Field comparison of electrochemical gas sensor data correction algorithms for ambient air measurements

Abstract Electrochemical gas sensors (ECGS) have gained substantial popularity in ambient measurements. Several data correction algorithms had been proposed to tackle the drifting response of ECGS due to environmental factors, but there is a lack of performance evaluation of these data correction schemes. To fill this knowledge gap, we conduct a comprehensive evaluation of these data correction algorithms using a large dataset from field comparisons. The dataset covered three commonly used gas pollutants, including CO, NO2 and O3 measured by both ECGS and reference instruments, with a time resolution of 1 minute and a duration of 6 months. Taking advantage of this large dataset, the performance of 8 different data correction schemes (2 new algorithms and 6 algorithms from the literature) was benchmarked by a set of evaluation metrics using raw signals from ECGS (nA level currents from the working and auxiliary electrodes). Eight scenarios were considered to examine the robustness of correction algorithms in response to different training and evaluation data period configurations. In addition, the bias dependence on temperature, RH, target gas levels and cross-sensitivity by different correction algorithms was investigated. Recommendations on data correction scheme selection are provided based on the comparison results.