PCA- and PMF-based methodology for air pollution sources identification and apportionment

Air pollution is a wide concern for human health and requires the development of air quality control strategies. In order to achieve this goal pollution sources have to be accurately identified and quantified. The case study presented in this paper is part of a scientific project initiated by the French Ministry of Ecology and Sustainable Development. For the following study measurements of chemical composition data for particles have been conducted on a French urban site. The first step of the study consists in the identification of the sources profiles which is achieved through principal component analysis (PCA) completed by a rotation technique. Then the apportionment of the sources is evaluated with a receptor modeling using positive matrix factorization (PMF) as estimation method. Finally the joint use of these two statistical methods enables to characterize and apportion five different sources of fine particulate emission. Copyright (C) 2008 John Wiley & Sons, Ltd.