Dust detection and analysis in museum environment based on pattern recognition

Air quality monitoring is an essential task in indoor environments, which require particular attention especially if they are affected by a considerable flow of people, as in museum environments. In the latter case, air quality is not only important for the health and safety of persons, but also for the protection of artworks, which may be damaged from dust, in the form of particulate or fibres. In this paper, we describe a new approach for the detection and analysis of dust by means of machine learning and pattern recognition. The proposed technique relies on a classification algorithm, which aims to identify the characteristics of dust especially in terms of shape and accumulation speed. This information is useful to design efficient countermeasures to reduce the harmful effects of dust and to determine its origin as well.