Abstract
Odour nuisance is an increasingly topical problem, especially in newly developed urban areas. The use of machine learning algorithms for the classification and quantification of odour sources is becoming more and more widespread using instrumental odour monitoring systems (IOMS) for odour measurements. In this context of odour nuisances, the role of citizens can represent one of the fundamental factors for controlling the environment as several studies have already stressed: citizen science is now considered an additional tool for the smart management of environmental monitoring since it is able to carry out an in-depth analysis of the pollution problem. This paper presents a continuous monitoring study at the fenceline of three urban wastewater treatment plants. The study was based on two distinct elements: firstly, continuous monitoring was used at the plant fenceline using instrumental odour monitoring systems (IOMS) for odour measurements. Once a database with all IOMS data was obtained, odour classification and quantification algorithms were developed via machine learning techniques, such as Artificial Neural Networks (ANNs) and random forest, which were used to set-up a system capable of automatically recognizing both the odour class and concentration. Then, citizen science was used, by employing the data derived from an app available for the citizens: the app was set up in a way that citizens could enter the type and intensity of the smell they detected so each report would be recorded with GPS location, date, time and weather data allowing a comprehensive data mapping across space and time. We carried out a monitoring campaign over a period of five months, and then we compared the data obtained from the algorithms with the reports of the citizens, then studying the actual causes of the nuisances and verifying whether they were related to the monitored plant. At first, we carried out an analysis of the results provided by the IOMS, so that we could identify the most frequent odour classes and relative odour concentrations: it was decided to investigate different ranges of odour concentration to verify which sources were most influential in the most intense episodes of nuisance. Then, we correlated such information with the weather data and citizens’ reports, to find out whether the reports were related to the plant. The description of the odours perceived by the population, alongside the identification of the appropriate wind cone influencing the receptors from the plant, allowed us to identify the events that could be attributed to the known sources. The results obtained from joint analysis of IOMS and citizens data were therefore useful for establishing to what extent the unpleasant odours perceived by the citizens came from the monitored plant.
