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Updates to the Treatment of Secondary Organic Aerosol in CMAQv4.7. Sergey L. Napelenok, Annmarie G. Carlton, Prakash V. Bhave, Golam Sarwar, George Pouliot, Robert W. Pinder, Edward O. Edney, Marc Houyoux, Kristen M. Foley U.S. Environmental Protection Agency Research Triangle Park, NC.

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updates to the treatment of secondary organic aerosol in cmaqv4 7

Updates to the Treatment of Secondary Organic Aerosol in CMAQv4.7

Sergey L. Napelenok, Annmarie G. Carlton, Prakash V. Bhave,

Golam Sarwar, George Pouliot, Robert W. Pinder,

Edward O. Edney, Marc Houyoux, Kristen M. Foley

U.S. Environmental Protection Agency

Research Triangle Park, NC

7th Annual CMAS Conference

Chapel Hill, NC

covered topics
Covered Topics
  • Previous SOA module (CMAQv4.6)
  • Updated SOA module (CMAQv4.7)
  • Additions/Enhancements
    • New precursors
    • SOA from aromatics under low-NOx conditions
    • Acid catalyzed SOA
    • Oligomerization of semi-volatile material
    • SOA from in-cloud oxidation
    • Parameterization changes
  • Preliminary Results
previous modeled reactive organic gases and soa cmaqv4 6








5 Precursors


Aitken Mode ASOA (AORGAI)

Accumulation Mode ASOA (AORGAJ)

Aitken Mode BSOA (AORGBI)

Accumulation Mode BSOA (AORGBJ)

4 SOA Species

Previous Modeled Reactive Organic Gases and SOA (CMAQv4.6)
current reactive organic gases and soa cmaqv4 7












8 Precursors

Products (Accumulation Mode Only)











19 SOA Species



density update

acid catalyzed


MEGAN emission factors in BEIS 3.14

“aged” SOA

2-product model

cloud produced

Current Reactive Organic Gases and SOA (CMAQv4.7)
no x dependence
NOx Dependence
  • Under low-NOx conditions, aromatic peroxy radicals preferentially react with HO2 (and not NO)
  • The resulting SOA formed from these reactions is considered non-volatile
  • For example, benzene:
  • The pathway determines the type of SOA formed
    • ΔBENZENENOpartitions according to the 2-product Odum model
    • ΔBENZENEHO2becomes non-volatile SOA
  • Parameterization was adapted from Ng et al. (2007)
  • Implemented for SAPRC99 and CB05
  • Changes to emissions processing are required to produce “delumped” benzene emissions
acid catalyzed reactions
Acid Catalyzed Reactions
  • Under acidic conditions, SOA production from isoprene is enhanced
  • CMAQ parameterization is based on a series of chamber experiments where the acidity of the seed aerosol was controlled (Surratt et al., 2007)
  • Acidity enhancement is approximated by normalizing the empirical relationship:
  • The hydrogen ion concentration is computed from a charge balance and limited by the range of the experimental conditions [ 0.0, 530.0 ] nmol/m3
condensed phase reactions
Condensed-Phase Reactions
  • Semi-volatile SOA is converted to low-volatility products through polymerization processes
  • Kalberer et al. (2004) estimated that after 20 hours of aging, 50% of organic particle mass consists of polymers
  • CMAQ semi-volatile species are “aged” by transferring the mass into two separate non-volatile species:
    • Anthropogenic Oligomers from alkanes, xylene, toluene, and benzene
    • Biogenic Oligomers from monoterpenes, isoprene, sesquiterpenes
  • The SOA/SOC ratio for these products was set at 2.1 according to Turpin & Lim (2001) for non-urban areas
cloud produced soa
Cloud-Produced SOA
  • Most air quality models predict lower OC concentrations aloft than are suggested by measurements
  • Models also predict less spatial and temporal variability
  • CMAQ implementation:
    • Glyoxal and Methylglyoxal as precursors, based on Carlton et al. (2007) and Altieri et al. (2008)
  • Yield is estimated to be 4% based on laboratory data
  • OM/OC = 2.0
  • Cloud-produced SOA is considered non-volatile
parameterization changes h vap
Parameterization Changes - ΔHvap
  • Enthalpy of vaporization has a role in the dependence of the partitioning coefficient, Kom, on temperature:
  • Previously, 156kJ/mol was used for all SOA species in CMAQ
  • Values that appear in literature and are used in other models range from 15-88 kJ/mol, depending on the compound.
  • The old CMAQ value was too large and was partly to blame for exaggerated night-time and wintertime SOA peaks
impact of new h vap values
Impact of New ΔHvapValues

