This paper presents application of an electronic nose prototype comprised of eight sensors, five TGS-type sensors, two electrochemical sensors and one PID-type sensor, to identify odour interaction phenomenon in two-, three-, four- and five-component odorous mixtures. Typical chemical compounds, such as toluene, acetone, triethylamine, α-pinene and n-butanol, present near municipal landfills and sewage treatment plants were subjected to investigation. Evaluation of predicted odour intensity and hedonic tone was performed with selected artificial neural network structures with the activation functions tanh and Leaky rectified linear units (Leaky ReLUs) with the parameter a=0.03. Correctness of identification of odour interactions in the odorous mixtures was determined based on the results obtained with the electronic nose instrument and non-linear data analysis. This value (average) was at the level of 88% in the case of odour intensity, whereas the average was at the level of 74% in the case of hedonic tone. In both cases, correctness of identification depended on the number of components present in the odorous mixture.

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This article addresses the problem of drinking water distribution system (DWDS) security in the terms of water quality which in the era of terrorist threat is of high importance to the public. The contribution of this paper is the development of the so called security module to extend a multi-species water quality model. This gives an insight to the situation in DWDS not only under normal operational conditions but also in case of a malicious attack on water quality. Moreover the security inputs are formally introduced to the model structure. This module enables simulation of both bacterial and/or chemical water contamination in DWDS environment. Previously defined inputs are utilised in proposed experiments by an attacking agent to influence the DWDS quality. The purpose of model development is to enable water scientists and water authorities to simulate the contamination propagation pathways and mechanisms throughout the network without omitting the 'natural' water chemistry effects. A simple simulation example for the exemplary DWDS illustrates the model performance for two distinct contamination scenarios.

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