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000006233 084__ $$2WoS$$aEngineering, Environmental
000006233 084__ $$2WoS$$aEnvironmental Sciences
000006233 1001_ $$0P:(DE-Juel1)4528$$aKiendler-Scharr, A.$$b0$$uFZJ
000006233 245__ $$aAerosol Mass Spectrometric Features of Biogenic SOA: Observations from a Plant Chamber and in Rural Atmospheric Environments
000006233 260__ $$aColumbus, Ohio$$bAmerican Chemical Society$$c2009
000006233 300__ $$a8166 - 8172
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000006233 440_0 $$01865$$aEnvironmental Science and Technology$$v43$$x0013-936X$$y21
000006233 500__ $$aWe gratefully acknowledge support by the European Commission (IP-EUCAARI, Contract No. 036833-2). Q.Z. was supported by the U.S. Department of Energy's Atmospheric Science Program (Office of Science, BER), Grant No. DE-FG02-08ER64627. We thank James Allan (U. Manchester) for the AMS data analysis software and anonymous reviewers for their constructive comments.
000006233 520__ $$aSecondary organic aerosol (SOA) is known to form from a variety of anthropogenic and biogenic precursors. Current estimates of global SOA production vary over 2 orders of magnitude. Since no direct measurement technique for SOA exists, quantifying SOA remains a challenge for atmospheric studies. The identification of biogenic SOA (BSOA) based on mass spectral signatures offers the possibility to derive source information of organic aerosol (OA) with high time resolution. Here we present data from simulation experiments. The BSOA from tree emissions was characterized with an Aerodyne quadrupole aerosol mass spectrometer (Q-AMS). Collection efficiencies were close to 1, and effective densities of the BSOA were found to be 1.3 +/- 0.1 g/cm(3). The mass spectra of SOA from different trees were found to be highly similar. The average BSOA mass spectrum from tree emissions is compared to a BSOA component spectrum extracted from field data. It is shown that overall the spectra agree well and that the mass spectral features of BSOA are distinctively different from those of OA components related to fresh fossil fuel and biomass combustions. The simulation chamber mass spectrum may potentially be useful for the identification and interpretation of biogenic SOA components in ambient data sets.
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000006233 588__ $$aDataset connected to Web of Science, Pubmed
000006233 650_2 $$2MeSH$$aAerosols: analysis
000006233 650_2 $$2MeSH$$aAtmosphere: chemistry
000006233 650_2 $$2MeSH$$aMass Spectrometry
000006233 650_2 $$2MeSH$$aOrganic Chemicals: analysis
000006233 650_2 $$2MeSH$$aParticle Size
000006233 650_2 $$2MeSH$$aTime Factors
000006233 650_2 $$2MeSH$$aTrees: chemistry
000006233 650_2 $$2MeSH$$aVolatilization
000006233 650_7 $$00$$2NLM Chemicals$$aAerosols
000006233 650_7 $$00$$2NLM Chemicals$$aOrganic Chemicals
000006233 650_7 $$2WoSType$$aJ
000006233 7001_ $$0P:(DE-HGF)0$$aZhang, Q.$$b1
000006233 7001_ $$0P:(DE-Juel1)VDB72897$$aHohaus, Th.$$b2$$uFZJ
000006233 7001_ $$0P:(DE-Juel1)129345$$aKleist, E.$$b3$$uFZJ
000006233 7001_ $$0P:(DE-Juel1)VDB66036$$aMensah, A.$$b4$$uFZJ
000006233 7001_ $$0P:(DE-Juel1)16346$$aMentel, T. F.$$b5$$uFZJ
000006233 7001_ $$0P:(DE-Juel1)VDB73341$$aSpindler, C.$$b6$$uFZJ
000006233 7001_ $$0P:(DE-Juel1)VDB56253$$aUerlings, R.$$b7$$uFZJ
000006233 7001_ $$0P:(DE-Juel1)5344$$aTillmann, R.$$b8$$uFZJ
000006233 7001_ $$0P:(DE-Juel1)VDB1780$$aWildt, J.$$b9$$uFZJ
000006233 773__ $$0PERI:(DE-600)1465132-4$$a10.1021/es901420b$$gVol. 43, p. 8166 - 8172$$p8166 - 8172$$q43<8166 - 8172$$tEnvironmental Science & Technology$$v43$$x0013-936X$$y2009
000006233 8567_ $$uhttp://dx.doi.org/10.1021/es901420b
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