Organic aerosol composition and aging in the atmosphere: How to fit laboratory experiments, field data, and modeling together American Chemical Society

Organic aerosol composition and aging in the atmosphere:

How to fit laboratory experiments, field data, and modeling together

American Chemical Society Meeting, Denver, COMarch 24, 2015

Colette L. Heald and Qi Chen

My Conceptual View of Atmospheric Chemistry Research

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

[Jimenez et al. 2009][Cappa et al. 2011]

Elemental Composition: Simple Description of Chemical Composition Providing Links to Climate-Relevant Properties

Radiative PropertiesHygroscopicity (Cloud-Forming Properties)

Bulk elemental ratios10,000’s of (unidentified?) compounds with variable properties

O:CH:C

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

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.

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

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(c)

Ground Urban Downwind Remote/Rural (HR-AMS) Urban Downwind Remote/Rural (Q-AMS)

Aircraft (HR-AMS) MILAGRO <0.5km MILAGRO 6-8km DC-3 <0.5km DC-3 6-8km

— Fitted to Ambient Means (R2 = 0.67)Slope = -0.58 ± 0.04 (1); Intercept = 1.96 ± 0.03 Fitted to invididual datasets (HR-AMS)(shown for the data range) — Urban — Downwind — Remote/Rural — Aircraft Laboratory-generated (all methods)Biomass burning OA (b)Anthropogenic POA(d/g - diesel/gasoline exhaust; c - cooking; t - trash burning)Biogenic SOA (I - isoprene; L - limonene; M - monoterpene; S - sesquiterpene)Aromatic SOA(X - xylene; T - toluene; Z - benze; r - others)Fresh IVOC SOA(n - naphthalene; p - phenol; o - o-cresol; x - dimethylphenol; a - C8 to C19 alkane)Glyoxal aqueous uptake (G)IEPOX-SOA (E)monoterpene ELVOC (e)(all at low-NOx except that *n represents high NOx) Other types of OAMarine Emissions (R) Laboratory photochemical aging

1.61.20.80.40

O:C

Anthropogenic (POA+SVOC/IVOC)

(d)

SOA (gas + particle)

1086420Biomass burning (POA+SVOC/IVOC)

(days)Heterogeneous Oxidation

squalane OA Lubricating oil particles glyoxal OA (aquesous)

(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC

UptonMILAGROWhistler Mtn.

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

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1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


But There is Diversity Among Campaigns

All individual slopes steeper (-0.7 to -1.0) than bulk …overall fitting compensating for various intercepts

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

n nn nn

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

n nn nn

nnn

n

ppp

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X

X

X

TTT

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rrrr rr

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III IIIII III

InInInInSSSSSSSS

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dg

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dt

tcc

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

n nn nn

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ppp

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X

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


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.

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

n nn nn

nnn

n

ppp

ooooxxxx



X

X

X

TTT

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rrrr rr

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b

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III IIIII III

InInInInSSSSSSSS

S

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dg

dd

dt

tcc

ccccGGG

EE

R

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

n nn nn

nnn

n

ppp

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X

X

X

TTT

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rrrr rr

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bbb

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tcc

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


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.

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

n nn nn

nnn

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X

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

n nn nn

nnn

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ppp

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X

X

X

TTT

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rrrr rr

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III IIIII III

InInInInSSSSSSSS

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dt

tcc

ccccGGG

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

n nn nn

nnn

n

ppp

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X

X

X

TTT

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rrrr rr

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III IIIII III

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


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]1.20.80.40.0

O:C

(f) Rural TSOA & Aging(g/p)

SGPMelpitz(s)

Add Aged ASOAAdd Aged BBOA

1.20.80.40.0

(g) Rainforest TSOA & Aging(g/p)ISOA & Aging(g/p)IEPOXp, ELVOC

AmazonBorneo

Add Aged (APOA, BBOA, ASOA)

Marine OA

2.4

2.0

1.6

1.2

0.8

H:C

1.20.80.40.0

(e) Monoterpene dominant

TSOA & Aging(g/p)

Add ELVOCAdd Aged (APOA, BBOA, ASOA)

