Articles | Volume 6, issue 12
https://doi.org/10.5194/acp-6-4867-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Special issue:
https://doi.org/10.5194/acp-6-4867-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
30 Oct 2006
Implementation of a Markov Chain Monte Carlo method to inorganic aerosol modeling of observations from the MCMA-2003 campaign – Part I: Model description and application to the La Merced site
F. M. San Martini
Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
now at: the Board on Chemical Sciences and Technology, National Academies, Washington, D.C., USA
E. J. Dunlea
Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
Cooperative Institute for Research in the Environmental Sciences (CIRES), Univ. of Colorado at Boulder, Boulder, CO, USA
M. Grutter
Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Mexico City, Mexico
T. B. Onasch
Aerodyne Research Inc., Billerica, MA, USA
J. T. Jayne
Aerodyne Research Inc., Billerica, MA, USA
M. R. Canagaratna
Aerodyne Research Inc., Billerica, MA, USA
D. R. Worsnop
Aerodyne Research Inc., Billerica, MA, USA
C. E. Kolb
Aerodyne Research Inc., Billerica, MA, USA
J. H. Shorter
Aerodyne Research Inc., Billerica, MA, USA
S. C. Herndon
Aerodyne Research Inc., Billerica, MA, USA
M. S. Zahniser
Aerodyne Research Inc., Billerica, MA, USA
J. M. Ortega
Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
now at: Sandia National Laboratory, Livermore, CA, USA
G. J. McRae
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
L. T. Molina
Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
Molina Center on Energy and the Environment, La Jolla, CA, USA
M. J. Molina
Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
Department of Chemistry and Biochemistry, University of California, San Diego, CA, USA
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Cited
9 citations as recorded by crossref.
- Estimating the variability of contact parameter temperature dependence with the Monte Carlo Markov Chain method A. Määttänen & M. Douspis 10.1016/j.grj.2014.09.002
- A Bayesian approach for probabilistic safety assessment of HLW repository Y. Lee et al. 10.1016/j.anucene.2019.107203
- Ground‐level nitrogen dioxide concentrations inferred from the satellite‐borne Ozone Monitoring Instrument L. Lamsal et al. 10.1029/2007JD009235
- Using tunable infrared laser direct absorption spectroscopy for ambient hydrogen chloride detection: HCl-TILDAS J. Halfacre et al. 10.5194/amt-16-1407-2023
- Implementation of a Markov Chain Monte Carlo method to inorganic aerosol modeling of observations from the MCMA-2003 campaign – Part II: Model application to the CENICA, Pedregal and Santa Ana sites F. San Martini et al. 10.5194/acp-6-4889-2006
- Inverse modeling of cloud-aerosol interactions – Part 1: Detailed response surface analysis D. Partridge et al. 10.5194/acp-11-7269-2011
- A case study of ozone production, nitrogen oxides, and the radical budget in Mexico City E. Wood et al. 10.5194/acp-9-2499-2009
- Inverse modelling of cloud-aerosol interactions – Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach D. Partridge et al. 10.5194/acp-12-2823-2012
- Single particle characterization using a light scattering module coupled to a time-of-flight aerosol mass spectrometer E. Cross et al. 10.5194/acp-9-7769-2009
9 citations as recorded by crossref.
- Estimating the variability of contact parameter temperature dependence with the Monte Carlo Markov Chain method A. Määttänen & M. Douspis 10.1016/j.grj.2014.09.002
- A Bayesian approach for probabilistic safety assessment of HLW repository Y. Lee et al. 10.1016/j.anucene.2019.107203
- Ground‐level nitrogen dioxide concentrations inferred from the satellite‐borne Ozone Monitoring Instrument L. Lamsal et al. 10.1029/2007JD009235
- Using tunable infrared laser direct absorption spectroscopy for ambient hydrogen chloride detection: HCl-TILDAS J. Halfacre et al. 10.5194/amt-16-1407-2023
- Implementation of a Markov Chain Monte Carlo method to inorganic aerosol modeling of observations from the MCMA-2003 campaign – Part II: Model application to the CENICA, Pedregal and Santa Ana sites F. San Martini et al. 10.5194/acp-6-4889-2006
- Inverse modeling of cloud-aerosol interactions – Part 1: Detailed response surface analysis D. Partridge et al. 10.5194/acp-11-7269-2011
- A case study of ozone production, nitrogen oxides, and the radical budget in Mexico City E. Wood et al. 10.5194/acp-9-2499-2009
- Inverse modelling of cloud-aerosol interactions – Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach D. Partridge et al. 10.5194/acp-12-2823-2012
- Single particle characterization using a light scattering module coupled to a time-of-flight aerosol mass spectrometer E. Cross et al. 10.5194/acp-9-7769-2009
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