Subscriber access provided by US EPA - RTP LIBRARY

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 

# Article


# CMAQ Model Performance Enhanced When In-Cloud

Secondary Organic Aerosol is Included: Comparisons of Organic Carbon Predictions with Measurements Annmarie G. Carlton, Barbara J. Turpin, Katye E. Altieri, Sybil P. Seitzinger, Rohit Mathur, Shawn J. Roselle, and Rodney J. Weber

Environ. Sci. Technol.

, Article ASAP • DOI: 10.1021/es801192n Downloaded from http://pubs.acs.org on November 23, 2008



## More About This Article

Additional resources and features associated with this article are available within the HTML version:

• Supporting Information

• Access to high resolution figures

• Links to articles and content related to this article

• Copyright permission to reproduce figures and/or text from this article

# CMAQ Model Performance Enhanced When In-Cloud Secondary Organic Aerosol is Included: Comparisons of Organic Carbon Predictions with Measurements

ANNMARIE G. CARLTON,* , † BARBARA J. TURPIN, ‡ KATYE E. ALTIERI, § SYBIL P. SEITZINGER, § , | ROHIT MATHUR, † SHAWN J. ROSELLE, † AND RODNEY J. WEBER ⊥ Air Resources Laboratory, Atmospheric Sciences Modeling Division, National Oceanic and Atmospheric Administration, 109 TW Alexander Drive, Durham, North Carolina 27711, Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, New Jersey 08901, Institute of Marine and Coastal Sciences and Rutgers/NOAA CMER Program, Rutgers University, 71 Dudley Road, New Brunswick, New Jersey 08901, and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332

Received April 30, 2008. Revised manuscript received July 31, 2008. Accepted September 30, 2008.



### Mounting evidence suggests that low-volatility (particlephase) organic compounds form in the atmosphere through aqueous phase reactions in clouds and aerosols. Although some models have begun including secondary organic aerosol (SOA) formation through cloud processing, validation studies that compare predictions and measurements are needed. In this work, agreement between modeled organic carbon (OC) and aircraft measurements of water soluble OC improved for all 5 of the compared ICARTT NOAA-P3 flights during August when an in-cloud SOA (SOA cld 

### ) formation mechanism was added to CMAQ(a regional-scale atmosphericmodel). The improvement was most dramatic for the August 14th flight, a flight designed specifically to investigate clouds. During this flight, the normalized mean bias for layer-averaged OC was reduced from

- 64 to

- 15% and correlation ( r ) improved from 0.5 to 0.6. Underpredictions of OC aloft by atmospheric models may be explained, in part, by this formation mechanism (SOA cld 

### ). OC formation aloft contributes to long-range pollution transport and has implications to radiative forcing, regional air quality and climate. 

## Introduction

Despite the importance of atmospheric organic particulate matter (PM) to climate, air quality, and health, its sources and formation remain poorly understood ( 1, 2 ). Most global and regional models fail to accurately predict particulateorganic carbon (OC) concentrations. Organic PM is underpredicted in summer ( 3 ), especially in the free troposphere ( 4); variations in OC concentrations are not captured well by atmospheric models ( 5 ). Errors in emission estimates, meteorology, and uncertainties in secondary organic aerosol (SOA) partitioning are considered unlikely causes for the disagreement between predicted and observed particulate OC aloft ( 4). Discrepancies are believed to result largely from poorly understood SOA formation processes; 20

- 50% of continental organic PM at the mid latitudes is of secondary origin ( 1 ). The cloud processing hypothesis for SOA formation ( 6 ) is that water-soluble oxidation products of reactive organic compounds partition into cloud droplets, oxidize further, and create low-volatility compounds that remain, in part, in the particle phase upon droplet evaporation ( > 90% of cloud droplets evaporate ( 7 )). Cloud-production of SOA (SOAcld ) is a secondary PM formation process supported by laboratoryand field evidence but currently missing from most atmospheric models. This missing pathway provides a plausible explanation for the recently noted failure of models to predict accurate vertical, spatial, and temporal OC profiles. Including (SOAcld ) in atmospheric models will add OC at multiple elevations with temporal and spatial variability. Results from a recent modeling study that included SOAcld suggest that this process is atmospherically important ( 8 ). However, validating measurement comparisons are still needed. In this work, model OC predictions were evaluated using aircraftwater soluble OC concentrations from five flights (summarized in Table 1) and a month of OC concentrations from two ground-level air monitoring networks.



