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 4 2-), 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.
PM10 source apportionment in Milan (Italy) using time-resolved data / V. Bernardoni, R. Vecchi, G. Valli, A. Piazzalunga, P. Fermo. - In: SCIENCE OF THE TOTAL ENVIRONMENT. - ISSN 0048-9697. - 409:22(2011), pp. 4788-4795.
PM10 source apportionment in Milan (Italy) using time-resolved data
V. Bernardoni
;R. VecchiSecondo
;G. Valli;P. FermoUltimo
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 4 2-), 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.File | Dimensione | Formato | |
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