Waste treatment plants (WTPs) often generate odours that may cause nuisance to citizens living nearby. In general, people are becoming more sensitive to environmental issues, and particularly to odour pollution. Instrumental Odour Monitoring Systems (IOMSs) represent an emerging tool for continuous odour measurement and real-time identification of odour peaks, which can provide useful information about the process operation and indicate the occurrence of anomalous conditions likely to cause odour events in the surrounding territories. This paper describes the implementation of two IOMSs at the fenceline of a WTP, focusing on the definition of a specific experimental protocol and data processing procedure for dealing with the interferences of humidity and temperature affecting sensors’ responses. Different approaches for data processing were compared and the optimal one was selected based on field performance testing. The humidity compensation model developed proved to be effective, bringing the IOMS classification accuracy above 95%. Also, the adoption of a class-specific regression model compared to a global regression model resulted in an odour quantification capability comparable with those of the reference method (i.e., dynamic olfactometry). Lastly, the validated models were used to process the monitoring data over a period of about one year.

Implementation of electronic noses at the fenceline of industrial plants is becoming relevant in order to monitor odour emissions from these facilities. Literature studies proved the applicability of these instruments for this purpose; however, their application over long period of time is hindered by the presence of interferences. We propose the application of two pre-processing strategies to compensate the presence of humidity variations and sensor drift, two interferents commonly found in uncontrolled sampling systems. We validated the methodology on two electronic noses installed at the fenceline of a waste treatment plant during 16 months. Different classification models have been developed based on different data pre-treatment and their performance was evaluated through field performance testing. Results show that the implemented methodology effectively reduces drift effects and improves classification accuracy for different odour sources, enhancing the feasibility of these instruments for industrial odour monitoring.

The most recent Best Available Techniques (BAT) for waste treatment plants indicate odour as environmental pollutants to be monitored and controlled. In Italy, the prescription of permanent installations of electronic noses (or Industrial Odour Monitoring Systems - IOMS) in the environmental permit of waste treatment plants (WTPs) is becoming more and more frequent. IOMS are intended to provide a real-time estimation of the odour concentration at the fenceline of the plant. Although this type of IOMS application is becoming very common, it is far from being state-of-the-art. In this context, this paper describes a research project aimed at the implementation at the fenceline of a WTP of an IOMS network, comprising two IOMS and a meteorological station. The paper focuses on presenting the experimental procedure involved for training IOMS and verifying their performance in the field. 5 olfactometric campaigns were carried out at the plant to build IOMS calibration models aimed at WTP odours detection, classification, and quantification. Results of field performance testing proved, with classification accuracies above 95% achieved by both IOMS, a very good capability of properly trained IOMS to recognize WTP odours. Moreover, they pointed out a great agreement between the estimations of the odour concentration provided by the IOMS and the odour concentration assessed by dynamic olfactometry, thereby boosting the research in this field. Finally, the paper reports the results of 1-year monitoring at the WTP with the purpose of evaluating the possibility to define an alarm threshold for the odour concentration.

The realisation of an electronic nose network for the monitoring of the odour concentrations at the fenceline of a waste treatment plant has been investigated. One of the main challenges for instrumental environmental monitoring concerns the interference of the daily variability of humidity and temperature of the ambient air to which sensors are exposed. In this study, we propose a solution to overcome this problem, implementing a specific normalisation pretreatment into the data processing procedure. The preliminary results obtained show a better classification and quantification performance whenever humidity model compensation is applied.

Implementation of electronic noses at the fenceline of industrial plants is becoming relevant in order to monitor odour emissions from these facilities. Literature studies proved the applicability of these instruments for this purpose; however, their application over long period of time is hindered by the presence of interferences. We propose the application of two pre-processing strategies to compensate the presence of humidity variations and sensor drift, two interferents commonly found in uncontrolled sampling systems. We validated the methodology on two electronic noses installed at the fenceline of a waste treatment plant during 16 months. Different classification models have been developed based on different data pre-treatment and their performance was evaluated through field performance testing. Results show that the implemented methodology effectively reduces drift effects and improves classification accuracy for different odour sources, enhancing the feasibility of these instruments for industrial odour monitoring.