The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Inter-comparison of source apportionment models for the estimation of wood burning aerosols during wintertime in an Alpine city (Grenoble, France)
Open accessAbstract
Abstract. The emission of organic aerosols (OA) in the ambient air by residential wood burning is nowadays a subject of great scientific concern and a growing number of studies aim at apportioning the influence of such emissions on urban air quality. In the present study, results obtained using two commonly-used source apportionment models, i.e., Chemical Mass Balance (CMB, performed with off-line filter measurements) and Positive Matrix Factorization (PMF, applied to Aerosol Mass Spectrometer measurements), as well as using the recently-proposed Aethalometer model (based on the measurement of the aerosol light absorption at different wavelengths) are inter-compared. This work is performed using field data obtained during the winter season (14 to 29 January 2009) at an urban background site of a French Alpine city (Grenoble). Converging results from the different models indicate a major contribution of wood burning organic aerosols (OMwb) to the ambient aerosol organic fraction, with mean OMwb contributions to total OA of 68%, 61% and 37% for the CMB, the Aethalometer and the AMS-PMF models respectively, during the period when the three modelling studies overlapped (12 days). Quantitative discrepancies might notably be due to the overestimation of OMwb calculated by the CMB due to the loss of semi-volatile compounds from sources to receptor site, as well as to the accounting of oxidized primary wood burning organic (OPOAwb) aerosols within the Oxygenated Organic Aerosol (OOA) PMF-factor. This OOA factor accounts on average for about 50% of total OM, while non-combustion sources contribute to about 25% and 28% of total OM according to the CMB and Aethalometer models respectively. Each model suggests a mean contribution of fossil fuel emissions to total OM of about 10%. A good agreement is also obtained for the source apportionment of elemental carbon (EC) by both the CMB and the Aethalometer models, with fossil fuel emissions representing on average more than 80% of total EC.
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Cites
Paatero, P., & Tapper, U. (1994) · Environmetrics · doi:10.1002/env.3170050203 · Cited by 6,023
Evolution of Organic Aerosols in the Atmosphere
Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang, Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., et al. (2009) · Science · doi:10.1126/science.1180353 · Cited by 4,167
Light Absorption by Carbonaceous Particles: An Investigative Review
Bond, T. C., & Bergstrom, R. W. (2006) · Aerosol Science and Technology · doi:10.1080/02786820500421521 · Cited by 2,980
Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H., Ulbrich, I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L., et al. (2007) · Geophysical Research Letters · doi:10.1029/2007gl029979 · Cited by 2,452
Turpin, B. J., & Lim, H.-J. (2001) · Aerosol Science and Technology · doi:10.1080/02786820119445 · Cited by 2,036
Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols
Andreae, M. O., & Gelencsér, A. (2006) · Atmospheric Chemistry and Physics · doi:10.5194/acp-6-3131-2006 · Cited by 2,006
Elemental Carbon-Based Method for Monitoring Occupational Exposures to Particulate Diesel Exhaust
Birch, M. E., & Cary, R. A. (1996) · Aerosol Science and Technology · doi:10.1080/02786829608965393 · Cited by 1,783
Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon
Kirchstetter, T. W., Novakov, T., & Hobbs, P. V. (2004) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2004jd004999 · Cited by 1,608
Ulbrich, I. M., Canagaratna, M. R., Zhang, Q., Worsnop, D. R., & Jimenez, J. L. (2009) · Atmospheric Chemistry and Physics · doi:10.5194/acp-9-2891-2009 · Cited by 1,534
Coupled Partitioning, Dilution, and Chemical Aging of Semivolatile Organics
Donahue, N. M., Robinson, A. L., Stanier, C. O., & Pandis, S. N. (2006) · Environmental Science & Technology · doi:10.1021/es052297c · Cited by 1,525
Cited by
An overview of particulate emissions from residential biomass combustion
Vicente, E. D., & Alves, C. A. (2018) · Atmospheric Research · doi:10.1016/j.atmosres.2017.08.027 · Cited by 339
Waked, A., Favez, O., Alleman, L. Y., Piot, C., Petit, J.-E., Delaunay, T., Verlinden, E., Golly, B., Besombes, J.-L., Jaffrezo, J.-L., et al. (2014) · Atmospheric Chemistry and Physics · doi:10.5194/acp-14-3325-2014 · Cited by 294
Self-citationLevoglucosan as a tracer of biomass burning: Recent progress and perspectives
Bhattarai, H., Saikawa, E., Wan, X., Zhu, H., Ram, K., Gao, S., Kang, S., Zhang, Q., Zhang, Y., Wu, G., et al. (2019) · Atmospheric Research · doi:10.1016/j.atmosres.2019.01.004 · Cited by 280
Metadata
- Authors
- Favez, O., El Haddad, I., Piot, C., Boréave, A., Abidi, E., Marchand, N., Jaffrezo, J.-L., Besombes, J.-L., Personnaz, M.-B., Sciare, J., Wortham, H., George, C., & D'Anna, B.
- Venue
- Atmospheric Chemistry and Physics
- Publisher
- Copernicus GmbH
- Published
- 2010-06-16
- Volume
- 10
- Issue
- 12
- Pages
- 5295-5314
- ISSN
- 1680-7316 (ISSN-L)
- 1680-7324
- OpenAlex
- W2110596142
- Cited by
- 320
- References
- 67
- FWCI
- 9.75
- Type
- journal article
- Language
- en
- Open access
- gold
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- Favez, O., El Haddad, I., Piot, C., Boréave, A., Abidi, E., Marchand, N., Jaffrezo, J.-L., Besombes, J.-L., Personnaz, M.-B., Sciare, J., Wortham, H., George, C., & D'Anna, B. (2010). Inter-comparison of source apportionment models for the estimation of wood burning aerosols during wintertime in an Alpine city (Grenoble, France). Atmospheric Chemistry and Physics. https://doi.org/10.5194/acp-10-5295-2010
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