Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.509 IF 5.509
  • IF 5-year value: 5.689 IF 5-year
    5.689
  • CiteScore value: 5.44 CiteScore
    5.44
  • SNIP value: 1.519 SNIP 1.519
  • SJR value: 3.032 SJR 3.032
  • IPP value: 5.37 IPP 5.37
  • h5-index value: 86 h5-index 86
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 161 Scimago H
    index 161
ACP | Articles | Volume 19, issue 2
Atmos. Chem. Phys., 19, 973-986, 2019
https://doi.org/10.5194/acp-19-973-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 19, 973-986, 2019
https://doi.org/10.5194/acp-19-973-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 24 Jan 2019

Research article | 24 Jan 2019

Positive matrix factorization of organic aerosol: insights from a chemical transport model

Anthoula D. Drosatou et al.
Related authors  
Simulation of the chemical evolution of biomass burning organic aerosol
Georgia N. Theodoritsi and Spyros N. Pandis
Atmos. Chem. Phys., 19, 5403-5415, https://doi.org/10.5194/acp-19-5403-2019,https://doi.org/10.5194/acp-19-5403-2019, 2019
Short summary
Summertime aerosol volatility measurements in Beijing, China
Weiqi Xu, Conghui Xie, Eleni Karnezi, Qi Zhang, Junfeng Wang, Spyros N. Pandis, Xinlei Ge, Qingqing Wang, Jian Zhao, Wei Du, Yanmei Qiu, Wei Zhou, Yao He, Jingwei Zhang, Junling An, Ying Li, Jie Li, Pingqing Fu, Zifa Wang, Douglas R. Worsnop, and Yele Sun
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-135,https://doi.org/10.5194/acp-2019-135, 2019
Manuscript under review for ACP
Short summary
Particle wall-loss correction methods in smog chamber experiments
Ningxin Wang, Spiro D. Jorga, Jeffery R. Pierce, Neil M. Donahue, and Spyros N. Pandis
Atmos. Meas. Tech., 11, 6577-6588, https://doi.org/10.5194/amt-11-6577-2018,https://doi.org/10.5194/amt-11-6577-2018, 2018
Short summary
A portable dual smog chamber system for atmospheric aerosol field studies
Christos Kaltsonoudis, Spiro D. Jorga, Evangelos Louvaris, Kalliopi Florou, and Spyros N. Pandis
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-394,https://doi.org/10.5194/amt-2018-394, 2018
Revised manuscript accepted for AMT
Short summary
Insights into the morphology of multicomponent organic/inorganic aerosols from molecular dynamics simulations
Katerina S. Karadima, Vlasis G. Mavrantzas, and Spyros N. Pandis
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1098,https://doi.org/10.5194/acp-2018-1098, 2018
Revised manuscript accepted for ACP
Short summary
Related subject area  
Subject: Aerosols | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Simulation of the chemical evolution of biomass burning organic aerosol
Georgia N. Theodoritsi and Spyros N. Pandis
Atmos. Chem. Phys., 19, 5403-5415, https://doi.org/10.5194/acp-19-5403-2019,https://doi.org/10.5194/acp-19-5403-2019, 2019
Short summary
Contribution and uncertainty of sectorial and regional emissions to regional and global PM2.5 health impacts
Monica Crippa, Greet Janssens-Maenhout, Diego Guizzardi, Rita Van Dingenen, and Frank Dentener
Atmos. Chem. Phys., 19, 5165-5186, https://doi.org/10.5194/acp-19-5165-2019,https://doi.org/10.5194/acp-19-5165-2019, 2019
Short summary
New particle formation from sulfuric acid and ammonia: nucleation and growth model based on thermodynamics derived from CLOUD measurements for a wide range of conditions
Andreas Kürten
Atmos. Chem. Phys., 19, 5033-5050, https://doi.org/10.5194/acp-19-5033-2019,https://doi.org/10.5194/acp-19-5033-2019, 2019
Short summary
Receptor modelling of both particle composition and size distribution from a background site in London, UK – a two-step approach
David C. S. Beddows and Roy M. Harrison
Atmos. Chem. Phys., 19, 4863-4876, https://doi.org/10.5194/acp-19-4863-2019,https://doi.org/10.5194/acp-19-4863-2019, 2019
Short summary
Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model – Part 3: Assessing the influence of semi-volatile and intermediate-volatility organic compounds and NOx
Ali Akherati, Christopher D. Cappa, Michael J. Kleeman, Kenneth S. Docherty, Jose L. Jimenez, Stephen M. Griffith, Sebastien Dusanter, Philip S. Stevens, and Shantanu H. Jathar
Atmos. Chem. Phys., 19, 4561-4594, https://doi.org/10.5194/acp-19-4561-2019,https://doi.org/10.5194/acp-19-4561-2019, 2019
Short summary
Cited articles  
Allan, J. D., Williams, P. I., Morgan, W. T., Martin, C. L., Flynn, M. J., Lee, J., Nemitz, E., Phillips, G. J., Gallagher, M. W., and Coe, H.: Contributions from transport, solid fuel burning and cooking to primary organic aerosols in two UK cities, Atmos. Chem. Phys., 10, 647–668, https://doi.org/10.5194/acp-10-647-2010, 2010. 
Brinkman, G., Vance, G., Hannigan, M. P., and Milford, J. B.: Use of synthetic data to evaluate positive matrix factorization as a source apportionment tool for PM2.5 exposure data, Environ. Sci. Technol., 40, 1892–1901, 2006. 
Canonaco, F., Crippa, M., Slowik, J. G., Baltensperger, U., and Prévôt, A. S. H.: SoFi, an IGOR-based interface for the efficient use of the generalized multilinear engine (ME-2) for the source apportionment: ME-2 application to aerosol mass spectrometer data, Atmos. Meas. Tech., 6, 3649–3661, https://doi.org/10.5194/amt-6-3649-2013, 2013. 
Dall'Osto, M., Paglione, M., Decesari, S., Facchini, M. C., O'Dowd, C., Plass-Duellmer, C., and Harrison, R. M.: On the origin of AMS cooking Organic Aerosol at a rural site, Environ. Sci. Technol., 49, 13964–13972, 2015. 
Publications Copernicus
Download
Short summary
The ability of positive matrix factorization (PMF) factor analysis to identify and quantify the organic aerosol (OA) sources accurately is tested in this modeling study. The estimated uncertainty of the contribution of fresh biomass burning is less than 30 % and of the other primary sources is less than 40 %, when these sources contribute more than 20 % to the OA. Τhe first oxygenated OA factor includes mainly highly aged OA, while the second oxygenated OA factor contains fresher secondary OA.
The ability of positive matrix factorization (PMF) factor analysis to identify and quantify the...
Citation