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Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols

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Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National Laboratory – PowerPoint PPT presentation

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Title: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols


1
Modeling Dynamic Partitioning of Semi-volatile
Organic Gases to Size-Distributed Aerosols
  • Rahul A. Zaveri
  • Richard C. Easter
  • Pacific Northwest National Laboratory

International Workshop on Air Quality Forecasting
Research November 29, 2011 Potomac, MD, USA
2
Outline
  • Motivation
  • SOA formation processes
  • SOA modeling challenges
  • MOSAIC aerosol modeling framework
  • Gas-particle partitioning of organic gases (new)
  • Sample results
  • Future Directions

3
Need to efficiently and reliably model aerosol
size, number, mass, composition, and their
climate related properties at urban to global
scales
Battelle Proprietary
4
SOA Formation Processes
  • Up to 90 of submicron aerosol mass is composed
    of organics (Kanakidou et al., 2005 Zhang et
    al., 2007)
  • SOA formation is quite rapid (within a few hours)
    during daytime (Volkamer et al., 2006 Kleinman
    et al., 2007 de Gouw et al., 2008)
  • SOA from oxidation of SVOCs from diesel exhaust
    may help explain some of the missing organic
    aerosol mass in models (Robinson et al., 2007)
  • Observed rapid growth of newly formed particles
    (via homogeneous nucleation) is thought to be by
    SOA condensation (Kuang et al., 2008)
  • Anthropogenic and biogenic SOA precursors may
    interact to enhance the overall SOA yield (Weber
    et al., 2007)
  • Particle-phase reactions of absorbed VOCs within
    inorganic particles can form SOA (Jang et al.,
    2003 Kroll et al., 2005 Liggio et al., 2005)
  • Accretion reactions, including aldol
    condensation, acid dehydration, and gem-diol
    condensation can transform VOCs into oligomeric
    compounds (Gao et al., 2004 Jang et al., 2003
    Kalberer et al., 2004)

5
SOA Modeling Challenges
  • Gas-particle partitioning processes are still
    poorly understood at a fundamental level
  • How do we handle the complexities in the
    gas-phase VOC chemistry?
  • Should we use Raoults Law or some sort of
    reactive uptake formulation as driving force for
    gas-particle mass transfer?
  • Should we use Henrys Law if the organics are
    dissolved in the aqueous phase?
  • Are the organic particles liquid or solid?
    Virtanen et al. (2011) and Vaden et al. (2011)
    suggest that SOA particles are solid.
  • How do we treat organic-inorganic interactions
    and the associated phase transitions?
  • How do we treat particle-phase reactions? What
    are the time scales?
  • What are the anthropogenic-biogenic interactions?
    How do we reliably represent them in models?

6
General Problem Solving Approach
  • Develop a comprehensive aerosol model framework
    that includes all the processes that we think are
    (or might be) important
  • Evaluate the roles of specific processes using
    appropriate laboratory and field observations
  • Simplify, parameterize, and optimize the process
    model as much as possible to increase
    computational efficiency and decrease memory
    requirements

7
MOSAIC Aerosol Module
  • Model for Simulating Aerosol Interactions and
    Chemistry (Zaveri et al., 2008)
  • Comprehensive aerosol module for air quality and
    climate modeling
  • Flexible framework for coupling various gas and
    aerosol processes
  • Robust, accurate, and highly efficient custom
    numerical solvers for several processes
  • Suitable for 3-D regional and global models
  • Implementation in
  • Weather Research and Forecasting Model (WRF-Chem)
    done
  • Global model Community Atmosphere Model (CAM5)
    in progress
  • EPAs CMAQ planned

8
Thermodynamics Mass Transfer Treatments in
MOSAIC
Kinetic mass transfer between the gas and
size-distributed particles (1 to 10,000 nm)
Thermodynamic equilibriumwithin the particle
phase (depends on composition and RH)
  • Custom numerical techniques have been developed
    to solve these equations efficiently and
    accurately

9
Gas-Particle Partitioning of Organics
  • Organic-inorganic interactions within the
    particle to determine water uptake and phase
    separation(partially implemented)
  • Size-distributed, dynamic mass transfer between
    gas and particles
  • Raoults Law in the absence ofaqueous phase
    (implemented)
  • Reactive uptake that instantly converts VOC to
    non-volatile products (implemented)
  • Henrys Law in the presence of aqueous phase
    (future)
  • Particle-phase reactions (future)

10
Raoults Law vs. Reactive Uptake
Raoults Law Based Mass Transfer
HC oxidant ? a1 G1 a2 G2
A1 A2
xi mole fraction
dGi dt
-ki (Gi xiPi0)
vapor pressure
11
Sample Results
  • Idealized Case
  • Sacramento Urban Air Case

12
Idealized Case
Initial aerosol composition mass ratio
OA/(NH4)2SO4 1
13
Idealized Case
Initial aerosol composition mass ratio
OA/(NH4)2SO4 1
14
Idealized Case
Initial aerosol composition mass ratio
OA/(NH4)2SO4 1
15
Sacramento June 6, 2010
Initial aerosol composition mass ratio
OA/(NH4)2SO4 10
16
Sacramento June 6, 2010
Initial aerosol composition mass ratio
OA/(NH4)2SO4 10
17
Sacramento June 6, 2010
Initial aerosol composition mass ratio
OA/(NH4)2SO4 10
18
Sacramento June 6, 2010
Aitken mode mass ratio OA/(NH4)2SO4 10
Accumulation mode mass ratio OA/(NH4)2SO4 0.01
19
Sacramento June 6, 2010
Aitken mode mass ratio OA/(NH4)2SO4 10
Accumulation mode mass ratio OA/(NH4)2SO4 0.01
20
Future Directions
  • Perform additional constrained Lagrangian model
    analyses to test different SOA formation
    mechanisms
  • Use carefully designed chamber experiments to
    constrain and evaluate different formulations
  • Extend model analyses to mixtures of organic and
    inorganic species at different relative
    humidities
  • Implement and evaluate new SOA formulations in
    WRF-Chem using urban to regional field
    observations

21
Thank you for your attention
Funding for this work was provided by Department
of Energy (DOE) Atmospheric System Research (ASR)
Program
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