Unwanted odour emissions are considered air pollutants that may cause detrimental impacts to the environment as well as an indicator of unhealthy air to the affected individuals resulting in annoyance and health related issues. These pollutants are challenging to handle due to their invisibility to the naked eye and can only be felt by the human olfactory stimuli. A strategy to address this issue is by introducing an intelligent processing system to odour monitoring instrument such as artificial neural network to achieve a robust result. In this paper, a review on the application of artificial neural network for the management of environmental odours is presented. The principal factors in developing an optimum artificial neural network were identified as elements, structure and learning algorithms. The management of environmental odour has been distinguished into four aspects such as measurement, characterization, control and treatment and continuous monitoring. For each aspect, the performance of the neural network is critically evaluated emphasizing the strengths and weaknesses. This work aims to address the scarcity of information by addressing the gaps from existing studies in terms of the selection of the most suitable configuration, the benefits and consequences. Adopting this technique could provide a new avenue in the management of environmental odours through the use of a powerful mathematical computing tool for a more efficient and reliable outcome.

The release of air pollutants from the operation of wastewater treatment plants (WWTPs) is often a cause of odor annoyance for the people living in the surrounding area. Odors have been indeed recently classified as atmospheric pollutants and are the main cause of complaints to local authorities. In this context, the implementation of effective treatment solutions is of key importance for urban water cycle management. This work presents a critical review of the state of the art of odor treatment technologies (OTTs) applied in full-scale WWTPs to address this issue. An overview of these technologies is given by discussing their strengths and weaknesses. A sensitivity analysis is presented, by considering land requirements, operational parameters and efficiencies, based on data of full-scale applications. The investment and operating costs have been reviewed with reference to the different OTTs. Biofilters and biotrickling filters represent the two most applied technologies for odor abatement at full-scale plants, due to lower costs and high removal efficiencies. An analysis of the odors emitted by the different wastewater treatment units is reported, with the aim of identifying the principal odor sources. Innovative and sustainable technologies are also presented and discussed, evaluating their potential for full-scale applicability.

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.