## In-Cloud SOA: Plausibility and Current Understanding

Cloud production of secondary inorganic aerosol (i.e., sulfate) is well documented and growing evidence supports cloud production of SOA. Temporal and diurnal concentration dynamics support in-cloud production of low volatility carboxylic acids in the atmosphere (e.g., oxalic acid) fromprecursor water-soluble aldehydes (e.g., glyoxal and methylglyoxal) ( 9 ) and zero-dimensional modeling demonstrates kinetic feasibility for a variety of additional precursors (e.g., glyocolaldehyde, glycolic acid, pyruvic acid) ( 10

- 13 ). Glyoxal and methylglyoxal are highly water-soluble compounds found widely in the atmosphere with anthropogenic and biogenic precursors ( 14, 15 ). Oxalic acid (usually measured as oxalate and predominantly present as the monoanion at typical cloud pH) is found widely in the environment, primarily in the particle phase ( 16 ); primary sources are not sufficient to explain observed concentrations ( 17 ). Positive correlations between atmospheric oxalate and sulfate have been documented in an array of field investigations; in-cloud formation provides the likely explanation ( 18, 19 ). Recent laboratory evidence confirms that oxalic acid and other lowvolatility species form in the aqueous phase during photooxidation of pyruvic acid ( 13 ), glyoxal ( 20, 21 ), and methylglyoxal ( 22 ). Aircraft measurements indicate elevated organic acid concentrations, with oxalic acid as substantialcomponent, in layers above clouds, presumably as a consequence of aqueous chemistry ( 23

- 25 ). * Corresponding author e-mail: carlton.annmarie@epa.gov; phone: 919-541-1451; fax: 919-541-1379.

† Air Resources Laboratory, Atmospheric Sciences Modeling Division, National Oceanic and Atmospheric Administration.

‡ Department of Environmental Sciences, Rutgers University.

§ Institute of Marine and Coastal Sciences, Rutgers University.

| Rutgers/NOAA CMER Program, Rutgers University.

⊥ School of Earth and Atmospheric Sciences, Georgia Institute of Technology. 

### Environ. Sci. Technol. XXXX, xxx, 000–000

**10.1021/es801192n CCC: $40.75**  **XXXX American Chemical Society VOL. xxx, NO. xx, XXXX / ENVIRONMENTAL SCIENCE & TECHNOLOGY** 9 AMost notably, formation of SOA from cloud processing of isoprene oxidation products is predicted to affect air quality ( 8

). However, SOAcld modeling efforts remain to be validated with measurements. Reaction rate constants used in previous cloud chemistry models ( 11, 12 ) were chiefly developed fromlaboratory experiments that measured reactant disappearance; reaction mechanisms and products were mostly assumed from bond strength calculations. This represents a substantial knowledge gap. To validate and refine currentcloud chemistry mechanisms, aqueous photochemical experimentswithdetailedproductanalysiswereperformedforpyruvic acid, glyoxal, and methylglyoxal with hydroxyl radical, the predominant aqueous phase atmospheric oxidant ( 26 ). Recent laboratory experiments demonstrate that previous aqueous phase mechanisms inadequately describe oxidation pathways, rates, and products ( 13, 20

- 22 ). The chemical transport modeling described in this paper makes use of yields from those laboratory photooxidation experiments. Daily OC predictions from Community Multiscale Air Quality (CMAQ) model simulations (August 1

- 31, 2004) conducted with and without SOA cld were compared with ground-based measurements of OC from two monitoring networks and with aircraft measurements of water soluble OC (WSOC). CMAQ model performance improved when SOAcld was included. Addition of SOAcld reduced the normalized mean bias between CMAQ OC predictions and ground level OC measurements from the Speciation Trends Network (STN; urban) and from the Interagency Monitoring ofProtected Visual Environments (IMPROVE; rural/background) network. Correlations increased and bias was reduced when CMAQ OC predictions with SOAcld were compared with WSOC concentrations aloft measured with a particle-into-liquid sampler (PILS) aboard the NOAA P3 aircraft ( 27) during the International Consortium for Atmospheric Research and Transformation (ICARTT) campaign. 

