Identification of Air Pollutants with Thermally Modulated Metal Oxide Semiconductor Gas Sensors through Machine Learning Based Response Models

Abstract

This study addresses the problem of environmental monitoring of air in cities and industrial areas, which consists in identification of gases and volatile organic compounds using metal oxide (MOX) semiconductor gas sensors. To provide selectivity in the detection of certain gases, the laboratory-made MOX gas sensors are operated in a modulated working temperature mode in combination with signal processing and machine learning approach to establish the response models. Six types of nonlinear operating temperature conditions—the so-called heating dynamics—were applied to twelve sensors with sensing layers of different chemical composition. Nine gases (CO, CH\({}_{4}\), H\({}_{2}\), NH\({}_{3}\), NO, NO\({}_{2}\), H\({}_{2}\)S, SO\({}_{2}\), formaldehyde) in six different concentrations each were used as polluting admixtures to dry clean air. Due to the high complexity of the model describing the processes of interaction between gases and sensors, machine learning methods (logistic regression, random forest and gradient boosting) based on the use of physical experiment data were used to process the sensor response. Optimal heating dynamics and optimal machine learning methods for gas identification have been determined.