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Open AccessArticle

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

1
Department of Physics, Università degli Studi di Milano and INFN-Milan, Via Celoria 16, 20133, Milan, Italy
2
PM_TEN s.r.l., piazza della Vittoria 7/14, 16121 Genova, Italy
3
Department of Physics and INFN, University of Genoa, via Dodecaneso 33, 16146 Genova, Italy
*
Author to whom correspondence should be addressed.
now at: Laboratory of Atmospheric Chemistry (LAC), Paul Scherrer Institut (PSI), Aarebrücke, 5232 Villigen, Switzerland.
Atmosphere 2017, 8(11), 218; https://doi.org/10.3390/atmos8110218
Received: 15 October 2017 / Revised: 6 November 2017 / Accepted: 7 November 2017 / Published: 13 November 2017
(This article belongs to the Special Issue Carbonaceous Aerosols in Atmosphere)
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 babs measured at different wavelengths, and to provide the BrC Ångström Absorption exponent (αBrC). 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 αBrC and a better correlation of the BrC absorption coefficient (babsBrC) 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 αBrC 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 babs(BC) apportionment and EC measurements allowed MAC determination, which gave analogous results at the two sites. View Full-Text
Keywords: Multi-Wavelength Absorption Analyzer model; optical component apportionment; optical source apportionment; aerosol absorption coefficient; polar photometer; brown carbon; black carbon; Ångström Absorption exponent; light absorption; optical measurements of particle-loaded filters Multi-Wavelength Absorption Analyzer model; optical component apportionment; optical source apportionment; aerosol absorption coefficient; polar photometer; brown carbon; black carbon; Ångström Absorption exponent; light absorption; optical measurements of particle-loaded filters
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MDPI and ACS Style

Bernardoni, V.; Pileci, R.E.; Caponi, L.; Massabò, D. 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. Atmosphere 2017, 8, 218. https://doi.org/10.3390/atmos8110218

AMA Style

Bernardoni V, Pileci RE, Caponi L, Massabò D. 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. Atmosphere. 2017; 8(11):218. https://doi.org/10.3390/atmos8110218

Chicago/Turabian Style

Bernardoni, Vera; Pileci, Rosaria E.; Caponi, Lorenzo; Massabò, Dario. 2017. "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" Atmosphere 8, no. 11: 218. https://doi.org/10.3390/atmos8110218

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