## Model Development, Application, and Limitations
An in-cloud SOA formation parametrization was included in a regional scale chemical transport model, the Models-3 Community Multiscale Air Quality (CMAQ), model version 4.6 (www.cmascenter.org). Simulations for August 1

- 31, 2004 were conducted for the eastern U.S. (12 km horizontal resolution, vertical extent from the surface to 100 mb) twice, once with and once without the in-cloud SOA mechanism. The meteorology for these simulations was developed using the MM5, version 3.6.3 model (Supporting Information). Addition of cloud produced SOA did not reduce SOAformation from other pathways because glyoxal and methylglyoxal are not precursors for other types of SOA in CMAQv.4.6 and • OH is not an advected species (e.g., consumption occurs only for an individual time step). SOA precursors in CMAQv4.6 are monoterpenes, alkanes, xylene, creosol, and toluene. A detailed description of CMAQ aerosol processes and SOA formation is discussed in ref 28. Additional CMAQ configuration and input details are in the Supporting Information. Gas-phase concentrations of glyoxal and methylglyoxal were simulated as oxidation products of isoprene and aromatics using the SAPRC mechanism ( 29 ). Water-soluble fractions of glyoxal, methylglyoxal, and • OH partitioned into the aqueous phase (according to Henry’s Law) and reacted, consistent with measured reaction rates ( k GLY-OH ) 3E10 ( 21 ), kMGLY assumed to be the same). SOAcld yields (1

- 10%) (molar) were applied stoichiometrically within the CMAQ aqueous chemistry module (aqchem) to the reacted fractions of glyoxal and methylglyoxal. Organic mass not retained as aerosol is assumed to oxidize to CO 2 and the yield value accounts for this loss. All gas-, aqueous-, and aerosol-phase species were subject to wet deposition. Recent laboratory findings suggest SOAcld yields range from 1 to 10% (molar) resulting from large, higher molecular weight compounds, in addition to monomeric carboxylic acids formed during aqueous photooxidation. The oxalic acid yield (mass) from aqueous photooxidation of glyoxal at 10 min of reaction time (typical of cloud contact time for one cloud cycle ( 11 ) has been reported as 1% ( 21 ). This yield was converted to a molar basis, and a 3% molar yield for higher molecular weight species was estimated from total organic carbon (TOC) measurements at 10 min (TOC minus sum of quantified species). Their sum (4%) is the total SOAcld molar yield used in these simulations (further information provided in the Supporting Information), but molar yields can range from ∼ 1% for oxalic acid alone (from glyoxal) and ∼ 10% for oxalic acid and higher molecular weight species. Uncertainty arises when yields calculated from laboratory experiments conducted under controlled conditions (e.g., pH ) 4.1

- 4.8 ( 21 )) are extrapolated to the atmosphere. However cloudparcel modeling with explicit kinetic aqueous phase chemistry demonstrates little sensitivity in SOAcld yields to pH; cloud contact time is the most sensitive parameter ( 30 ). To describe uncertainty in yield values, the range (1

- 10%) wasused to provide bounds and describe uncertainty in modelpredicted OC concentrations in Figure 1 (gray shading). When the glyoxal kinetic mechanism was explicitly incorporated into a cloud parcel model ( 30 ), the calculated SOAcld yield from isoprene at VOC/NOx ratios ∼ 5, (typical of CMAQpredicted ratios in these simulations at cloud-relevant altitudes), supports a higher yield from glyoxal/methylglyoxal than used here, suggesting these results are conservative. CMAQ model evaluation was conducted using WSOC concentrations from five ICARTT flights (Table 1, Figure S-1,Supporting Information) and with a month of daily averaged OC concentrations from STN and IMPROVE. CMAQ OC predictions were calculated from organic aerosol mass predictions using organic matter-to-organic carbon (OM/ OC) ratios for all organic aerosol species ( 31 ). It should be noted there is some difference between these metrics (i.e., WSOC represents only the water-soluble fraction of total OC). This difference is diminished however when SOA is a substantial contributor to OC because SOA is oxygenated/ water soluble. 

