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

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 (PM2.5), 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.