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Comparison of receptor models for source apportionment of the PM10 in Zaragoza (Spain)

AuthorsCallén Romero, Mª Soledad ; Cruz Eiriz, M. T. de la; López Sebastián, José Manuel ; Navarro López, María Victoria ; Mastral Lamarca, Ana María
Receptor modelling
Issue Date13-May-2009
CitationChemosphere 76(8): 1120-1129 (2009)
AbstractReceptor models are useful to understand the chemical and physical characteristics of air pollutants by identifying their sources and by estimating contributions of each source to receptor concentrations. In this work, three receptor models based on principal component analysis with absolute principal component scores (PCA-APCS), Unmix and Positive Matrix Factorization (PMF) were applied to study for the first time the apportionment of the airborne particulate matter less or equal than 10 m (PM10) in Zaragoza, Spain, during one year sampling campaign (2003-2004). The PM10 samples were characterized regarding their concentrations in inorganic components: trace elements and ions and also organic components: polycyclic aromatic hydrocarbons (PAH) not only in the solid phase but also in the gas phase. A comparison of the three receptor models was carried out in order to do a more robust characterization of the PM10. The three models predicted that the major sources of PM10 in Zaragoza were related to natural sources (60%, 75% and 47% respectively for PCA-APCS, Unmix and PMF) although anthropogenic sources also contributed to PM10 (28%, 25% and 39%). With regard to the anthropogenic sources, while PCA and PMF allowed high discrimination in the sources identification associated with different combustion sources such as traffic and industry, fossil fuel, biomass and fuel oil combustion, heavy traffic and evaporative emissions, the Unmix model only allowed the identification of industry and traffic emissions, evaporative emissions and heavy duty vehicles. The three models provided good correlations between the experimental and modelled PM10 concentrations with major precision and the closest agreement between the PMF and PCA models.
Publisher version (URL)http://dx.doi.org/10.1016/j.chemosphere.2009.04.015
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