### TABLE 1

. 

### Statistical Measures of CMAQ-Predicted OC and WSOC Measurements for ICARTT Flights a

base CMAQ

model CMAQ with SOA cld flight

**correlation ( r**  **) 0.77 0.78 3-Aug 0.76 0.81 6-Aug**  **0.15 0.19 11-Aug 0.47 0.57 14-Aug**  **0.92 0.93 15-Aug**

**normalized mean**

**bias** - **100%**

- **98% 3-Aug**

- **59%**

- **49% 6-Aug**

- **68%**

- **66% 11-Aug**

- **64%**

- **15% 14-Aug**

- **82%**

- **61% 15-Aug**

WSOC
measurements OC base CMAQ OC With SOA cld flight

**mean carbon**

**(** < μ **g m** - **3 )**  **2.4 0.7 0.8 3-Aug 0.9 0.4 0.5 6-Aug**  **3.4 1.0 1.0 11-Aug 0.9 0.2 0.6 14-Aug**  **1.5 0.3 0.5 15-Aug**

**a Note: Statistical measures for ICARTT comparisons are**  **for layer-averaged CMAQ-predicted OC and PILS-measured WSOC.** 

B 9 **ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. xxx, NO. xx, XXXX**

## Results
Model performance for predicting OC was enhanced when SOAcld was included, as demonstrated by reduced normalized mean biases and improved correlations for all five ICARTT P3 flights (Table 1). Agreement between predicted OC and measured WSOC was particularly improved for the August 14th cloud experiment NOAA P3 flight (Figures 1 and 2). The August 14th flight was selected for comparison because the flight was specifically designed to investigate clouds, whereas other flights typically focused on urban or power plant plumes (Figure S-1, Supporting Information); in addition, this flight did not appear to be impacted by wild fires (Figure S-2,Supporting Information). The time series (Figure 1) and layeraveraged values (Figure 2) demonstrate that the base CMAQ model drastically underpredicts OC. Predictions that include SOAcld show excellent agreement with WSOC measurements.Substantial enhancement of model performance is particularly noted aloft in the improved vertical OC profile (Figure 2). The normalized mean bias for layer-averaged OC predictions during the August 14 flight was reduced from

- 64% to

- 15% when SOAcld was included. At ∼ 15 UTC (Figure 1), the NOAA P3 was flying within a cloud (RH measurements at ∼ 100%) not predicted by the model near the horizontal domain boundary. In addition, at this time, simulated concentrations near the horizontal domain boundaries were influenced by boundary conditions (which did not include SOAcld ) because there was inflow from outside the domain. Hence, at ∼ 15 UTC, CMAQ predictions of OC with and without SOAcld are excluded from the evaluation. At ∼ 19 UTC when the aircraft encounters an urban plume (strong covariance of CO and WSOC, Figure S-2, Supporting Information), CMAQ underpredicts OC in the plume even with SOAcld included. Note that model predictions in the surface layer ( < ∼ 34 m) were also affected. Surface OC predictions were evaluated in urban areas with data from STN and in rural and background areas with data from the IMPROVE network. The monthly averaged August 2004 OC observations were 2.6 μ g m - 3 for STN and 1.9 μ g m - 3 for IMPROVE. Without SOAcld , CMAQ predicted monthly averages were 2.0 μ g m - 3 for STN and 1.1 μ g m - 3 for IMPROVE. Monthly averages increased to 2.2 μ g m - 3 and 1.3 μ g m - 3 for STN and IMPROVE, respectively when SOAcld was included. Normalized mean bias calculated from the August daily averaged total carbon observations were reduced from

