In this work Positive Matrix Factorization (PMF) was applied to 4-hour resolved PM10 data collected in Milan (Italy) during summer and winter 2006. PM10 characterisation included elements (Mg-Pb), main inorganic ions (NH 4+, NO 3-, SO 42-), levoglucosan and its isomers (mannosan and galactosan), and organic and elemental carbon (OC and EC).PMF resolved seven factors that were assigned to construction works, re-suspended dust, secondary sulphate, traffic, industry, secondary nitrate, and wood burning. Multi Linear Regression was applied to obtain the PM10 source apportionment. The 4-hour temporal resolution allowed the estimation of the factor contributions during peculiar episodes, which would have not been detected with the traditional 24-hour sampling strategy. © 2011 Elsevier B.V.
Bernardoni, V., Vecchi, R., Valli, G., Piazzalunga, A., Fermo, P. (2011). PM10 source apportionment in Milan (Italy) using time-resolved data. SCIENCE OF THE TOTAL ENVIRONMENT, 409(22), 4788-4795 [10.1016/j.scitotenv.2011.07.048].
PM10 source apportionment in Milan (Italy) using time-resolved data
PIAZZALUNGA, ANDREA;
2011
Abstract
In this work Positive Matrix Factorization (PMF) was applied to 4-hour resolved PM10 data collected in Milan (Italy) during summer and winter 2006. PM10 characterisation included elements (Mg-Pb), main inorganic ions (NH 4+, NO 3-, SO 42-), levoglucosan and its isomers (mannosan and galactosan), and organic and elemental carbon (OC and EC).PMF resolved seven factors that were assigned to construction works, re-suspended dust, secondary sulphate, traffic, industry, secondary nitrate, and wood burning. Multi Linear Regression was applied to obtain the PM10 source apportionment. The 4-hour temporal resolution allowed the estimation of the factor contributions during peculiar episodes, which would have not been detected with the traditional 24-hour sampling strategy. © 2011 Elsevier B.V.
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