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

Abstract

Deployment of large numbers of low capital cost sensors to increase the spatial density of air quality measurements enables applications that build on mapping air at neighborhood scales. Effective deployment requires not only low capital costs for observations but also a simultaneous reduction in labor costs. The Berkeley Environmental Air Quality and CO₂ Network (BEACO₂N) is a 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 BEACO₂N O₃, CO, NO, and NO₂ sensors. This method identifies and leverages uniform periods in concentrations across the network for calibration. The calibration achieves high accuracy and low biases with respect to temperature, humidity, and concentration, with coefficients of determination and root mean square errors of 0.88 and 3.70 ppb for O₃, 0.66 and 3.16 ppb for NO₂, and 0.79 and 1.58 ppb for NO. Performance of the CO sensor is 0.90 and 33.3 ppb at a site colocated with reference measurements. 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.