Pre-Deployment Testing, Augmentation and Calibration of Cross-Sensitive Sensors

Over the past few years, many low-cost pollution sensors have been integrated into measurement platforms for air quality monitoring. However, using these sensors is challenging: concentrations of toxic gases in ambient air often lie at sensors’ sensitivity boundaries, environmental conditions affect the sensor signal, and the sensors are crosssensitive to multiple pollutants. Datasheet information on these effects is scarce or may not cover deployment conditions. Consequently the sensors need to undergo extensive pre-deployment testing to examine their feasibility for a given application and to find the optimal measurement setup that allows accurate data collection and calibration. In this work, we propose a novel method to conduct infield testing of low-cost sensors. The algorithm proposed is based on multiple least-squares and leverages the physical variation of urban air pollution to quantify the amount of explained and unexplained sensor signal. We verify (i) whether a sensor is feasible for air quality monitoring in a given environment, (ii) model sensor cross-sensitivities to interfering gases and environmental effects and (iii) compute the optimal sensor array and its calibration parameters for stable and accurate sensor measurements over long time periods. Finally, we apply our testing approach on five off-the-shelf low-cost sensors and twelve reference signals using over 9 million measurements collected in an urban area. We propose an optimized sensor array and show—compared to a state-of-the-art calibration technique—an up to 45% lower calibration error with better long-time stability of the calibration parameters.