- 44% to

- 22% for IMPROVE FIGURE 1. CMAQ-predicted OC and WSOC measurements (black) made from the August 14 NOAA-P3 flight during ICARTT. Base CMAQ OC predictions (blue) and CMAQ OC prediction including cloud-produced SOA (red) are plotted on the primary y -axis; plane altitude (solid line) is plotted on the secondary y -axis. Note agreement between measured WSOC and OC (when SOA cloud is included) in particular at elevated altitude. Gray area indicates uncertainty. The lower bound assumes a 2% mass yield (oxalic acid only) and the higher bound assumes a 32% mass yield (oxalic acid and higher molecular weight compounds).

FIGURE 2. Layer-averaged vertical profiles of OC and WSOC on August 14, 2004. Normalized mean bias for layer-average values for this flight was reduced from

- 65% to

- 15% when SOA cld was included. Note: Dashed line and “ × ” indicates layer-averaged base CMAQ OC prediction. Solid line and “ O ” indicates CMAQ OC prediction with cloud-produced SOA included. WSOC observations, from the NOAA P3 flight are indicated with “ 2 ”. The x -axis is log scale.

**VOL. xxx, NO. xx, XXXX / ENVIRONMENTAL SCIENCE & TECHNOLOGY** 9 Cand from

- 33% to

- 16% for STN, when SOAcld was included. Correlations at the surface between CMAQ predictions with and without SOAcld did not improve for the daily averaged values at either surface network. Cloud-produced SOA predictions varied in time and space, reflecting meteorology, for example, cloud fields, suggesting SOAcld may be an episodic phenomenon, though regional signals were observed (Figure S-3, Supporting Information). On August 14th, predicted daily averaged SOAcld concentrations are negligible in certain areas of the domain, while in others they are substantial. The August 14th maximum predicted 24-h average SOAcld concentration was ∼ 5 μ g m - 3

, and for total SOA, it was ∼ 10 μ g m - 3 ; these maxima occurred at different locations. The highest SOAcld concentrations and fractions tended to be in areas with the most cloud cover. At certain locations SOAcld was the dominant component of predicted SOA (Figure S-3, Supporting Information). This was particularly true over the ocean. While SOAcld is predicted to have a low concentration over the ocean, other SOA species are predicted to be even lower, making the SOAcld fraction large. Monoterpene SOA, for example, is not efficiently transported to the remote marine environment. In addition, isoprene SOA, which is expected to contribute to remote marine OC concentrations ( 32 ), is not included in this CMAQ version.



## Discussion and Implications

Including SOA formed through cloud processing improved OC model and measurement agreement for all five P3 flights and most substantially for the August 14th cloud experimentflight. SOA from cloud processing of glyoxal and methylglyoxal substantially increased organic PM mass concentrations in areas influenced by clouds. Increases in OC concentrations are noted for all vertical model layers, especially aloft. Elevated OC concentrations aloft have been observed in several locations (e.g., in the Eastern U.S.) ( 5, 33 ) and in particular, in cloud-influenced air parcels ( 23 ). Other studies report ground-level SOA concentrations that are elevated when air from aloft is brought to the surface ( 34, 35 ). In addition to OC mass increases, improved correlation for compared modeled/measured organic aerosol for all five flights, suggests that inclusion of cloud processing may improve capture of OC variability, which is currently poor ( 5

). Aqueous chemistry in fair weather cumulus venting to the free troposphere was identified as a source of nitric acid in the free troposphere during ICARTT ( 36 ). Similarly, cloud processing can result in substantial SOA aloft, potentiallyexplaining elevated OC measurements in the free troposphere. SOA produced aloft can be transported long distances and thus can impact air quality regionally. On the basis of our findings, continued investigation of cloud-produced organic aerosol in three-dimensional atmospheric models is warranted.



## Acknowledgments This research has been supported in part by a grant from the U.S. Environmental Protection Agency’s (EPA) Science to Achieve Results (STAR) program (R833751). This researchwas performed, in part, under the Memorandum of Understanding between the EPA through its office of Research and Development and the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA)under agreement number DW13921548. This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies or views. 

