sources of organic aerosol: semivolatile emissions and ... · sources of organic aerosol:...

Sources of Organic Aerosol: Semivolatile Emissions and Photochemical Aging

Allen Robinson and Peter Adams

Center for Atmospheric Particle StudiesCarnegie Mellon University

Presented at Sources and Atmospheric Formation of Organic Particulate Matter Progress Review Meeting, EPA, RTP, NC, Sept 21, 2010.

2

A knowledge gap. . .

?HOA

OOA

Average urban OAZhang et al, GRL, 2007

3

Elemental Carbon

Resovled Particle-Phase

Particle-Phase UCM

Resolved IVOCs & SVOCs

IVOCs & SVOCs UCM

gas-phase volatile organics

87 % of particle-phase & SVOC/IVOC emissions are UCM

Diesel exhaust

UCM

UCM

Complexity of Combustion Emissions

Schauer et al., ES&T 2002

Presenter

Presentation Notes

Combustion sources such as power plants, both gasoline and diesel powered engines are a complex mixture of compounds that range in physical and chemical composition. If WE look at a speciated profile for diesel exhaust, or more specifically in the particle-phase and IVOC/SVOC range - there is a small fraction of emissions that are speciated. A large fraction of the emissions (about 87%) reported as an unresolved complex mixture. It would be very difficult to fully speciate OA coming out of a combustion source. Furthermore, modeling SOA formation from these emissions is difficult because we don’t know what is coming out of the source, and even if we did, we wouldn’t have SOA yield data for most of these compounds. The next question to think about then is – how can we represent combustion emissions on a simpler level, particularly in the IVOC/SVOC range?

4

Old conceptual model

C* (g m-3)10-2 100 102 104 106 108 1010

S1 S2Volatile

Products

SOAPrecursor

PrimaryEmissions

POA

1

2

Primary Organic Aerosol

Non-volatile

Non-reactive

Secondary Organic Aerosol

Very volatile precursors

Semivolatile products

• Monoterpenes• Light aromatics

• Diesel exhaust• Wood smoke

5

Revised conceptual model

C* (g m-3)

10-2 100 102 104 106 108 1010

S1 S2Volatile

Products

SOAPrecursor

P2

PrimaryEmissions

P1 P3 P4 P5

f1 f3f2 f5f4

Semivolatile

Reactive

1 2

(Robinson et al. Science 2007)

0

2

4

6

8 Traditional SOA IVOC SOA SVOC SOA POA

10-2 100 102 104 106 108 1010

Cum

mul

ativ

e O

A (T

g yr

-1)

C* (g m-3 @ 298 K)

0

5

10

15

20

Emis

sion

s (T

g yr

-1)

Biogenic Anthropogenic

0.25

0.5

0.75

1.0

0

Esti

mat

ed Y

ield

( Donahue et al. AE 2009)

Traditional SOA

Non-Traditional SOA

7

Objectives of Research

What is the volatility distribution of fresh emissions?

What is the effect of aging on primary emissions?

What are the implications for urban and regional organic aerosol concentrations?

Change T or COA

Measure changes in partitioning

Fit data to derive volatility distribution

Lipsky et al., ES&T, 2006An et al., J. Aerosol Sci., 2007

Dilution SamplerThermodenuder

Objective 1. Quantifying volatility distributions

Presenter

Presentation Notes

Current methods to measure partitioning include the use of thermodenuders and dilution samplers (to change either the temperature or organic concentration of the emissions in order to measure changes in the partitioning) . Thermodenuders raise the temperature of emissions while concentrations of organics are diluted in dilution samplers by mixing with clean air. Both methods use measured partitioning data and fit absorptive partitioning theory to obtain volatility distributions.

