HR: 0800h
AN: A11A-0020    [Abstracts]
TI: Implementation of a Markov Chain Monte Carlo Method to Inorganic Aerosol Modeling: Mexico City Metropolitan Area Case Study
AU: * San Martini, F M
EM: ico@mit.edu
AF: Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139 United States
AU: Dunlea, E
EM: dunlea@post.harvard.edu
AF: Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139 United States
AU: Ortega, J M
EM: jmoa@mit.edu
AF: Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139 United States
AU: McRae, G J
EM: mcrae@mit.edu
AF: Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139 United States
AU: Molina, L T
EM: ltmolina@mit.edu
AF: Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139 United States
AU: Molina, M J
EM: mmolina@mit.edu
AF: Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139 United States
AU: Dzepina, K
EM: katja.dzepina@colorado.edu
AF: Department of Chemistry and Biochemistry, University of Colorado-Boulder, UCB 216, Boulder, CO 80309 United States
AU: Jimenez, J
EM: jose.jimenez@colorado.edu
AF: Department of Chemistry and Biochemistry, University of Colorado-Boulder, UCB 216, Boulder, CO 80309 United States
AU: Shorter, J H
EM: shorter@aerodyne.com
AF: Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821 United States
AU: Canagaratna, M R
EM: mrcana@aerodyne.com
AF: Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821 United States
AU: Herndon, S C
EM: herndon@aerodyne.com
AF: Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821 United States
AU: Onasch, T B
EM: onasch@aerodyne.com
AF: Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821 United States
AU: Jayne, J T
EM: jayne@aerodyne.com
AF: Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821 United States
AU: Wormhoudt, J C
EM: jody@aerodyne.com
AF: Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821 United States
AU: Zahniser, M S
EM: mz@aerodyne.com
AF: Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821 United States
AU: Worsnop, D R
EM: worsnop@aerodyne.com
AF: Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821 United States
AU: Kolb, C E
EM: kolb@aerodyne.com
AF: Aerodyne Research, Inc., 45 Manning Road, Billerica, MA 01821 United States
AU: Salcedo, D
EM: dara@ciq.uaem.mx
AF: Universidad Aut¢noma del Estado de Morelos, Av. Universidad #1001, Cuernavaca Morelos, 62210 Mexico
AU: Marley, N A
EM: marley@anl.gov
AF: Argonne National Laboratory, Environmental Research Division, Bldg. 203/ER, Argonne, IL 60439 United States
AU: Gaffney, J S
EM: gaffney@anl.gov
AF: Argonne National Laboratory, Environmental Research Division, Bldg. 203/ER, Argonne, IL 60439 United States
AU: Grutter de la Mora, M
EM: grutter@servidor.unam.mx
AF: Centro de Ciencias de la Atmsfera, Universidad Nacional Aut¢noma de M‚xico, Circuito Exterior s/n, Ciudad Universitaria, 04510 Mexico
AB: Significant effort has been devoted to collecting data on urban particulate matter (PM) concentrations, and advances in particle measurement technologies have allowed for an increasingly sophisticated picture to be developed. Relative to this, the dataset for the gas phase precursors to the inorganic PM is sparse, despite the necessity of these observations in determining effective control strategies. A Bayesian method has been implemented to exploit the asymmetry between the rich aerosol dataset and the relatively poor dataset on gas-phase precursors. A Markov Chain Monte Carlo algorithm was combined with the equilibrium inorganic aerosol model ISORROPIA to produce a powerful tool to analyze aerosol data and predict gas phase concentrations where these are unavailable. The method directly incorporates measurement uncertainty, prior knowledge, and provides for a formal framework to combine measurements of different quality. Applying the method to data from Mexico City, evidence for stable and metastable aerosols was found, and model predictions relative to the observations and their uncertainties are critically compared. Gas phase concentrations, where unavailable, were estimated including, for the first time in Mexico City, hydrochloric acid. The policy implications of these findings will be discussed, with a focus on the role of ammonia and chloride species on particle formation.
DE: 6309 Decision making under uncertainty
DE: 0305 Aerosols and particles (0345, 4801)
DE: 0345 Pollution--urban and regional (0305)
DE: 0365 Troposphere--composition and chemistry
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting