Virtual sensor array as a tool for classifying air pollution

This work examined the acquisition of information about gases using a virtual sensor array and classification. We were particularly interested in the approach in which classes are defined in a qualitative–quantitative manner, that is, by identifying the gas and concentration range. This type of information will be of interest for air pollution assessment purposes. In this field of application, it is often not necessary to provide very precise information. The idea of the virtual sensor array exploits the dependence of a gas sensor’s response on operating conditions. Originally it was developed as a means to improve the selectivity of an electronic nose when energy consumption by this device was a serious limitation. If the response of one sensor is measured in n time points, and each time point is characterised by different controlled exposure conditions, the sensor becomes analogous to an n-dimensional virtual sensor array. Compared with conventional approaches, a virtual sensor array based on a single sensor offers low power consumption, low volume, and low cost, which opens up new markets for wide application of portable and handheld devices. In this article, we show that a virtual sensor array and classification may serve as a reliable source of qualitative–quantitative information about gases. Twenty-six classes (five substances, each at five concentration ranges, and pure air) were recognised with a true positive rate higher than 99.14 ± 0.49% and a true negative rate higher than 99.21 ± 0.52%. As demonstrated, the basis for recognition could be a virtual sensor array associated with a low-power consuming sensor (210–280 mW). The complexity of the applied classifier could be adjusted depending on the choice of sensor operating conditions. For a complex classifier like support vector machine, dynamic exposure was sufficient to obtain high classification performance. A simpler classifier like k-nearest neighbours required more information, that is, information associated with static as well as dynamic exposure.