1. Organic aerosol composition and aging in the atmosphere: How to fit laboratory experiments, field data, and modeling together American Chemical Society Meeting, Denver, CO March 24, 2015 Colette L. Heald and Qi Chen

2. My Conceptual View of Atmospheric Chemistry Research

3. The Importance and Challenge(s) of Organic Aerosol Globally OA makes up 25-75% of total fine aerosol at the surface. Models have difficulty in reproducing the concentration and variability of OA. [Heald et al., 2011] Average aerosol composition for 37 campaigns in the NH [Zhang et al., 2007] 10,000’s of (unidentified?) compounds with variable properties How Global Model Simulation (GEOS-Chem) stacks up to OA measured in 17 airborne field studies

4. [Jimenez et al. 2009] [Cappa et al. 2011] Elemental Composition: Simple Description of Chemical Composition Providing Links to Climate-Relevant Properties Radiative Properties Hygroscopicity (Cloud-Forming Properties) Bulk elemental ratios 10,000’s of (unidentified?) compounds with variable properties O:C H:C

5. Van Krevelen Diagram: Insight Into OA Aging [Heald et al., 2010] Need to re-visit: (1) more data (2) corrected AMS elemental ratios (Canagaratna et al., 2014) Total OA (AMS data) fell on -1 slope, suggesting that aging (mixing, chemistry, volatilization) follow consistent path. We noted levelled off at higher O:C (alcohol addition, fragmentation?) Atmospheric aging

6. Updated Van Krevelen of Ambient Measurements See clear progression in OSc. Fitted slope shallower (-0.6 slope) than Heald et al., 2014 (-1 slope), largely because AMS correction affects O:C more than H:C.

7. But There is Diversity Among Campaigns All individual slopes steeper (-0.7 to -1.0) than bulk …overall fitting compensating for various intercepts

8. A Disconnect Between Laboratory and Ambient Elemental Composition? Most of the laboratory data lies below the ambient line… Except isoprene SOA (low NOx) and glyoxal uptake.

9. Do Photochemical Aging Experiments Resolve This Disconnect? Trajectory of photochemical aging lines up with ambient trajectory. Few aging experiments get to high O:C within ~10 days of aging.

10. Statistical Mixtures Demonstrate the Consistencies and Inconsistencies of Lab and Field Measurements Mixtures can explain some of the difference in trajectory observed across regions. Mis-match suggests that either/both (1)Have not identified important OA source types (2)Laboratory studies are not representative of ambient composition (mixtures?) [Chen et al., submitted] Polluted Isoprene Aircraft Terpenes Riverside

11. Goal: Develop an Observationally-Based Model Simulation of OA Elemental Composition (and Aging) Step 1: Re-fit 2 product SOA yields Step 2: Assign elemental ratios to POA/SOA types simulated in model based on lab data Simulated surface composition occupies a narrow range (O:C = 0.3 to 0.5), compared to wider range seen in ambient.

12. Updated (Very Simple) Aging Scheme Step 3: Account for semi-volatile POA emissions Step 4: Age gas-phase organics based on flow-tube data, but end point constrained by field obs End point: O:C=1.1 H:C=1.4 (defined by field obs) Emissions From Fossil Fuel Biofuel Biomass Burning VOC Hydrophobic O-POA n Oxidation Products SOG i Gas-phaseParticle-phase SOA i Hydrophilic I-POA n Marine Emissions Biogenic Emissions ×0.5 1.15d Isoprene Monoterpenes Sesquiterpenes Aromatics ×0.5 OH, O 3 NO 3 OH, O 3 NO 3 k age, j SVOC j SVOC-SOA 2, j SOG-SOA 1, i k carbon, j ×85% ×15% SVOC-SOA 1, j SOG-SOA 2, i k age, i k carbon, i Marine POA (GEOS-Chem v9-01-03)

13. New Scheme Dramatically Alters Simulation of Elemental Composition Now simulate a wider range of oxygen content, and also more pronounced seasonality in continental regions. O:C Base O:C Updated AgingOSc Updated Aging

14. Comparison With Surface AMS Observations Aging drastically improves ability to capture high O:C in remote regions. But H:C underestimated, consistent with missing sources/pathways for high H:C New scheme also demonstrates better match to observed mass.

15. Vertical Comparison From Airborne Campaigns Similarly, aging is critical to reproducing observed O:C. Cannot simulate O:C>1, or variability in observed H:C. But for airborne measurements, including heterogeneous oxidation helps to reproduce the vertical gradient. [Chen et al., in prep]

16. Conclusions We use the Van Krevelen diagram to explore the composition of OA in lab and field experiments Mixing of OA types can explain much of the ambient variation Missing pathways which maintain high H:C Lab data cannot explain very highest O:C We develop a simple, measurement-based aging scheme for OA Dramatically improves simulation of OA mass (global burden increases by 40%) and elemental composition in remote conditions Including heterogeneous oxidation important for remote/aloft Need better observational constraints for aging Funding Acknowledgement: