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PM source apportionment by Positive Matrix Factorization (PMF) using an extended aerosol chemical characterization including specific molecular markers

Abstract : Airborne PM pollution has emerged out as a critical issue all across the world. Quantitative and qualitative source analysis is becoming imperative to imply effective emission control strategies to reduce ambient air pollutants. Receptor oriented models, based on the statistical approach, have been developed to analyze various characteristics of the pollutants measured at the receptor site and to estimate their contributions to the source. Among the multivariate statistical receptor models used for PM source apportionment, Positive Matrix Factorization (PMF) has been adopted world wide as one of the most convenient technique. PMF has a non negative constraint and is able to quantify the factor contribution directly without a subsequent use of multiple regression analysis. More than 40% of European source apportionment studies have applied PMF (Belis et al. 2013). Recent advancements have proposed the use of new organic molecular markers in PMF to better investigate the contribution of biogenic and/or secondary organic aerosols. It has been observed that the use of these compounds improves the efficacy of PM source apportionment (Waked et al. 2014). The main objective of this study was to apportion specific PM10 sources, by using a wide variety of such organic molecular markers as PMF input data, for samples collected at an urban station “Les Frenes” of a local air quality network (Air Rhône-Alpes), considered as representative of a densely populated urban area Grenoble (France). PM10 samples were collected every third day (24 h-basis sampling) on quartz filters over a one year period (2013) and extended chemical characterization was performed including the quantification of species such as OC/EC, ions/cations (Na+, Mg2+, NH4+, Cl-, SO42-, NO3-), Polycyclic Aromatic Hydrocarbon (PAH), oxy-PAH, nitro-PAH, polyols (arabitol, mannitol), Methane Sulfonic Acid (MSA), levoglucosan, sulfur-containing PAH (Benzo[b]naphtha[2,1-d]thiophene, BNT), oxalate, higher odd number alkanes (C27, C29, C31), metals (Ba, Cu, Cr, Zn, Sb, Ni, V, Al, Ti, Fe, Mn, Rb, Ca, K). Results showed that the 10-factor profiles have given the best fit in the PMF analysis including biogenic emissions (marine, soil, plant debris), secondary inorganic (nitrate and sulfate factors) and organic aerosols, dust and aged sea salt particles and anthropogenic sources (oil combustion, traffic exhaust, biomass burning, industry…) (Figure 1). The highest percentage contribution to PM is made by secondary inorganic aerosol (~20%). It is interesting to note that Secondary PAH-aerosol factor accounts for ~ 4%. Discussion will further underline the factor contribution on seasonal basis and the stability of the chosen solution.
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Submitted on : Tuesday, August 7, 2018 - 2:24:34 PM
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  • HAL Id : ineris-01855119, version 1

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Deepchandra Srivastava, Frédéric Masson, S. Ngo, Antoine Waked, Jean-Luc Jaffrezo, et al.. PM source apportionment by Positive Matrix Factorization (PMF) using an extended aerosol chemical characterization including specific molecular markers. European Aerosol Conference (EAC 2015), Sep 2015, Milan, Italy. ⟨ineris-01855119⟩

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