  With the growing concern for air quality and its impact on human health,
interest in environmental gas monitoring has increased. However,
chemi-resistive gas sensing devices are plagued by issues of sensor
reproducibility during manufacturing. This study proposes a novel approach for
detecting sensor-to-sensor variations in sensing devices using the explainable
AI (XAI) method of SHapley Additive exPlanations (SHAP). This is achieved by
identifying sensors that contribute the most to environmental gas concentration
estimation via machine learning, and measuring the similarity of feature
rankings between sensors to flag deviations or outliers. The methodology is
tested using artificial and realistic Ozone concentration profiles to train a
Gated Recurrent Unit (GRU) model. Two applications were explored in the study:
the detection of wrong behaviors of sensors in the train dataset, and the
detection of deviations in the test dataset. By training the GRU with the
pruned train dataset, we could reduce computational costs while improving the
model performance. Overall, the results show that our approach improves the
understanding of sensor behavior, successfully detects sensor deviations down
to 5-10% from the normal behavior, and leads to more efficient model
preparation and calibration. Our method provides a novel solution for
identifying deviating sensors, linking inconsistencies in hardware to
sensor-to-sensor variations in the manufacturing process on an AI model-level.
