Jana Milford

Department of Mechanical Engineering

University of Colorado, Boulder

Atmospheric Science Progress Review

Meeting

Atmospheric Aerosols from Biogenic Hydrocarbon Oxidation

June 21-22, 2007

AcknowledgmentsAcknowledgmentsTanarit Sakulyanontvittaya,* CUTanarit Sakulyanontvittaya,* CU--Boulder, Mechanical EngineeringBoulder, Mechanical EngineeringDetlev Helmig,* Tiffany Duhl, Ryan Daly, John Ortega, David TannDetlev Helmig,* Tiffany Duhl, Ryan Daly, John Ortega, David Tanner, er, CUCU--Boulder, Institute for Arctic and Alpine ResearchBoulder, Institute for Arctic and Alpine ResearchChristine Wiedinmyer,* Alex Guenther,* Peter Harley,* John OrlanChristine Wiedinmyer,* Alex Guenther,* Peter Harley,* John Orlando, do, Louisa Emmons, Louisa Emmons, SouSou MatsunakaMatsunaka, National Center for Atmospheric , National Center for Atmospheric ResearchResearchBob Bob ArntsArnts, Chris , Chris GeronGeron, Jeff Herrick, J. Harvey, U.S. Environmental , Jeff Herrick, J. Harvey, U.S. Environmental Protection AgencyProtection AgencyMark Mark PotosnakPotosnak, Desert Research Institute, Desert Research InstituteBill Stockwell, Howard UniversityBill Stockwell, Howard UniversityGreg Greg YarwoodYarwood, Environ, EnvironRaoulRaoul ZavariZavari, Pacific Northwest National Laboratory, Pacific Northwest National LaboratoryTed Russell, Helena Park, Georgia TechTed Russell, Helena Park, Georgia TechGail Gail TonnesonTonneson, University of California at Riverside, University of California at RiversideNSF, Atmospheric Chemistry Program (ATM #0304704) and the U.S. NSF, Atmospheric Chemistry Program (ATM #0304704) and the U.S. EPA (#RDEPA (#RD--8310790183107901--0). 0).

* Coauthor* Coauthor

OutlineOutline

BackgroundBackgroundBiogenic hydrocarbon emissions and secondary Biogenic hydrocarbon emissions and secondary organic aerosol (SOA) formation organic aerosol (SOA) formation Sesquiterpenes (SQTs) and Sesquiterpenes (SQTs) and monoterpenesmonoterpenes (MTs)(MTs)

Project objectivesProject objectivesMethodsMethods

Chemical transport modeling Chemical transport modeling MeasurementsMeasurementsEmissions modelingEmissions modeling

ResultsResultsEmissions comparisonsEmissions comparisons

ConclusionsConclusions

Sesquiterpene (SQT) and Monoterpene (MT) Sesquiterpene (SQT) and Monoterpene (MT) Emissions from VegetationEmissions from Vegetation

Significant emissionsSignificant emissionsNorth America total Monoterpene North America total Monoterpene emissions 17.9 Tg C yremissions 17.9 Tg C yr--11

(Guenther et al., 2000)(Guenther et al., 2000)Sesquiterpene emissions? Sesquiterpene emissions?

Highly reactiveHighly reactiveOxidation products can partition to Oxidation products can partition to the aerosol phasethe aerosol phase

MonoterpenesMonoterpenes CC1010 HH1616Sesquiterpenes Sesquiterpenes CC1515 HH2424

α-pinene

Parent terpenoidParent terpenoid Aerosol Yield (%)Aerosol Yield (%)

ΔΔ33--CareneCarene 2.3 2.3 –– 10.910.9

ββ--CaryophylleneCaryophyllene 17.2 17.2 –– 62.562.5

αα--HumuleneHumulene 20.0 20.0 –– 66.766.7

LimoneneLimonene 6.1 6.1 –– 22.822.8

MyrceneMyrcene 7.6 7.6 –– 12.712.7

αα--PinenePinene 2.4 2.4 –– 7.87.8

ββ--PinenePinene 4.2 4.2 –– 13.013.0

SabineneSabinene 4.7 4.7 –– 10.610.6

Griffin et al., 1999

Aerosol yields for biogenic MTs and SQTsAerosol yields for biogenic MTs and SQTs

What are the regional landscape fluxes of MTs and SQTs?What are the regional landscape fluxes of MTs and SQTs?Environmental controlsEnvironmental controlsSpatial and temporal variationsSpatial and temporal variations

What is the contribution of BVOC oxidation to SOA What is the contribution of BVOC oxidation to SOA formation in the eastern U.S.?formation in the eastern U.S.?

Diurnal and seasonal trendsDiurnal and seasonal trendsDifferences in the contributions from MTs and SQTsDifferences in the contributions from MTs and SQTs

How sensitive is secondary aerosol formation from BVOC How sensitive is secondary aerosol formation from BVOC to anticipated changes in:to anticipated changes in:

Process model assumptions?Process model assumptions?Emissions of nitrogen oxides?Emissions of nitrogen oxides?Land cover?Land cover?

