Indoor air pollutant sources using blind source separation methods

Rachid Ouaret (1, 2) , Anda Ionescu (1) , Olivier Ramalho (3) , Yves Candau (1)

1 CERTES EA 3481 - Centre d'Etudes et Recherches en Thermique, Environnement et Systèmes [Créteil]
2 Toulouse INP - Institut National Polytechnique (Toulouse)
3 CSTB - Centre Scientifique et Technique du Bâtiment

The objective of this study is to separate different sources of variability of air pollutant concentrations time series of particulate matter (PM) monitored in real indoor environments. Different blind source separation (BSS) methods (ICA, PMF, NMF) were applied in order to identify the PM sources and their contributions. The source profiles were characterized by their autocorrelation functions (ACF) which were compared to the ACFs of other variables. Their interpretation was completed by the analysis of polar plots including exogenous factors. Source contributions were also quantified.

Domaines: Statistiques [math.ST]; Intelligence artificielle [cs.AI]; Ingénierie de l'environnement; Autres [stat.ML]; Théorie [stat.TH]

Rachid Ouaret, Anda Ionescu, Olivier Ramalho, Yves Candau. Indoor air pollutant sources using blind source separation methods. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning., Apr 2017, Bruges, Belgium. ⟨hal-01721960⟩