## Supporting Information Available P3 flight tracks, acetonitrile and CO oncentrations with cloud data for the August 14th flight, spatial profiles of predicted SOAcld concentrations and fractions (of total SOA) in addition to carbon measurements from glyoxal experiments. This material is available free of charge via the Internet at http:// pubs.acs.org.



## Literature Cited

(1) Kanakidou, M.; Seinfeld, J. H.; Pandis, S. N.; Barnes, I.; Dentener, F. J.; Facchini, M. C.; Dingenen, R. V.; Ervens, B.; Nenes, A.; Nielsen, C. J.; Swietlicki, E.; Putaud, J. P.; Balkanski, Y.; Fuzzi, S.; Horth, H.; Moortgat, G. K.; Winterhalter, R.; Myhre, C. E. L.; Tsigaridis, K.; Vignati, E.; Stephaou, E. G.; Wilson, J. Organic aerosol and global climate modelling: a review. Atmos. Chem. Phys 2005
, 5 , 1053–1123. (2) Goldstein, A. H.; Galbally, I. E. Known and unexplored organic constituents in the earth’s atmosphere. Environ. Sci. Technol. 2007
, 41 (5), 1514–1521. (3) Morris, R. E.; Koo, B.; Guenther, A.; Yarwood, G.; McNally, D.; Tesche, T. W.; Tonnesen, G.; Boylan, J.; Brewer, P. Modelsensitivity evaluation for organic carbon using two multipollutant air quality models that simulate regional haze in the southeastern United States. Atmos. Environ. 2006
, 40 (26), 4960– 4972. (4) Heald, C. L.; Jacob, D. J.; Park, R. J.; Russell, L. M.; Huebert, B. J.; Seinfeld, J. H.; Liao, H.; Weber, R. J. A large organic aerosol source in the free troposphere missing from current models. Geophys. Res. Lett. 2005
, 32 , L18809DOI: 10.1029/2005GL023831. (5) Heald, C. L.; Jacob, D. J.; Turquety, S.; Hudman, R. C.; Weber, R. J.; Sullivan, A. P.; Peltier, R. E.; Atlas, E. L.; de Gouw, J. A.; Warneke, C.; Holloway, J. S.; Neuman, J. A.; Flocke, F. M.; Seinfeld, J. H. Concentrations and sources of organic carbon aerosols in the free troposphere over North America. J. Geophys. Res.-Atmos. 2006
, 111 , D14310DOI:10.1029/2005JD006847. (6) Blando, J. D.; Turpin, B. J. Secondary organic aerosol formation in cloud and fog droplets: A literature evaluation of plausibility. Atmos. Environ. 2000
, 34 , 1623–1632. (7) Pruppacher, H. R.; Klett, J. D. , Microphysics of Clouds and Precipiations; D. Eridel: Boston, 1978; p 349. (8) Chen, J.; Griffin, R. J.; Grini, A.; Tulet, P. Modeling secondary organic aerosol fomration through cloud processing of organic compounds. Atmos. Chem.Phys. 2007
, 7 , 5343–5355. (9) Chebbi, A.; Carlier, P. Carboxylic acids in the troposphere, occurrence, sources and sinks: A review. Atmos. Environ. 1996
, 30 (24), 4233–4249. (10) Warneck, P. In-cloud chemistry opens pathway to the formation of oxalic acid in the marine atmosphere. Atmos. Environ. 2003
, 37 (17), 2423–2427. (11) Ervens, B.; Feingold, G.; Frost, G. J.; Kreidenweis, S. M. A modeling study of aqueous production of dicarboxylic acids: 1. Chemical pathways and speciated organic mass production. J. Geophys. Res. 2004
, 109, D15205DOI:10.1029/2003JD004387. (12) Lim, H.-J.; Carlton, A. G.; Turpin, B. J. Isoprene forms secondary organic aerosol through cloud processing: model simulations. Environ. Sci. Technol. 2005
, 39, 4441–4446. (13) Carlton, A. G.; Lim, H.-J.; Altieri, K.; Seitinger, S.; Turpin, B. J. Link between isoprene and SOA: Fate of pyruvic acid in dilute aqueous solution. Geophys. Res. Lett. 2006
, 33, L06822DOI: 10.1029/2005GL025374. (14) Volkamer, R.; Platt, U.; Wirtz, K. Primary and secondary glyoxal formation from aromatics: Experimental evidence for the bicycloalkyl-radical pathway from benzene, toluene, and p

