Variance-based variable selection in sensor calibration with strong interferents -- application to air pollution monitoring with a carbon nanotube sensor array

Air and water pollution are major threats to public health, highlighting the need for reliable environmental monitoring. Low-cost multisensor systems are promising but suffer from limited selectivity, because their responses are influenced by non-target variables (interferents) such as temperature and humidity. This complicates pollutant detection, especially in data-driven models with noisy, correlated inputs. We propose a method for selecting the most relevant interferents for sensor calibration, balancing performance and cost. Including too many variables can lead to overfitting, while omitting key variables reduces accuracy. Our approach evaluates numerous models using a bias-variance trade-off and variance analysis. The method is first validated on simulated data to assess strengths and limitations, then applied to a carbon nanotube-based sensor array deployed outdoors to characterize its sensitivity to air pollutants.