ManitouWhistlerMountain

2.4

2.0

1.6

1.2

0.8

H:C

(a) Riverside TSOAISOA(NOx)

ASOA

Add APOA

(b) Fresno TSOAISOA(NOx)

ASOA

Add APOA, BBOA

(d) downwind TSOA & Aging(g/p)ISOA(NOx)

ASOA & Aging(g/p)BBOA & Aging(g/p)APOA & Aging(g/p)

SPC

(c) Mexico City TSOAISOA(NOx)

ASOABBOAAPOA

Add Aged (APOA, BBOA)

1.61.20.80.40.0

ISOA & Aging(g/p),IEPOXpAdd GSOA & Aging

TSOA & Aging(g/p)ISOA(NOx)


ELVOC

(h) AircraftDC3

1.20.80.40.0

O:C


SGPMelpitz(s)


1.20.80.40.0


AmazonBorneo


Marine OA

2.4

2.0

1.6

1.2

0.8

H:C

1.20.80.40.0


TSOA & Aging(g/p)



2.4

2.0

1.6

1.2

0.8

H:C


ASOA

Add APOA


ASOA

Add APOA, BBOA



SPC


ASOABBOAAPOA


1.61.20.80.40.0




ELVOC

(h) AircraftDC3

1.20.80.40.0

O:C


SGPMelpitz(s)


1.20.80.40.0


AmazonBorneo


Marine OA

2.4

2.0

1.6

1.2

0.8

H:C

1.20.80.40.0


TSOA & Aging(g/p)



2.4

2.0

1.6

1.2

0.8

H:C


ASOA

Add APOA


ASOA

Add APOA, BBOA



SPC


ASOABBOAAPOA


1.61.20.80.40.0




ELVOC

(h) AircraftDC3

1.20.80.40.0

O:C


SGPMelpitz(s)


1.20.80.40.0


AmazonBorneo


Marine OA

2.4

2.0

1.6

1.2

0.8

H:C

1.20.80.40.0


TSOA & Aging(g/p)



2.4

2.0

1.6

1.2

0.8

H:C


ASOA

Add APOA


ASOA

Add APOA, BBOA



SPC


ASOABBOAAPOA


1.61.20.80.40.0




ELVOC

(h) AircraftDC3

1.20.80.40.0

O:C


SGPMelpitz(s)


1.20.80.40.0


AmazonBorneo


Marine OA

2.4

2.0

1.6

1.2

0.8

H:C

1.20.80.40.0


TSOA & Aging(g/p)



2.4

2.0

1.6

1.2

0.8

H:C


ASOA

Add APOA


ASOA

Add APOA, BBOA



SPC


ASOABBOAAPOA


1.61.20.80.40.0




ELVOC

(h) AircraftDC3

1.20.80.40.0

O:C


SGPMelpitz(s)


1.20.80.40.0


AmazonBorneo


Marine OA

2.4

2.0

1.6

1.2

0.8

H:C

1.20.80.40.0


TSOA & Aging(g/p)



2.4

2.0

1.6

1.2

0.8

H:C


ASOA

Add APOA


ASOA

Add APOA, BBOA



SPC


ASOABBOAAPOA


1.61.20.80.40.0




ELVOC

(h) AircraftDC3

Polluted

Isoprene Aircraft

Terpenes

Riverside

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.

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

-1.0 -0.5 0.0 0.5 1.0OSc

(a)

Mexico City

Whistler Peak

Mace Head

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

H:C

1.61.20.80.40

O:C

n nn nn

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(c)




1.61.20.80.40

O:C


(d)





(b)

Riverside

Mexico City (T0)

Fresno

Borneo

DC-3

AmazonSGP

BEACHON

Melpitz

Cool

Davis

SPC


Updated (Very Simple) Aging SchemeStep 3: Account for semi-volatile POA emissionsStep 4: Age gas-phase organics based on flow-tube data, but end point constrained by field obs

End point:O:C=1.1H:C=1.4(defined by field obs)

