an official journal of: published by:
an official journal of: published by:
Editor in Chief: RAFFAELLO COSSU


  • Christian Ratti - Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Italy
  • Beatrice Julia Lotesoriere - Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Italy
  • Carmen Bax - Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Italy
  • Laura Capelli - Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Italy

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Copyright: © 2023 CISA Publisher


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.


Editorial History

  • Received: 15 Sep 2023
  • Revised: 29 Jan 2024
  • Accepted: 14 Feb 2024
  • Available online: 31 Mar 2024


Bax, C., Sironi, S., & Capelli, L. (2020a). Definition and application of a protocol for electronic nose field performance testing: Example of odor monitoring from a tire storage area. Atmosphere, 11(4).
DOI 10.3390/ATMOS11040426

Bax, C., Sironi, S., & Capelli, L. (2020b). How can odors be measured? An overview of methods and their applications. In Atmosphere (Vol. 11, Issue 1). MDPI AG.
DOI 10.3390/atmos11010092

Bland, J. M., & Altman, D. G. (1999). Measuring agreement in method comparison studies

Brancher, M., Griffiths, K. D., Franco, D., & de Melo Lisboa, H. (2017). A review of odour impact criteria in selected countries around the world. In Chemosphere (Vol. 168, pp. 1531–1570). Elsevier Ltd.
DOI 10.1016/j.chemosphere.2016.11.160

Cortes, C., Vapnik, V., & Saitta, L. (1995). Support-Vector Networks Editor. In Machine Leaming (Vol. 20). Kluwer Academic Publishers

De Vito, S., Karatzas, K., Bartonova, A., & Fattoruso, G. (n.d.). Environmental Informatics and Modeling Environmental Informatics and Modeling Air Quality Networks Data Analysis, Calibration & Data Fusion

John, A. T., Murugappan, K., Nisbet, D. R., & Tricoli, A. (2021). An outlook of recent advances in chemiresistive sensor-based electronic nose systems for food quality and environmental monitoring. In Sensors (Vol. 21, Issue 7). MDPI AG.
DOI 10.3390/s21072271

Kursa, M. B., & Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. In JSS Journal of Statistical Software (Vol. 36).

Oliva, G., Zarra, T., Pittoni, G., Senatore, V., Galang, M. G., Castellani, M., Belgiorno, V., & Naddeo, V. (2021). Next-generation of instrumental odour monitoring system (IOMS) for the gaseous emissions control in complex industrial plants. Chemosphere, 271.
DOI 10.1016/j.chemosphere.2021.129768

Panzitta, A., Bax, C., Lotesoriere, B. J., Ratti, C., & Capelli, L. (2022). Realisation of a Multi-Sensor System for Real-Time Monitoring of Odour Emissions at a Waste Treatment Plant. Chemical Engineering Transactions, 95, 139–144.
DOI 10.3303/CET2295024

Prudenza, S., Bax, C., & Capelli, L. (2023). Implementation of an electronic nose for real -time identification of odour emission peaks at a wastewater treatment plant. Heliyon, 9(10).
DOI 10.1016/j.heliyon.2023.e20437

Smola, A. J., Sch¨olkopf, B., & Sch¨olkopf, S. (2004). A tutorial on support vector regression *. In Statistics and Computing (Vol. 14). Kluwer Academic Publishers