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 (SOAcld) 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 (SOAcld).

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 particulate

organic 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 laboratory

and 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 aircraft

water 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) from

precursor 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 low

volatility 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 substantial

component, 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 A
Most 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 from

laboratory 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 current

cloud 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 SOAcld 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 of

Protected 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 SOA

formation 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 (kGLY-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 CO2 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 cloud

parcel 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%) was

used 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 Flightsa

base CMAQ

model

CMAQ with

SOAcld 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

SOAcld flight

mean carbon

(<μg 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

SOAcldwas 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 μg m-3 for STN and 1.9 μg m-3 for IMPROVE. Without

SOAcld, CMAQ predicted monthly averages were 2.0 μg m-3

for STN and 1.1 μg m-3 for IMPROVE. Monthly averages

increased to 2.2 μg m-3 and 1.3 μg 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 SOAcloud 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 SOAcld

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 C
and 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 μg m-3,

and for total SOA, it was ∼10 μg 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 experiment

flight. 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, potentially

explaining 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 research

was 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. Model

sensitivity 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 pxylene. 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 SOA

production 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 Gulf

of 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): An

extended 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 IIsParticulate 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