xylene. J. Phys.Chem. A 2001
, 105 (33), 7865–7874. (15) Spaulding, R. S.; Schade, G. W.; Goldstein, A. H.; Charles, M. J. Characterization of secondary atmospheric photooxidation products: Evidence for biogenic and anthropogenic sources. J. Geophys. Res., Atmos. 2003
, 108 (D8), 4247 DOI:10.1029/ 2002JD002478. (16) Martinelango, P. K.; Dasgupta, P. K.; Al-Horr, R. S. Atmospheric production of oxalic acid/oxalate and nitric acid/nitrate in the Tampa Bay airshed: parallel pathways. Atmos. Environ. 2007
, 41 , 4258–4269. (17) Huang, X.-F.; Yu, J. Z. Is vehicle exhaust a significant primary source of oxalic acid in ambient aerosols? Geophys. Res. Lett. 2007
, 34 , L02808DOI:10.1029/2006GL028457. (18) Crahan, K. K.; Hegg, D.; Covert, D. S.; Jonsson, H. An exploration of aqueous oxalic acid production in the coastal marine atmosphere. Atmos. Environ. 2004
, 23, 3757–3764.

D 9 **ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. xxx, NO. xx, XXXX**(19) Yu, J. Z.; Huang, X.-F.; Xu, J.; Hu, M. When aerosol sulfate goes up, so does oxalate: Implication for the formation mechanisms of oxalate. Environ. Sci. Technol. 2005
, 29, 128–133. (20) Altieri, K. E.; Carlton, A. G.; Turpin, B. J.; Seitzinger, S. Formation of oligomers in cloud-processing: reactions of isoprene oxidation products. Environ. Sci. Technol. 2006
, 40
, 4956–4960. (21) Carlton, A. G.; Turpin, B. J.; Altieri, K. E.; Reff, A.; Seitzinger, S.; Lim, H. J.; Ervens, B. Atmospheric oxalic acid and SOAproduction from glyoxal: Results of aqueous photooxidation experiments. Atmos. Environ. 2007
, 41 (35), 7588–7602. (22) Altieri, K. E.; Seitzinger, S. P.; Carlton, A. G.; Turpin, B. J.; Klein, G. C.; Marshall, A. G. Oligomers formed through in-cloud methylglyoxal reactions: chemical composition, properties, and mechanisms investigated by ultra-high resolution FT-ICR mass spectrometry. Atmos. Environ. 2008
, 42, 1476–1490, DOI: 10.1016/j.atmosenv.2007.11.015. (23) Sorooshian, A.; Varutbangkul, V.; Brechtel, F. J.; Ervens, B.; Feingold, G.; Bahreini, R.; Murphy, S. M.; Holloway, J. S.; Atlas, E. L.; Buzorius, G.; Jonsson, H.; Flagan, R. C.; Seinfeld, J. H. Oxalic acid in clear and cloudy atmospheres: Analysis of data from ICARTT 2004. J. Geophys. Res., Atmos. 2006
, 111 (D23), D23S45. (24) Sorooshian, A.; Ng, N. L.; Chan, A. W. H.; Feingold, G.; Flagan,R. C.; Seinfeld, J. H. Particulate organic acids and overall watersoluble aerosol composition measurements from the 2006 Gulfof Mexico atmospheric composition and climate study (GoMACS). J. Geophys. Res., Atmos. 2007
, 112, D13201DOI: 10.1029/ 2007/JD008537. (25) Sorooshian, A.; Lu, M. L.; Brechtel, F. J.; Jonsson, H.; Feingold, G.; Flagan, R. C.; Seinfeld, J. H. On the source of organic acid aerosol layers above clouds. Environ. Sci. Technol. 2007
, 41 (13), 4647–4654. (26) Ervens, B.; George, C.; Williams; J, E.; Buxton, G. V.; Salmon, G. A.; Bydder, M.; Wilkinson, F.; Dentener, F.; Mirabel, P.; Wolke, R.; Herrmann, H. CAPRAM 2.4 (MODAC mechanism): Anextended and condensed tropospheric aqueous phase mechanism and its application. J. Geophys. Res.-Atmos. 2003
, 108 (D14), 4426 DOI: 10.1029/2002JD002202. (27) Sullivan, A. P.; Peltier, R. E.; Brock, C. A.; de Gouw, J. A.; Holloway, J. S.; Warneke, C.; Wollny, A. G.; Weber, R. J. Airborne measurements of carbonaceous aerosol soluble in water over northeastern United States: Method development and an investigation into water-soluble organic carbon sources. J. Geophys. Res., Atmos. 2006
, 111 (D23), 14. (28) Yu, S.; Bhave, P. V.; Dennis, R. L.; Mathur, R. Seasonal and regional variations of primary and secondary organic aerosols over the Continental United States: Semi-empirical estimates and model evaluation. Environ. Sci. Technol. 2007
, 41 (13), 4690– 4697. (29) Carter, W. P. L. Implementation of the SAPRC-99 Chemical Mechanism into the Models-3 Framework ; U.S. Environmental Protection Agency: Washington, DC, 2000. (30) Ervens, B.; Carlton, A. G.; Turpin, B. J.; Altieri, K. E.; Kreidenweis, S. M.; Feingold, G. Secondary organic aerosol yields from cloud processing upon isoprene oxidation. Geophys. Res. Lett. 2008
, 35

