Indoor Air Quality Monitoring System with High Accuracy of Gas Classification and Concentration Prediction via Selective Mechanism Research

The efficacy of sensors, particularly sensor arrays, lies in their selectivity. However, research on selectivity remains notably obscure and scarce. In this work, indoor pollutants (C₇H₈, HCHO, CH₄, and NO₂) were chosen as the target gas. Following the screening of six oxides from previous work, temperature-programmed desorption/reduction experiments were conducted to delve into the origins of selectivity. The results explicate the superiority of NiO in detecting toluene and unveil the distinctive NO₂ sensing mechanism of WO₃ sensors. Based on the sensor array comprising these oxides, it can clearly detect low concentrations of C₇H₈ (<i>S</i> = 1.6 to 50 ppb), HCHO (<i>S</i> = 1.4 to 50 ppb), and NO₂ (<i>S</i> = 3.3 to 50 ppb), which satisfies the requisites of indoor air monitoring. Meanwhile, three machine learning models (Extreme Gradient Boosting, Support Vector Machine, and Back Propagation Neural Network) are employed for gas classification. The classification accuracies of these models are 95.45%, 100%, and 100%, while the <i>R</i>² values of the concentration prediction are 99.65%, 94.9%, and 98.04%, respectively, indicating the rationality of material selection. Furthermore, it can still achieve relatively high accuracy in gas classification (94.12%) and concentration prediction (89.36%), even for gas mixtures of four gases. Finally, an indoor air quality monitoring system is developed, which enables real-time monitoring of indoor gas quality through the Internet of Things.