Office of Research and Development 6 October 2008 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 7 th Annual CMAS Conference Chapel Hill, NC

Office of Research and Development 1 Covered Topics Previous SOA module (CMAQv4.6) Updated SOA module (CMAQv4.7) Additions/Enhancements –New precursors –SOA from aromatics under low-NO x conditions –Acid catalyzed SOA –Oligomerization of semi-volatile material –SOA from in-cloud oxidation –Parameterization changes Preliminary Results

Office of Research and Development 2 Previous Modeled Reactive Organic Gases and SOA (CMAQv4.6) Sources Anthropogenic: Alkanes Xylene Cresol Toluene Biogenic: Monoterpenes 5 Precursors Products Aitken Mode ASOA (AORGAI) Accumulation Mode ASOA (AORGAJ) Aitken Mode BSOA (AORGBI) Accumulation Mode BSOA (AORGBJ) 4 SOA Species

Office of Research and Development 3 Current Reactive Organic Gases and SOA (CMAQv4.7) Sources Anthropogenic: Alkanes Xylene Toluene Benzene Biogenic: Monoterpenes Isoprene Sesquiterpenes Cloud: Glyoxal/Methylglyoxal 8 Precursors Products (Accumulation Mode Only) AALKJ AXYL1J, AXYL2J, AXYL3J ATOL1J, ATOL2J, ATOL3J ABNZ1J, ABNZ2J, ABNZ3J AOLGAJ ATRP1J, ATRP2J AISO1J, AISO2J, AISO3J ASQTJ AOLGBJ AORGCJ 19 SOA Species low-NO x high-NO x acid catalyzed 2-product model “aged” SOA cloud produced unchanged MEGAN emission factors in BEIS 3.14 density update

Office of Research and Development 4 NO x Dependence Under low-NOx conditions, aromatic peroxy radicals preferentially react with HO 2 (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 –ΔBENZENE NO partitions according to the 2-product Odum model –ΔBENZENE HO2 becomes 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

Office of Research and Development 5 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, ] nmol/m 3

Office of Research and Development 6 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

Office of Research and Development 7 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

Office of Research and Development 8 Parameterization Changes - ΔH vap Enthalpy of vaporization has a role in the dependence of the partitioning coefficient, K om, 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 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

Office of Research and Development 9 Impact of New ΔH vap Values Compound Ethalpy of vaporization (kJ/mol)* SV ALK40 SV XYL32 SV TOL18 SV BNZ18 SV TRP40 SV ISO40 SV SQT40 *based on lab measurements (Offenberg et al., 2006)

Office of Research and Development 10 Interpreting SOA Results – OM/OC ratios Model calculates total organic mass OM/OC ratios are necessary for comparisons with measurements CMAQv4.7OM/OC AALK1.56 AXYL, ATOL, ABNZ2.0 AISO1 & AISO21.6 AISO32.7 ATRP1.4 ASQT2.1 AORGC2.0 AOLGA2.1 AOLGB2.1 AORGPA1.0 CMAQv4.6OM/OC AORGA1.67 AORGB1.47 AORGPA1.2

Office of Research and Development 11 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

Office of Research and Development 12 Anthropogenic SOA (monthly average, μg/m 3 ) Change to enthalpy of vaporization SOA model updates August 2006 January 2006

Office of Research and Development 13 Biogenic SOA (monthly average, μg/m 3 ) Change to enthalpy of vaporization SOA model updates August 2006 January 2006

Office of Research and Development 14 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?

Office of Research and Development 15 Comparison to Carbon Tracer Measurements – RTP 2003

Office of Research and Development 16 Summary 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 ΔH vap offsetting gains from new precursors –Intercontinental transport (boundary conditions) of longer-lived species? –Role of intermediate VOCs?

Office of Research and Development 17 Current and Future Plans Continue SOA Module Evaluation –RTP tracer data comparison –LADCO data set analysis –Sensitivity Analysis Parameterization Updates –Yields, H vap, non-volatile rates, etc. Rosenbrock solver for aqueous chemistry More sophisticated oligomerization treatment Further investigation in current and possible new precursors

Office of Research and Development 18 Contact: you are here!

Office of Research and Development 19 BONUS SLIDES

Office of Research and Development 20 Model CPU Time is Important! 17% increase

Office of Research and Development 21

Office of Research and Development 22 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

Office of Research and Development 23 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 ΔH vap values are lower than what was used here (42 kJ/mol)

Office of Research and Development 24 Overview of the Underlying SOA Module Absorptive Partitioning Theory as adapted by Schell et al. (2001) Generally: Some products, C i,j, are condensable and contribute to SOA production: Stoichiometric yield coefficients,, are obtained from smog-chamber studies

Office of Research and Development 25 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 ΔH vap ) Molecular weights:

Office of Research and Development 26 Seasonal Pattern Improves (SOA b ) August 2006 January 2006 AERO5AERO4New - Old

Office of Research and Development 27 Evaluation of TC results at Duke 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 Observations Previous Incr. SOA updates Obs Previous SOA Incr. update Total Carbon mean stdev CoV Daily amplitude [ug/m3] median min max # paired hours: 246 # days with complete data: 25

Office of Research and Development 28 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, , 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, , 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, , 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, , Morris, R.E., B. Koo, A. Guenther, G. Yarwood, D. McNally, T.W. Tesche, G. Tonnesen, J. Boylan, P. Brewer, Atmos. Environ., 40, , 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, , 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, , 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, , 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, , Further Reading Material