Prediction of Atmospheric Ozone Concentrations with a Temperature-Modulated Gas Sensor Array

Abstract

Ozone is one of the most important pollutant gases. The excellent sensitivity and low limit of detection of gas sensors based on Semiconducting Metal Oxides (SMOXs) make them ideal candidates to accurately monitor outdoor air quality. We present a convolutional neural network (CNN) architecture that is trained on the resistance readout of a multi-pixel SMOX gas sensor array operated in temperature modulation. The trained model outperforms a ridge regressor in the quantification of ozone concentrations in real outdoor air.

Keywords

• gas sensor
• SMOX
• temperature modulation
• convolutional neural network
• outdoor air quality