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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 1 International Workshop on Air Quality Forecasting Research November 29, 2011 Potomac, MD, USA

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

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

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

Rahul A. ZaveriRichard C. Easter

Pacific Northwest National Laboratory

1

International Workshop on Air Quality Forecasting ResearchNovember 29, 2011Potomac, MD, USA

Page 2: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

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Motivation

SOA formation processes

SOA modeling challenges

MOSAIC aerosol modeling framework

Gas-particle partitioning of organic gases (new)

Sample results

Future Directions

Outline

Page 3: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

3 Battelle Proprietary

Dust

Phytoplankton

Dimethylsulfide

H2SO4, MSA

OxidationSoot

Sea-salt

Direct RadiativeForcing

Indirect RadiativeForcing

Ter

pene

s

Oxi

dat

ion

For

est

Fire

s

Nucleation

Hydrocarbons,NOx, SO2, NH3, POA

Oxidation

H2SO4, HNO3,Organic aerosol

Activation

Resuspension

chemistry

Need to efficiently and reliably model aerosol size, number, mass, composition, and their climate related properties at urban to global scales

Page 4: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

4

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)

SOA Formation Processes

Page 5: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

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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 Raoult’s Law or some sort of reactive uptake formulation as driving force for gas-particle mass transfer?

Should we use Henry’s 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?

SOA Modeling Challenges

Page 6: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

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

Page 7: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

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

EPA’s CMAQ – planned

Page 8: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

Thermodynamics & Mass TransferTreatments in MOSAIC

H2SO4

HNO3HCl

SolidSalts

Aqueous Phase

OM, BC

NH3

H2O

OH-

NO3-

Cl-

SO4-

Na+

NH4+H+

Ca2+ParticlePhase

Thermodynamic equilibriumThermodynamic equilibriumwithin the particle phase within the particle phase (depends on composition and RH)(depends on composition and RH)

Kinetic mass transfer between the gas and Kinetic mass transfer between the gas and size-distributed particles (1 to 10,000 nm)size-distributed particles (1 to 10,000 nm)

Custom numerical techniques have been developed to solve these equations efficiently and accurately

Reversible Gas-Particle Reactions

Page 9: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

9

Organic-inorganic interactions within the particle to determine water uptake and phase separation(partially implemented)

Size-distributed, dynamic mass transfer between gas and particles

Raoult’s Law in the absence ofaqueous phase (implemented)

Reactive uptake that instantly converts VOC to non-volatile products (implemented)

Henry’s Law in the presence of aqueous phase (future)

Particle-phase reactions (future)

Gas-Particle Partitioning of Organics

OrganicsH2SO4

HNO3HCl

Salts, BC,Org

Particle Phase

NH3

H2O

OH-

NO3-

Cl-

SO4-

Na+

NH4+H+

Ca2+

Gas Phase

Org

Oligomer

Page 10: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

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Raoult’s Law vs. Reactive Uptake

HC + oxidant 1 G1 + 2 G2

dGi

dt

xi = mole fraction

vaporpressure

A1 A2

= -ki (Gi – xiPi0)

Raoult’s Law Based Mass Transfer

HC + oxidant 1 G1 + 2 G2

dGi

dt

A1 A2

= -kiGi

Reactive Uptake Mass Transfer

Page 11: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

Sample Results

Idealized Case

Sacramento Urban Air Case

Page 12: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

Idealized Case

Diameter, Dp (m)

0.01 0.1 1

Num

ber

Con

cent

ratio

n (c

m-3

)

0

20

40

60

80

100

120

140t = 0 h (initial)

Initial aerosol composition: mass ratio OA/(NH4)2SO4 = 1

Page 13: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

Idealized Case

Diameter, Dp (m)

0.01 0.1 1

Num

ber

Con

cent

ratio

n (c

m-3

)

0

20

40

60

80

100

120

140 t = 0 (initial)t = 9 h (Raoult's Law)

Time (h)

0 2 4 6 8 10

SO

A M

ass

(g

m-3

)

6

8

10

12

14

16

Initial aerosol composition: mass ratio OA/(NH4)2SO4 = 1

Page 14: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

Idealized Case

Diameter, Dp (m)

0.01 0.1 1

Num

ber

Con

cent

ratio

n (c

m-3

)

0

20

40

60

80

100

120

140

t = 0 h (Initial)t = 9 h (Raoult's Law)t = 9 h (Reactive Uptake)

Time (h)

0 2 4 6 8 10

SO

A M

ass

(g

m-3

)

6

8

10

12

14

16

Initial aerosol composition: mass ratio OA/(NH4)2SO4 = 1

Page 15: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

Sacramento June 6, 2010

Initial aerosol composition: mass ratio OA/(NH4)2SO4 = 10

Diameter, Dp (m)

0.01 0.1

Num

ber

Con

cent

ratio

n (c

m-3

)

0

200

400

600

800

1000

1200 Observed at 9:00 amInitial (fitted to obs.)Observed at 11:00 am

Page 16: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

Sacramento June 6, 2010

Initial aerosol composition: mass ratio OA/(NH4)2SO4 = 10

Diameter, Dp (m)

0.01 0.1

Num

ber

Con

cent

ratio

n (c

m-3

)

0

200

400

600

800

1000

1200 Observed at 9:00 amInitial (fitted to obs.)Observed at 11:00 amModel at 11:00 am (Raoult's Law)

Page 17: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

Sacramento June 6, 2010

Initial aerosol composition: mass ratio OA/(NH4)2SO4 = 10

Diameter, Dp (m)

0.01 0.1

Num

ber

Con

cent

ratio

n (c

m-3

)

0

200

400

600

800

1000

1200 Observed at 9:00 amInitial (fitted to obs.)Observed at 11:00 amModel at 11:00 am (Raoult's Law)Model at 11:00 am (Reactive Uptake)

Page 18: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

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Diameter, Dp (m)

0.01 0.1

Num

ber

Con

cent

ratio

n (c

m-3

)

0

200

400

600

800

1000

1200Observed at 9:00 amInitial (fitted to obs.)Observed at 11:00 amModel at 11:00 am (Raoult's Law)

Sacramento June 6, 2010

Aitken mode mass ratio OA/(NH4)2SO4 = 10

Accumulation mode mass ratio OA/(NH4)2SO4 = 0.01

Page 19: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

Diameter, Dp (m)

0.01 0.1

Num

ber

Con

cent

ratio

n (c

m-3

)

0

200

400

600

800

1000

1200Observed at 9:00 amInitial (fitted to obs.)Observed at 11:00 amModel at 11:00 am (Raoult's Law)Model at 11:00 am (Reactive Uptake)

19

Sacramento June 6, 2010

Aitken mode mass ratio OA/(NH4)2SO4 = 10

Accumulation mode mass ratio OA/(NH4)2SO4 = 0.01

Page 20: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

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

Page 21: Modeling Dynamic Partitioning of Semi-volatile Organic Gases to Size-Distributed Aerosols Rahul A. Zaveri Richard C. Easter Pacific Northwest National

21

Thank you for your attention

Funding for this work was provided byDepartment of Energy (DOE) Atmospheric System Research (ASR) Program