Pollution sources detection via principal component analysis and rotation

Marie Chavent (1, 2) , Hervé Guegan (3, 3) , Vanessa Kuentz (1, 2) , Brigitte Patouille (1) , Jérôme Saracco (2, 4)

1 IMB - Institut de Mathématiques de Bordeaux
2 CQFD - Quality control and dynamic reliability
3 CENBG - Centre d'Etudes Nucléaires de Bordeaux Gradignan
4 GREThA - Groupe de Recherche en Economie Théorique et Appliquée

Air pollution is a widely preoccupation which needs the development of control strategies. To reach this goal, pollution sources have to be precisely identified. Principal component analysis is a possible response to this problem. Indeed this factorial method enables to detect sources, that is to have a qualitative description of them. In this work, techniques of rotation are a useful help for the association of variables with factors. We highlight the fact that the rotation must be applied to the standardized principal components, so as to keep good interpretation properties. This methodology has then been applied to a problem of air pollution on a french site.

Mots clés: Factor Analysis; Rotation; Pollution data

Domaines: Applications [stat.AP]; Méthodologie [stat.ME]

Marie Chavent, Hervé Guegan, Vanessa Kuentz, Brigitte Patouille, Jérôme Saracco. Pollution sources detection via principal component analysis and rotation. ASMDA 2007, May 2007, Chania, Crète, Greece. ⟨hal-00278289⟩