The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors

Abstract

The newest generation of air quality sensors is small, low cost, and easy to deploy. These sensors are an attractive option for developing dense observation networks in support of regulatory activities and scientific research. They are also of interest for use by individuals to characterize their home environment and for citizen science. However, these sensors are difficult to interpret. Although some have an approximately linear response to the target analyte, that response may vary with time, temperature, and/or humidity, and the cross-sensitivity to non-target analytes can be large enough to be confounding. Standard approaches to calibration that are sufficient to account for these variations require a quantity of equipment and labor that negates the attractiveness of the sensors' low cost. Here we describe a novel calibration strategy for a set of sensors, including CO, NO, NO2, and O-3, that makes use of (1) multiple co-located sensors, (2) a priori knowledge about the chemistry of NO, NO2, and O-3, (3) an estimate of mean emission factors for CO, and (4) the global background of CO. The strategy requires one or more well calibrated anchor points within the network domain, but it does not require direct calibration of any of the individual low-cost sensors. The procedure nonetheless accounts for temperature and drift, in both the sensitivity and zero offset. We demonstrate this calibration on a subset of the sensors comprising BEACO(2)N, a distributed network of approximately 50 sensor "nodes", each measuring CO2, CO, NO, NO2, O-3 and particulate matter at 10 s time resolution and approximately 2 km spacing within the San Francisco Bay Area.