Combining TD and Dilution Data

9

C10001

f

C1001

f

C101

fX

OA

4

OA

2

OA

1p

Wood Smoke Volatility distribution

10 100 1000 100000.0

0.2

0.4

0.6

0.8

Emis

sion

Fac

tor

(g/k

g-fu

el)

C* (g/m3)

Grieshop et al. EST 2010

Partitioning and POA Emission Factors

10Robinson et al. JAWMA 2010

11

Objective 2. Effects of agingExperiments with increasingly complex mixtures

Diluted exhaust Emission Surrogates

• motor oil (new & used)• diesel fuel • mixtures of fuel & oil

Single components

• n-alkanes • branched alkanes• aromatics • simple mixtures

• diesel exhaust• wood smoke• aircraft exhaust

Presenter

Presentation Notes

In thinking about how we represent organic aerosol in the lab, the first broad category of precursors are single classes of organic compounds. For example, alkanes, aromatics, and simple mixtures of these compounds. Stepping up in complexity, we can use organic material that might represent the compounds in real combustion emissions - or emission surrogates. These emission surrogates contain a complex mixture of these single component compounds. Stepping up again in complexity, we can use actual diluted exhaust in our experiment, particularly with diesel exhaust and wood smoke.

12

Aging of Wood Smoke Experimental Setup

13

Wood smoke Aging Rapidly Creates Lots of “SOA”

o

50

40

30

20

10

0

CO

A (µ

g m

-3) Lights off

Measured OA

POA MassModeled Mass

f i 60 0

6543210

Elapsed since lights on (hours)

120

80

40

0Modeled MassPOA Mass

SOA via BC Scaling

Wal

l-los

sC

orre

cted

(g/

m3 )

(Grieshop et al. ACP 2009)

14

Data from multiple wood smoke aging experiments

2.5

2.0

1.5

1.0

6543210

Expt. 5

Expt. 3

Expt. 2

Expt. 4 - High NOx

Expt. 6

Expt. 1

6 Yellow Pine, smoldering/dying flame 50 2.18E+07 196

Laurel Oak Expts.Yellow Pine Expts.

Elapsed Time (hr)

Org

anic

Aer

osol

Enh

ance

men

t


15

Similarity of Aged Organic MS

MILAGRO OOA1 MILAGRO OOA2 MILAGRO BBOA

Residual 4 hour

example scatter plots

16

12

8

4

0

3020100 1612840 12840

1.00.90.80.70.60.50.4

543210-1Elapsed since lights on (hours)

1.00.90.80.70.60.50.4

Ambient Factors

Lab Diesel Spectra

intercept=0.37slope=0.63r2=0.82



r2_rel_org_milagro_ooa1 r2_rel_org_milagro_ooa2 r2_rel_org_milagro_bboa r2_nov19_pghOOA

r2_relorg_31janPOA_dies r2_rel_org31jan5hr_dies r2_rel_org31jan5hr_dresid

Diesel POA

Aged Diesel

BBOA

OOA1OOA2

R2

R2

Elapsed Time since UV (hrs)

Wood smoke versus lab diesel

Am

bien

t Fa

ctor

s co

urt

esy

of J

. Jim

enez

CU

Bou

lder

Wood smoke versus ambient


n-alkane high NOx SOA yields

16(Presto et al. EST 2010)

C-12 C-15

C-17

Yields are “high”

17

Parameterizing yields based on C*

18(Presto et al. EST 2010)

SOA from IVOCs can be highly oxygenated

19

OOA-like

AM

S f 4

4 Less oxidized

(Presto et al. EST 2009 & 2010)

Composition C15 SOA

20

Objective 3. Regional and global modeling to quantify organic aerosol

Modifications

Update inventory

Added species

Aging

Critical inputs

Volatility distribution

Aging mechanism

IVOC emissions

Activity coefficients

Presenter

Presentation Notes

Example application for July 2001 intensive

21

Aging mechanism

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.00

5

10

15

20

25

SVOCChamber

Data

Aer

osol

Mas

s (u

g m

-3)

Time (hrs)

SVOC + 1.5 IVOC

10-2 10-1 100 101 102 103 1040.0

0.2

0.4 Particle Gas

Nor

mal

ized

EF

C* (g m-3)C* @ 298K (ug/m3)Con

cent

ratio

n (u

g/m

3)

Primary

Time (hrs)

Aer

osol

Mas

s (u

g/m

3 )

