1. Incorporation of the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID) into CMAQ Yang Zhang, Betty K. Pun, Krish Vijayaraghavan, Shiang-Yuh Wu and Christian Seigneur AER, San Ramon, CA CMAQ Workshop, October 2002

2. MADRID Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution Gas/particle mass transfer Hybrid algorithm Full equilibrium algorithm Coagulation not important under polluted conditions Condensable gases Existing Particles Nucleation Condensation Coagulation

3. Gas-to-Particle Conversion Processes in MADRID Nucleation (McMurry and Friedlander,1979) Thermodynamic equilibrium for inorganic species –ISORROPIA (SO 4 =, NO 3 -, NH 4 +, Na +, Cl -, water) Equilibrium for organic species –Absorption based on empirical data –Dissolution and absorption from first principles Diffusion-limited condensation/volatilization –Hybrid mass transfer from Calpado & Pandis or from Meng et al. –Moving-center algorithm of Jacobson

4. Major Differences between MADRID and Original CMAQ Module CMAQ Modal size distribution NH 4 +, SO 4 =, NO 3 -, Na +, Cl - Coagulation Nucleation Full equilibrium approach to simulate mass transfer Standard dry deposition Absorption (irreversible) of 6 SOA using chamber data MADRID Sectional representation Same species Not treated New particle formation Hybrid or full equilibrium approach Revised flux approach Two SOA modules available

5. SOA Modules in MADRID MADRID 1 Modified CBM-IV & RADM2 4 anthropogenic SOA (aromatics) 34 biogenic SOA (monoterpenes) Absorption based on smog chamber data (Odum et al., 1997; Griffin et al., 1999) MADRID 2 CACM (1) –42 condensable products –hydrophobic surrogate SOA 4 anthropogenic, 1 biogenic –hydrophilic surrogate SOA 3 anthropogenic, 2 biogenic Absorption based on estimated properties Dissolution into existing aqueous particles (1) Caltech atmospheric chemistry mechanism

6. Meteorology SectionalModal CBM-IV / RADM2 + 19 biogenic reactions or CACM Sectional PM module PM concentrations Sectiona l PM chemical concentrations by size section PM deposition flux by chemical Pre- Processors Chemical Transport Model Output Conversion from modal to sectional PM concentrations Gas-phase: CBM- IV + 3 biogenic reactions Modal PM module PM chemical concentrations by mode Dry Deposition (sectional V dep ) Dry Deposition (modal V dep ) Emissions, initial conditions, boundary conditions (modal) ModalSectional Modal Incorporation of MADRID into Models-3

7. Los Angeles Application SCAQS episode of 27-28 August 1987 Simulation using MM5 and CMAQ-MADRID 1

8. SCAQS 1987 Episode 25-29 August 1987 Domain: 63 x 28 grid cells, consistent with previous modeling exercises Grid Resolution: 5 km MM5 used to generate input meteorology Emission inventory developed from previous simulations

9. SCAQS Modeling Domain HAWT CELA RIVR

10. Model Performance Ozone and PM 2.5 Species Error Bias O 3 34%9% PM 2.5 44% 14%

11. Model Performance PM 2.5 Components Species Error Bias Sulfate 38% 11% Nitrate 45% -38% EC 54%-20% OC 49%-22%

12. Observed and Simulated PM 2.5 Composition 27 August 28 August Sulfate Nitrate Ammonium EC OC Others Observations MADRID 1

13. Nashville, Tennessee Application SOS episode of 15-18 July 1995 Simulation using MM5 and CMAQ-MADRID 2

14. Model Performance Ozone, PM 2.5 and Sulfate Species Error Bias O 3 17%4% PM 2.5 17% -15% Sulfate 13% -11%

15. Formation of Condensable Organics Condensable products in Nashville Time (hour) Concentrations (  g/m 3 )

16. Formation of Particulate Organics Nashville Time (hour) SOA (  g/m 3 )

17. Hydrophobic vs. Hydrophilic Organics Nashville Time (hour) SOA (  g/m 3 )

18. Sensitivity of Hydrophilic Organics to Henry’s Law Constant Nashville Time (hour) Hydrophilic SOA (  g/m 3 ) RH Base case; H = 1.6 x 10 6 M/atmSensitivity case; H = 10 9 M/atm RH

19. Other Applications of MADRID Nashville –comparison of three SOA modules BRAVO –regional simulation with RADM2 and MADRID 1 Southeast –applications of MADRID 1 and MADRID 2 Eastern United States –application of MADRID 1 for one year for nitrogen deposition

20. Lessons from PM Simulations Accurate PM emission inventories are critical Secondary organic aerosols remain a major source of uncertainty Boundary conditions can have significant effects on O 3 and PM predictions Effects of clouds on sulfate need to be simulated for regional haze Models yet to be tested for wintertime conditions

21. Acknowledgments Funding for this work was provided by EPRI and CARB We would like to thank –J.H. Seinfeld, S. Pandis, M. Jacobson, R. Griffin, and A. Nenes for providing source codes used in MADRID –S. Leduc and F. Binkowski for discussions regarding CMAQ