*based on lab measurements (Offenberg et al., 2006)

interpreting soa results om oc ratios
Interpreting SOA Results – OM/OC ratios
  • Model calculates total organic mass
  • OM/OC ratios are necessary for comparisons with measurements
preliminary module evaluation
Preliminary Module Evaluation
  • Seasonal comparisons over eastern United States
    • January and August 2006
    • More reasonably predicted higher concentrations in the summer (previously winter was higher)
  • Model configuration:
    • 12km resolution
    • CMAQv4.6 “increment” with previous and new SOA treatment
    • CB05 chemistry (EBI solver)
    • “yamo” advection
    • “acm2” diffusion
    • “acm” clouds
anthropogenic soa monthly average g m 3

January 2006

August 2006

Anthropogenic SOA (monthly average, μg/m3)

Change to enthalpy of vaporization

SOA model updates

biogenic soa monthly average g m 3

January 2006

August 2006

Biogenic SOA (monthly average, μg/m3)

Change to enthalpy of vaporization

SOA model updates

preliminary module evaluation part 2
Preliminary Module Evaluation (Part 2)
  • Carbon Tracer comparison at a site in RTP, NC
    • Four 48 hour measurements during August/September 2003
    • Speciated biogenic SOA (isoprene, monoterpene, sesquiterpene) and aromatics
    • Improvement over the old model is evident, but concentrations are still much lower than indicated by tracer measurements
    • Uncertainty in the tracer measurements: can laboratory measured tracer/SOC ratios be applied to atmospheric conditions?
  • Major changes to CMAQ SOA module
    • New precursors
    • New processes
    • Updated parameters
    • Only moderate CPU time requirement increase – 17%
  • Preliminary evaluation suggests several improvements
    • Better seasonal trends
    • Better diurnal trends
    • Better science!
  • Total OC mass did not increase much
    • TRP SOA is lower due to lower ΔHvap offsetting gains from new precursors
    • Intercontinental transport (boundary conditions) of longer-lived species?
    • Role of intermediate VOCs?
current and future plans
Current and Future Plans
  • Continue SOA Module Evaluation
    • RTP tracer data comparison
    • LADCO data set analysis
    • Sensitivity Analysis
  • Parameterization Updates
    • Yields, Hvap, non-volatile rates, etc.
  • Rosenbrock solver for aqueous chemistry
  • More sophisticated oligomerization treatment
  • Further investigation in current and possible new precursors
results in context with other work morris et al 2006
Results in Context with Other Work – Morris et al., 2006
  • Identified several missing pathways for SOA formation in AQ models
  • Implemented three of these into CMAQ v4.4:
    • Polymerization
      • Based on Kalberer et al. (2004)
    • Production from sesquiterpenes
      • Emissions split from BEIS3 TERP
      • SOA is nonvolatile
    • Production from isoprene
      • New volatile species
results in context with other work henze et al 2008
Results in Context with Other Work – Henze et al., 2008
  • Developed a mechanism to model competition between low and high-yield pathways of SOA formation from aromatics
    • Toluene
    • Xylene
    • Benzene (new)
  • CMAQv4.7 aromatic SOA parameters are based on this work
  • Found benzene to be the most important aromatic species for SOA formation
  • CMAQv4.7 ΔHvap values are lower than what was used here (42 kJ/mol)
overview of the underlying soa module
Overview of the Underlying SOA Module
  • Absorptive Partitioning Theory as adapted by Schell et al. (2001)
  • Generally:
  • Some products, Ci,j, are condensable and contribute to SOA production:
  • Stoichiometric yield coefficients, , are obtained from smog-chamber studies
overview of the underlying soa module continued
Overview of the Underlying SOA Module (continued)
  • Partitioning between gas and particle phase is based on saturation concentration,
  • Considering a mixture of condensable compounds (Raoult’s Law), particle phase concentration of each can by calculated by:
    • Important parameters:
      • Stoichiometric yield coefficients:
      • Saturation concentrations: (also T and ΔHvap)
      • Molecular weights:
seasonal pattern improves soa b



