A Scalable Calibration Method for Enhanced Low Cost Sensor Accuracy in Dense Air Quality Monitoring Networks

Observing systems employing large numbers of low capital cost sensors have the potential to increase the spatial density of air quality measurements, enabling a variety of new applications that build on mapping air quality across metropolitan areas at neighborhood scale resolution. Achieving this goal requires not only a reduction in the capital costs for observations but also a simultaneous reduction in the labor costs for accurate, long-term observations. The Berkeley Environment Air quality and CO₂ Network (BEACO₂N) is an example of a dense low capital cost sensor network measuring O₃, CO, NO, and NO₂, particulate matter (PM_2.5) and CO₂ at dozens of locations in cities where it is deployed. Here, we describe a low labor cost in-situ field calibration for the Alphasense O₃, CO, NO, and NO₂ sensors that does not require co-location with a reference, accounts for temporal drift, and that can be easily implemented across a large number of nodes. This method leverages uniform periods in concentrations across the network to calibrate the sensors. The calibration has low biases with respect to temperature, humidity, and concentration, as well as high coefficients of determination and low root mean square errors. The method is a crucial step toward lowering operational costs of delivering accurate measurements in dense networks employing large numbers of inexpensive air quality sensors.