DataScheme #2

Scheme #1

Aging Scheme

• Gas-phase Reactions w/ OH

• kOH = 4 x 10-11 cm3 (molec s)-1

Updating Emission Inventories

RedistributingNon-Volatile POA

Revised Inventory

0.0

0.1

0.2

0.3

2

4

6

8

Emis

sion

s (T

g yr

-1) Redistributed POA

Existing VOC

C* (g m-3)10-2 100 102 104 106 108 1010

0

200

400

1000

C* (ug/m3)

Emis

ssio

ns(k

ton/

yr)

0 10-2 100 101 105

0.0

0.1

0.2

0.3

2

4

6

8

Emis

sion

s (T

g yr

-1) POA

VOC

C* (g m-3)0 10-2 100 102 104 106 108 1010

NEI 2002

23

Are there missing emissions?

0100200300400

1000200030004000500060007000

Mas

s (K

tons

/Yr)

C* @ 298K (ug/m3)101110-210-11001011021031041051061071081091010

VOCSVOC

?

MissingEmissions

0

1

2

3

Diesel

Catalyt

ic Gas

Non-Cat

Gas

Wood smoke

Emissions Data

SVO

C V

apor

sPO

A

(Schauer et al. EST 99, 00, 02)

Summertime OA Predictions

TraditionalNon-volatile POA Semivolatile POA

RevisedSemivolatile + Aging

0 1 2 3 4

OAg m-3

July 2001

Shrivastava et al. JGR 2008

Ratio of Revised-to-Traditional Predicted OA levels

July 2001 OA

0 2 4 6 8 10 120

2

4

6

8

10

12

0 2 4 6 8 10 120

2

4

6

8

10

12

(a) Nonvolatile STN IMPROVE

(b) Revised

Observed

Pred

icte

d

Traditional

Revised

1.4

1.2

1

0.8

0.6


Wintertime OA Predictions

OAg m-3

TraditionalNon-volatile POA Semivolatile POA

RevisedSemivolatile + Aging

0 1 2 3 4

January 2002


January 2002

0 2 4 6 8 10 120

2

4

6

8

10

12

0 2 4 6 8 10 120

2

4

6

8

10

12

(c) Nonvolatile

(d) Revised

Observed

Pred

icte

d

Traditional

Revised

1.2

1.0

0.8

0.6

Ratio of Revised-to-Traditional Predicted OA levels


Revised model predicts a more regional aerosol

Upwind Rural Site

Urban Site

Model-Measurement Comparison


Oxygenated Organics Dominate in Summer

Traditional ModelNon-Volatile POA

Revised modelSemivolatile + Aging

1

0.8

0.6

0.4

0.2

0

July 2001

Traditional SOATotal OA

Traditional SOA + Aged PrimaryTotal OA

Revised Model and AMS OOA

AMS data Zhang et al. GRL 2007Urban Rural

0.0

0.2

0.4

0.6

0.8

1.0 F

ract

iona

l con

trib

utio

nto

tota

l OA

Aged Primary Traditional SOA

AMS OOA

Pinnacle

state

parkDuke Fores

t

New York

Houston

Pittsburg

h

Conclusions

Primary emissions are semivolatile

Aging of emissions create substantial SOA

Accounting for aging and partitioning improves predictions of CTMs

Important implications for developing control strategies

31

Journal Papers

A.L. Robinson, A.P. Grieshop, N.M. Donahue, S.W. Hunt “Updating our conceptual model for fine particle mass emissions from combustion systems,” Journal of the Air and Waste Management Association, in press.

A.A. Presto, M.A. Miracolo, N.M. Donahue, A.L. Robinson “Secondary Organic Aerosol Formation from High- NOx Photo-Oxidation of Low-Volatility Precursors: n-Alkanes” Environmental Science & Technology, 44 (6), 2029–2034, 2010.

M.A. Miracolo, A.A. Presto, A.T. Lambe, C.J. Hennigan, N.M. Donahue, J.H. Kroll, D.R. Worsnop, A.L. Robinson “Photo-Oxidation of Low-Volatility Organics found in Motor Vehicle Emissions: Production and Chemical Evolution of Organic Aerosol Mass,” Environmental Science & Technology, 44(6), 2029–2034, 2010.

