station data over five months.

Conclusions:

• The novel calibration technique establishes cost-effective NO2 sensors as a viable and accurate alternative to traditional monitoring equipment.
• This breakthrough facilitates more accessible and widespread air quality monitoring.
• The findings support the development of improved strategies for managing air pollution and protecting public health.