chemosensors Article Environmental Odour Quantification by IOMS: Parametric vs. Non-Parametric Prediction Techniques Tiziano Zarra * , Mark Gino K. Galang, Vincenzo Belgiorno and Vincenzo Naddeo   Citation: Zarra, T.; Galang, M.G.K.; Belgiorno, V.; Naddeo, V. Environmental Odour Quantification by IOMS: Parametric vs. Non-Parametric Prediction Techniques. Chemosensors 2021, 9, 183. https://doi.org/10.3390/ chemosensors9070183 Academic Editor: Pierluigi Barbieri Received: 18 June 2021 Accepted: 14 July 2021 Published: 16 July 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy; mgalang@unisa.it (M.G.K.G.); v.belgiorno@unisa.it (V.B.); vnaddeo@unisa.it (V.N.) * Correspondence: tzarra@unisa.it; Tel.: +39-089-969855 Abstract: Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instrumental odour monitoring systems (IOMS) are an intelligent technology that can be applied to continuously assess annoyance and thus avoid complaints. However, gaps to be improved in terms of accuracy in deciphering information, especially in the implementation of the mathematical model, are still being researched, especially in environmental odour monitoring applications. This research presents and discusses the implementation of traditional and innovative parametric and non-parametric prediction techniques for the elaboration of an effective odour quantification mon- itoring model (OQMM), with the aim of optimizing the accuracy of the measurements. Artificial neural network (ANN), multivariate adaptive regression splines (MARSpline), partial least square (PLS), multiple linear regression (MLR) and response surface regression (RSR) are implemented and compared for prediction of odour concentrations using an advanced IOMS. Experimental analyses are carried out by using real environmental odour samples collected from a municipal solid waste treatment plant. Results highlight the strengths and weaknesses of the analysed models and their accuracy in terms of environmental odour concentration prediction. The ANN application allows us to obtain the most accurate results among the investigated techniques. This paper provides useful information to select the appropriate computational tool to process the signals from sensors, in order to improve the reliability and stability of the measurements and create a robust prediction model. Keywords: signal processing; electronic nose; air quality monitoring; environmental modelling; municipal solid waste 1. Introduction Odour emissions from industrial sources in ambient air can be a significant problem for the exposed populations because they create an unpleasant environment as well as phys- ical and psychological disorders [1–4], which leads to public nuisance and complaints [5–8]. The presence of unpleasant odours is associated with the perception of a health risk [9,10]. Therefore, the control of odour emissions, starting from the characterisation, is a key issue in order to minimize the presence in ambient air and, thus, guarantee a suitable environ- mental quality [11–14]. Nowadays, environmental odour characterisation is conducted by analytical, sensorial and combined analytical-sensorial methods [4,15,16]. Analytical techniques employed mainly laboratory equipment (e.g., GC-MS, colorimetric method, catalytic, infrared and electrochemical sensors, differential optical absorption spectroscopy, fluorescence spectrometry) [1,4]. These techniques are able to detect single or multiple gaseous compounds which can be presumed as odour tracers, and quantifies them in terms of ppm or mg m −3 . On the other hand, sensorial techniques are methods in which odours are identified using the human nose [4,17]. Among the sensorial methods, Dynamic Olfac- tometry (DO) regulated by the EN13725: 2003, currently under review by the WG2 of the CEN/TC264 “Air quality”, is mostly used in Europe. The DO measures the odour concen- tration in terms of European Odour Unit per Cubic Meter (OU E m −3 )[18,19]. Meanwhile, Chemosensors 2021, 9, 183. https://doi.org/10.3390/chemosensors9070183 https://www.mdpi.com/journal/chemosensors