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 min 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.

电化学气体传感器（ECGS）在环境测量中已获得广泛普及。已经提出了几种数据校正算法来解决由于环境因素引起的ECGS漂移响应，但是缺乏对这些数据校正方案的性能评估。为了填补这一知识空白，我们使用来自现场比较的大型数据集对这些数据校正算法进行了全面评估。该数据集涵盖了三种常用的气体污染物，包括CO，NO ₂和O ₃使用ECGS和参考仪器进行测量，时间分辨率为1分钟，持续时间为6个月。利用这个庞大的数据集，使用来自ECGS的原始信号（来自工作电极和辅助电极的nA级电流），通过一组评估指标对8种不同的数据校正方案（文献中的2种新算法和6种算法）的性能进行了基准测试。 。考虑了八个方案，以检查响应于不同训练和评估数据周期配置的校正算法的鲁棒性。此外，还研究了通过不同的校正算法对温度，相对湿度，目标气体水平和交叉灵敏度的偏差依赖性。根据比较结果，提供了有关数据校正方案选择的建议。