J.L. Jimenez, M.R. Canagaratna, N.M. Donahue, A.S.H. Prevot, Q. Zhang, J.H. Kroll, P.F. DeCarlo, J.D. Allan, H. Coe, N.L. Ng, A.C. Aiken, K.D. Docherty, I.M. Ulbrich, A.P. Grieshop, A.L. Robinson, J. Duplissy, J. D. Smith, K.R. Wilson, V.A. Lanz, C. Hueglin, Y.L. Sun, A. Laaksonen, T. Raatikainen, J. Rautiainen, P. Vaattovaara, M. Ehn, M. Kulmala, J.M. Tomlinson, D.R. Collins, M.J. Cubison, E.J. Dunlea, J.A. Huffman, T.B. Onasch, M.R. Alfarra, P.I. Williams, K. Bower, Y. Kondo, J. Schneider, F. Drewnick, S. Borrmann, S. Weimer, K. Demerjian, D. Salcedo, L. Cottrell, R. Griffin, A. Takami, T. Miyoshi, S. Hatakeyama, A. Shimono, J.Y Sun, Y.M. Zhang, K. Dzepina, J.R. Kimmel, D. Sueper, J.T. Jayne, S.C. Herndon, A.M. Trimborn, L.R. Williams, E.C. Wood, C.E. Kolb, U. Baltensperger, and D.R. Worsnop, “Evolution of Organic Aerosols in the Atmosphere,” Science, 326, 1525-1529, 2009.

32

Journal Papers

A. Asa-Awuku, M. A. Miracolo, J. H. Kroll, A. L. Robinson, N. M. Donahue “Mixing and phase partitioning of primary and secondary organic aerosols,” Geophysical Research Letters, 36, L15827, doi:10.1029/2009GL039301, 2009.

A.P. Grieshop, M.A. Miracolo, N.M. Donahue, and A.L. Robinson, “Constraining the Volatility Distribution and Gas-Particle Partitioning of Combustion Aerosols Using Isothermal Dilution and Thermodenuder Measurements,” Environmental Science & Technology, 43(13), 4750-4756.

A.A. Presto, M.A. Miracolo, J.H. Kroll, D.R. Worsnop, A.L. Robinson, and N.M. Donahue, “Intermediate- Volatility Organic Compounds: A Potential Source of Ambient Oxidized Organic Aerosol,” Environmental Science & Technology, 43(13), 4744–4749.

A.P. Grieshop, N.M. Donahue, and A.L. Robinson, “Laboratory Investigation of Photochemical Oxidation of Organic Aerosol from Wood Fires 2: Analysis of Aerosol Mass Spectrometer Data,” Atmospheric Chemistry and Physics, 9, 2227-2240, 2009.

A.P. Grieshop, J.M. Logue, N.M. Donahue, and A.L. Robinson, “Laboratory Investigation of Photochemical Oxidation of Organic Aerosol from Wood Fires 1: Measurement and Simulation of Organic Aerosol Evolution,” Atmospheric Chemistry and Physics, 9, 1263-1277, 2009.

N.M. Donahue, A.L. Robinson, and S.N. Pandis, “Atmospheric Organic Particulate Matter: From Smoke to Secondary Organic Aerosol,” Atmospheric Environment, 43(1) 97–109, 2009.

M.K. Shrivastava, T.E. Lane, N.M. Donahue, S.N. Pandis, and A.L. Robinson, “Effects of Gas-Particle Partitioning and Aging of Primary Emissions on Urban and Regional Organic Aerosol Concentrations,” Journal of Geophysical Research, 113, D18301, doi:10.1029/2007JD009735, 2008.

33

34

Acknowledgements

Neil Donahue, Spyros Pandis

Marissa Miracolo, Andy Grieshop, Jennifer Logue, David Lee, Kristina Wagstrom, Manish Shrivastava, David Lee, Jeff Pierce, Chris Hennigan, Ngoc Nguyen, Emily Weitkamp, Amy Sage, Tim Lane, Eric Lipsky

Funding: US EPA STAR

Center for AtmosphericParticle Studies

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