Emissions FromFossil Fuel

BiofuelBiomass Burning

VOC

HydrophobicO-POAn

Oxidation Products

SOGi

Gas-phase Particle-phase

*,i iCSOAi

HydrophilicI-POAn

Marine Emissions

Biogenic Emissions

×0.5

1.15d

IsopreneMonoterpenesSesquiterpenes

Aromatics

×0.5

OH, O3

NO3

OH, O3

NO3

kage, jSVOCj SVOC-SOA2, j

SOG-SOA1, i

kcarbon, j×85%

×15%

SVOC-SOA1, j

SOG-SOA2, i kage, ikcarbon, i

Marine POA

(GEOS-Chem v9-01-03)

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 Aging OSc Updated Aging

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Bas

e

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Agi

ng

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Agi

ngO

:Cfo

ssil

fuel

of 0

.03

inst

ead

of 0

.1

1.21.00.80.60.40.20.0

Observed O:C

2.2

2.0

1.8

1.6

1.4

1.22.2

2.0

1.8

1.6

1.4

1.22.2

2.0

1.8

1.6

1.4

1.2

2.22.01.81.61.41.2

Observed H:C

0.1

1

10

0.1

1

10

0.1

1

10

0.1 1 10

Observed OA [µg m-3

]

UrbanDownwindRemote/Rural

Urban (JJA)

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Bas

e

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Agi

ng

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Agi

ngO

:Cfo

ssil

fuel

of 0

.03

inst

ead

of 0

.1

1.21.00.80.60.40.20.0

Observed O:C

2.2

2.0

1.8

1.6

1.4

1.22.2

2.0

1.8

1.6

1.4

1.22.2

2.0

1.8

1.6

1.4

1.2

2.22.01.81.61.41.2

Observed H:C

0.1

1

10

0.1

1

10

0.1

1

10

0.1 1 10


]


Urban (JJA)

Comparison With Surface AMS Observations1.2

1.0

0.8

0.6

0.4

0.2

0.0

Bas

e

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Agi

ng

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Agi

ngO

:Cfo

ssil

fuel

of 0

.03

inst

ead

of 0

.1

1.21.00.80.60.40.20.0

Observed O:C

2.2

2.0

1.8

1.6

1.4

1.22.2

2.0

1.8

1.6

1.4

1.22.2

2.0

1.8

1.6

1.4

1.2

2.22.01.81.61.41.2

Observed H:C

0.1

1

10

0.1

1

10

0.1

1

10

0.1 1 10


]


Urban (JJA)

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Bas

e

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Agi

ng

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Agi

ngO

:Cfo

ssil

fuel

of 0

.03

inst

ead

of 0

.1

1.21.00.80.60.40.20.0

Observed O:C

2.2

2.0

1.8

1.6

1.4

1.22.2

2.0

1.8

1.6

1.4

1.22.2

2.0

1.8

1.6

1.4

1.2

2.22.01.81.61.41.2

Observed H:C

0.1

1

10

0.1

1

10

0.1

1

10

0.1 1 10


]


Urban (JJA)

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.

Vertical Comparison From Airborne Campaigns

10

8

6

4

2

0

Altit

ude (

km)

1.21.00.80.60.4O:C

Observation Base Aging Aging w/. SOA heterogeneous aging Aging w/. 5xSOG -> SOA Aging w/. 25 KJ/mol enthalpy Aging w/. 2xEpoa

2.5

2.0

1.5

1.0

0.5

0.0

OA

(µg

sm-3

)

7654321Altitude (km)

(a) IMPEX

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]

10

8

6

4

2

0

Altit

ude (

km)

1.21.00.80.60.4O:C

Observation Base Aging Aging w/. SOA heterogeneous aging Aging w/. 5xSOG -> SOA Aging w/. 25 KJ/mol enthalpy Aging w/. 2xEpoa

1086420OA

(b) DC-3

1.701.601.501.401.30H:C

4.03.02.01.00.0OA

10

8

6

4

2

0

Altit

ude (

km)

1.21.00.80.60.4O:C

(a) IMPEX

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:

Documents

Organic aerosol composition and aging in the atmosphere: How to fit laboratory experiments, field data, and modeling together American Chemical Society