1. Modeling Elemental Composition of Organic Aerosol: Exploiting Laboratory and Ambient Measurement and the Implications of the Gap Between Them Qi Chen* (1), Colette L. Heald (1), Jose L. Jimenez (2), Manjula R. Canagaratna (3), Qi Zhang (4), Ling-Yan He (5), Xiao-Feng Huang (5), Pedro Campuzano-Jost (2), Brett B. Palm (2), Douglas Day (2), Laurent Poulain (6), Scot T. Martin (7), Jonathan P. D. Abbatt (8), Alex K.Y. Lee (8), John Liggio (9) *Now at Peking University, China Funded by NSF

2. Insufficient Understanding of Organic Aerosol (OA) [Heald et al., ACP, 2011] Models have difficulty in reproducing the concentration and the variability of organic aerosol. transportation processing chemically constrained by H/C and O/C are variable for different sources vary while aging dictate hygroscopicity and particle density ???

3. Exploiting Ambient and Laboratory Measurement [Heald et al., GRL, 2010] [Ng et al., ACP, 2011] Need to re-visit: (1) more real-time data (2) corrected AMS elemental ratios an increase of 14-45% in O:C and of 7-20% in H:C for ambient OA (Canagaratna et al., 2014) [Simon and Bhave, et al, EST, 2012]

4. The New Dataset of OA Elemental Composition We synthesize a dataset of both laboratory and ambient observations of the OA elemental ratios, including unpublished results. This dataset contains a total of 56 surface observations (rural/remote, pollution/fire, and downwind conditions are all represented), three aircraft measurements, and chamber/flow-tube results. Comparisons between ambient and laboratory data are made.

5. Ambient Observations campaign-average Slope=-0.6 Intercept=2.0 Individual slopes are steeper ( − 0.7 to − 1.0), suggesting that the mean fit is compensating for various intercepts. diversity

6. Laboratory Measurements

7. Lab vs. Field #1: Statistical Mixtures Compared to Ambient Consistencies missing sources and pathways which maintain high H:C in areas polluted areas Low NOx isoprene chemistry and glyoxal-type of aqueous-phase chemistry can drive the match lab experiments do not adequately mimic ambient (mixtures? extend of aging?)

8. Lab vs. Field #2: Observationally-Based Model Simulation Step 1: SOA yields to reflect recent measurements Step 2: Account for semi-volatile POA emissions Step 3: Assign elemental ratios to POA/SOA types simulated in model based on lab data Step 4: Age gas-phase organics based on flow-tube data but end point constrained by field obs. (50% increase in burden) 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 End point: O:C=1.1 H:C=1.4 (defined by field obs) (GEOS-Chem v9-01-03)

9. Aging Dramatically Alters Simulation of OA Elemental Composition Aging leads to - more pronounced spatial variability (a wider range) - more pronounced seasonality over continents Surface distributions O:C H:C

10. Model Simulations Compared to Surface Observations The model performance in remote regions is largely improved by aging. H:C are underestimated, consistent with missing sources or pathways for high H:C.

11. Heterogeneous oxidation effectively helps to reproduce the vertical gradient. O:COA H:C Cannot reproduce variability in observed H:C. 1×10 -13 cm 3 molecule −1 s −1

12. Conclusions The disconnect between laboratory and ambient OA elemental composition, especially for areas influenced by pollution and/or fires -> missing sources and/or pathways which maintain high H:C -> linked to missing OA mass in those regions Simple, measurement-based aging scheme largely improves simulation of elemental composition. Including heterogeneous oxidation helps reproduce the vertical profile. Thank you!