PM10 source apportionment in Milan (Italy) using time-resolved data

Bernardoni, V.; Vecchi, R.; Valli, G.; Piazzalunga, A.; Fermo, P.

doi:10.1016/j.scitotenv.2011.07.048

Cited by 107 publications

(69 citation statements)

References 32 publications

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“…Non-negligible contribution resulting from OC NC was present (25% ± 9%), and it is expected to be related to secondary and/or natural contributions. OC FF /EC FF ratio was 1.4 ± 0.3, which is in agreement with, e.g., real-world profiles for the traffic source obtained by PMF in Milan [40]. OC WB /EC WB ratio in Propata was 5.0 ± 1.2.…”

Section: Results From the Propata Datasetsupporting

confidence: 89%

“…From optical apportionment (Equation (6)), EC resulted mainly of fossil origin in most cases (range 62-87%, except three cases in the range 42-44%), consistent with 14 C studies in the Milan area [40] and in other cities in Europe during wintertime [62,64]. As already found between b abs BrC and levoglucosan, very good correlation was found also between b abs,WB BC and levoglucosan (R 2 = 0.92).…”

Section: Results From the Milan Datasetsupporting

confidence: 85%

“…Possible reasons could be the more complex mixture of sources present in Milan and the aging processes [40,42,44], which can affect aerosol composition, particle mixing, and size distribution. Small variations in BrC properties related to these factors could have influenced the MWAA model results, leading to a higher percent of cases in which α BrC was affected by high uncertainties.…”

Section: Discussionmentioning

confidence: 99%

“…Milan is situated in the Po Valley, which is one of the main hot-spot pollution areas in Europe. For this reason, different source apportionment studies by receptor models based on chemical characterization were carried out in the last decade on integrated aerosol size-fractions (e.g., [39][40][41][42]) and on suitably characterized [43] size-segregated samples [44]. These works evidenced that the site is impacted by a complex mixture of sources; traffic (both exhaust and non-exhaust), wood burning, re-suspended dust, and industry were identified as the main primary sources, but secondary compounds play a key role in explaining the total measured mass.…”

Section: Sites and Samplingmentioning

confidence: 99%

“…Thus, α WB = 1.8 was used also in this work for the Propata dataset. In the case of Milan, performing the same choice led to an unreasonably high contribution from wood burning compared to previous studies based on 14 C measurements [41] or Positive Matrix Factorization on well chemically characterized samples [40,42]. So, we finally chose α WB = 2 in Milan.…”

Section: Optical Source and Component Apportionment: The Multi-wavelementioning

confidence: 99%

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The Multi-Wavelength Absorption Analyzer (MWAA) Model as a Tool for Source and Component Apportionment Based on Aerosol Absorption Properties: Application to Samples Collected in Different Environments

Bernardoni

Pileci

Caponi³

et al. 2017

Atmosphere

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Abstract:The multi-wavelength absorption analyzer model (MWAA model) was recently proposed to provide a source (fossil fuel combustion vs. wood burning) and a component (black carbon BC vs. brown carbon BrC) apportionment of b abs measured at different wavelengths, and to provide the BrC Ångström Absorption exponent (α BrC ). This paper shows MWAA model performances and issues when applied to samples impacted by different sources. To this aim, the MWAA model was run on samples collected at a rural (Propata) and an urban (Milan) site in Italy during the winter period. Lower uncertainties on α BrC and a better correlation of the BrC absorption coefficient (b abs BrC ) with levoglucosan (tracer for wood burning) were obtained in Propata (compared to Milan).Nevertheless, the correlation previously mentioned improved, especially in Milan, when providing a priori information on α BrC to MWAA. Possible reasons for this improvement could be the more complex mixture of sources present in Milan and the aging processes, which can affect aerosol composition, particle mixing, and size distribution. OC and EC source apportionment showed that wood burning was the dominating contributor to the carbonaceous fractions in Propata, whereas a more complex situation was detected in Milan. Simultaneous b abs (BC) apportionment and EC measurements allowed MAC determination, which gave analogous results at the two sites.

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Section: Results From the Propata Datasetsupporting

confidence: 89%