PCA and PMF based methodology for air pollution sources identification and apportionment
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
KUENTZ, Vanessa
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
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Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
KUENTZ, Vanessa
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
SARACCO, Jérôme
Quality control and dynamic reliability [CQFD]
Groupe de Recherche en Economie Théorique et Appliquée [GREThA]
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Quality control and dynamic reliability [CQFD]
Groupe de Recherche en Economie Théorique et Appliquée [GREThA]
Langue
en
Article de revue
Ce document a été publié dans
Environmetrics. 2009, vol. 20, p. 928-942
Wiley
Résumé en anglais
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 ...Lire la suite >
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 completed by a rotation technique. Then the apportionment of the sources is evaluated with a receptor modeling using Positive Matrix Factorization as estimation method. Finally the joint use of these two statistical methods enables to characterize and apportion five different sources of fine particulate emission.< Réduire
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