, L02816DOI: 10.1029/2007GL031828. (31) Appel, K. W.; Bhave, P. V.; Gilliland, A. B.; Sarwar, G.; Roselle, S. J. Evaluation of the community multiscale air quality (CMAQ) model version 4.5: sensitivities impacting model performance; Part II

s Particulate matter Atmos. Environ. 2008 , In press. (32) Henze, D. K.; Seinfeld, J. H. Global secondary organic aerosol from isoprene oxidation. Geophys. Res. Lett. 2006
, 33, L09812doi: 10.1029/2006GL025976. (33) Novakov, T.; Hegg, D. A.; Hobbs, P. V. Airborne measurements of carbonaceous aerosols on the East Coast of the United States. J. Geophys. Res. 1997
, 102, 30023–30030. (34) Lim, H.-J.; Turpin, B. J. Origins of primary and secondary organic aerosol in Atlanta: Results of time-resolved measurements during the Atlanta supersite experiment. Environ. Sci. Technol. 2002
, 36 (21), 4489–4496. (35) Polidori, A.; Turpin, B. J.; Lim, H. J.; Cabada, J. C.; Subramanian, R.; Robinson, A. L.; Pandis, S. N. Local and regional secondary organic aerosol: Insights from a year of semi-continuous measurements at Pittsburgh. Aerosol. Sci. Technol. 2006
, 40
, 861–872. (36) Hudman, R. C.; Jacob, D. J.; Turquety, S.; Leibensperger, E. M.; Murray, L. T.; Wu, S.; Gilliland, A. B.; Avery, M.; Bertram, T. H.; Brune, W.; Cohen, R. C.; Dibb, J. E.; Flocke, F. M.; Fried, A.; Holloway, J.; Neuman, J. A.; Orville, R.; Perring, A.; Ren, X.; Sachse, G. W.; Singh, H. B.; Swanson, A.; Wooldridge, P. J. Surface and lightning sources of nitrogen oxides over the United States: Magnitudes, chemical evolution, and outflow. J. Geophys. Res.- Atmos. 2007
, 112, D12.

ES801192N

**VOL. xxx, NO. xx, XXXX / ENVIRONMENTAL SCIENCE & TECHNOLOGY** 9 E