Innovative Approaches to Industrial Odour Monitoring: From Chemical Analysis to Predictive Models

This study evaluated the reliability of an electronic nose in monitoring odour concentration near a wastewater treatment plant and examined the correlation between four sensor readings and odour intensity. The electronic nose chemical sensors are related to the concentration of the following chemical species: two values for the concentration of VOCs recorded via the PID sensor (VPID) and the EC sensor (VEC), and concentrations of sulfuric acid (VH2S) and benzene (VC6H6). Using Random Forest and least squares regression analysis, the study identifies VH2S and VC6H6 as key contributors to odour concentration (CcOD). Three Random Forest models (RF0, RF1, RF2), with different characteristics for splitting between the test set and the training set, were tested, with RF1 showing superior predictive performance due to its training approach. All models highlighted VH2S and VC6H6 as significant predictors, while VPID and VEC had less influence. A significant correlation between odour concentration and specific chemical sensor readings was found, particularly for VH2S and VC6H6. However, predicting odour concentrations below 1000 ouE/m3 proved challenging. Linear regression further confirmed the importance of VH2S and VC6H6, with a moderate R-squared value of 0.70, explaining 70% of the variability in odour concentration. The study demonstrated the effectiveness of combining Random Forest and least squares regression for robust and interpretable results. Future research should focus on expanding the dataset and incorporating additional variables to enhance model accuracy. The findings underscore the necessity of specific sensor training and standardised procedures for accurate odour monitoring and characterisation.