New - Old

Seasonal Pattern Improves (SOAb)

January 2006

August 2006


Evaluation of TC results atDuke Forest (August 2006)

  • Monthly-averaged TC concentrations from SOA Increment are slightly lower than previous simulations.
  • Neither model version captures the peak on 25-Aug.
  • However, the SOA Increment exhibits several improvements relative to previous simulations:
  • lower coefficient of variation
  • lower daily amplitude


Previous Incr.

SOA updates

Obs Previous SOA

Incr. update


Total Carbon

mean 4.039 2.654 2.292

stdev 1.614 1.650 1.176

CoV 0.400 0.622 0.513

Daily amplitude [ug/m3]

median 2.610 3.707 2.853

min 0.740 2.144 1.373

max 5.820 6.223 5.318


# paired hours: 246

# days with complete data: 25

further reading material
Further Reading Material

Altieri, K.E., S.P. Seitzinger, A.G. Carlton, B.J. Turpin, G.C. Klein, A.G. Marshall, Oligomers formed through in-cloud methylglyoxal reactions: Chemical composition, properties, and mechanisms investigated by ultra-high resolution FT-ICR mass spectrometry, Atmos. Environ., 42, 1476-1490, 2008

Carlton, A.G., B.J. Turpin, K.E. Altieri, A. Reff, S. Seitzinger, H.J. Lim, and B. Ervens, Atmospheric Oxalic Acid and SOA Production from Glyoxal: Results of Aqueous Photooxidation Experiments, Atmos. Environ., 41, 7588-7602, 2007.

Henze, D.K., J.H. Seinfeld, N.L. Ng, J.H. Kroll, T.-M. Fu, D.J. Jacob, C.L. Heald, Global modeling of secondary organic aerosol formation from aromatic hydrocarbons: high- vs. low-yield pathways, Atmos. Chem. Phys., 8, 2405-2420, 2008.

Kalberer, M., D. Paulsen, M. Sax, M. Steinbacher, J. Dommen, A.S.H. Prevot, R. Fisseha, E. Weingartner, V. Frankevich, R. Zenobi, U. Baltensperger, Identification of polymers as major components of atmospheric organic aerosols, Science, 303, 1659-1662, 2004.

Morris, R.E., B. Koo, A. Guenther, G. Yarwood, D. McNally, T.W. Tesche, G. Tonnesen, J. Boylan, P. Brewer, Atmos. Environ., 40, 4960-4972, 2006.

Ng, N. L., J. H. Kroll, A. W. H. Chan, P. S. Chhabra, R. C. Flagan, and J. H. Seinfeld, Secondary organic aerosol formation from m-xylene, toluene, and benzene, Atmos. Chem. Phys., 7, 3909-3922, 2007.

Schell, B., I. J. Ackermann, H. Hass, F. S. Binkowski, and A. Abel, Modeling the formation of secondary organic aerosol within a comprehensive air quality modeling system, J. Geophys. Res., Vol 106, No D22, 28275-28293, 2001.

Surratt, J.D., M. Lewandowski, J.H. Offenberg, M. Jaoui, T.E. Kleindienst, E.O. Edney, J.H. Seinfeld, Effect of acidity on secondary organic aerosol formation from isoprene, Environ. Sci. Technol., 41, 5363-5369, 2007.

Turpin, B. J. and H.-J. Lim, Species contributions to PM2.5 mass concentrations: revisiting common assumptions for estimating organic mass, Aero. Sci. Technol., Vol 35, 602-610, 2001.