Solution to the Problem of Calibration of Low-Cost Air Quality Measurement Sensors in Networks.

Abstract

We provide a simple, remote, continuous calibration technique suitable for application in a hierarchical network featuring a few well-maintained, high-quality instruments ("proxies") and a larger number of low-cost devices. The ideas are grounded in a clear definition of the purpose of a low-cost network, defined here as providing reliable information on air quality at small spatiotemporal scales. The technique assumes linearity of the sensor signal. It derives running slope and offset estimates by matching mean and standard deviations of the sensor data to values derived from proxies over the same time. The idea is extremely simple: choose an appropriate proxy and an averaging-time that is sufficiently long to remove the influence of short-term fluctuations but sufficiently short that it preserves the regular diurnal variations. The use of running statistical measures rather than cross-correlation of sites means that the method is robust against periods of missing data. Ideas are first developed using simulated data and then demonstrated using field data, at hourly and 1 min time-scales, from a real network of low-cost semiconductor-based sensors. Despite the almost naïve simplicity of the method, it was robust for both drift detection and calibration correction applications. We discuss the use of generally available geographic and environmental data as well as microscale land-use regression as means to enhance the proxy estimates and to generalize the ideas to other pollutants with high spatial variability, such as nitrogen dioxide and particulates. These improvements can also be used to minimize the required number of proxy sites.