Key QuestionsKey Questions

Regional Chemical Transport ModelingRegional Chemical Transport Modeling

MM5/CMAQMM5/CMAQDomain ResolutionDomain Resolution

Horizontal: 36 km x 36 Horizontal: 36 km x 36 kmkmVertical layers: 9 Vertical layers: 9

Chemical MechanismChemical MechanismSAPRC99 with 3rd SAPRC99 with 3rd generation aerosol generation aerosol model and aqueous model and aqueous chemistrychemistry

EpisodesEpisodesJuly 2001July 2001January 2002January 2002

CMAQCMAQ ModificationsModifications

Photooxidation

Advection

Emissions

Deposition

Chemical Reactions

Aerosol Formation

•SAPRC99 mechanism with aqueous chemistry and aerosol module•BCARL, AHUMUL, SQSTwith O3 , NO3 , and OH

•Aerosol Yields•Partitioning Parameters•Aerosol Properties•BCARL, AHUMUL and SQST

•New SQT emissions•MEGAN model

IncorporateSQTs into

otherprocesses

Dry Deposition Wet Deposition

CMAQ InputsCMAQ Inputs

Initial Conditions

Meteorological Data – MM5

Anthropogenic Emissions DataSMOKE 2.0 (U.S.) - NEI 1999

•Last hour output

T. Russell and Sun-Kyoung Park (GA. Tech)

•January 2002•July 2001Area, Point, Mobile, Nonroad, and Point sources

•January 2002•July 2001

T. Russell and Sun-Kyoung Park (GA. Tech)

Boundary Conditions•MOZART2.2 output

SMOKE 2.1 - Mexico (1999), Canada (1996)

•January 2002•July 2001Area, Point, Mobile, Nonroad, and Point sources

Louisa Emmons

Model Evaluation: Focus on Eastern U.S. Model Evaluation: Focus on Eastern U.S. Supersites Supersites

Atlanta, Baltimore, NY, Pittsburgh, St. LouisAtlanta, Baltimore, NY, Pittsburgh, St. Louis

intensive periodsintensive periodsJuly 2001, January 2002 July 2001, January 2002

IMPROVE IMPROVE 24 h avg PM2.5, SO24 h avg PM2.5, SO44

==, NO, NO33--, OC, EC, OC, EC

SEARCH SEARCH urban/rural pairs in AL, FL, GA, MIurban/rural pairs in AL, FL, GA, MICC--14 data at three sites14 data at three sites

TVA CTVA C--14 data (Look Rock, TN)14 data (Look Rock, TN)

Biogenic Emissions Inventory DevelopmentBiogenic Emissions Inventory Development

MEGANMEGAN

0200400600800

100012001400

0 5 10 15 20 25 30 35 40

MM5MM5Meteorological

Data

Emission Data

Emission Model

Guenther, A.

Helmig et al., and Harley, P.

Land Use Land Use

& Cover& Cover

Measurement of SQT and MT emissionsMeasurement of SQT and MT emissions

Bag and cuvette Bag and cuvette enclosure systemsenclosure systemsCalibration systemCalibration system

Helmig et al, 2003Helmig et al, 2003

Cartridge and onCartridge and on--line line samplingsamplingGCGC--MS, GCMS, GC--PTRMS, PTRMS, GCGC--FIDFIDLaboratory and field Laboratory and field measurementsmeasurements

Branch enclosure measurements at Duke Branch enclosure measurements at Duke Forest (summer Forest (summer –– fall 2004)fall 2004)

Loblolly pineLoblolly pineFour FEB Teflon film branch enclosure systems Four FEB Teflon film branch enclosure systems operated simultaneouslyoperated simultaneously

Two towerTwo towerTwo groundTwo ground--levellevel

Double ozone scrubDouble ozone scrub--bingbing in all experimentsin all experiments

Aromatic doping used Aromatic doping used to test recoveriesto test recoveries

Possible 5Possible 5--10% wall 10% wall loss for SQTloss for SQT

isop

ropy

l-ben

zene

6.72

0

250

500

13.6

5

18.5

6

23.4

3

α-pi

nene

7.35

Retention Time (min)

FID

resp

onse

(mV

)

sabi

nene

8.56

9.30

β-m

yrce

ne

1,8-

cine

ole

limon

ene

10.2

410

.34

cis-

linal

oolo

xide

trans

-lina

lool

oxid

elin

alol

tri-is

opro

pyl-b

enze

ne

11.4

911

.92

12.3

6

tetra

-hyd

ro-n

apth

alen

e

nony

l-ben

zene

α-te

rpin

eol

14.6

5

tetra

deca

neβ-

cary

ophy

llene

β-fa

rnes

ene

γ-ca

dine

nege

rmac

rene

-D

20.2

520

.35

21.2

220

.35

22.2

922

.50

δ-ca

dine

ne

α-hu

mul

ene

21.0

5

*

*

* *

O

OH

O

OO

H

HH

OH

OH

Chromatogram (plotted as the flame ionization detector (FID) response) from a ponderosa pine emission sample. Monoterpene retention times 7.2–14.7 min; sesquiterpene retention times 18.9-22.5 min. Shaded peaks are the aromatic compounds from the reference standard.

Helmig et al., ES&T, 41:1545, 2007

1.

β-Caryophyllene

2. α/β- Bergamotene

3. β-Farnescene

4. α-Farnescene

5. α-Humulene

6. α-Muurolene

7. Germacrene

D

8. Δ-Cadinene

9. β-Selinene

10. γ-Cadinene

Helmig et al., in progress Atkinson and Arey, 2003, Griffin et al., 1999

Prominent Sesquiterpenes in Recent Measurements

Beta Caryophyllene Emissions Rate v. TemperatureLoblolly Pine, Duke Forest

ER = 1.16e0.17T

R2 = 0.87

0

200

400

600

800

1000

1200

15 20 25 30 35 40 45Temperature (oC)

Em

issi

on ra

te (n

gC g

-1 h

r-1)

Sesquiterpene (SQT) emission rate (ER) data from an enclosure experiment on a loblolly pine tree at Duke Forest showing total SQT emission rates plotted against the mean needle temperature inside the enclosure. Helmig et al., ES&T, 41:1545, 2007

Adsorbent Cartridge

Field GC

Field GC, Nighttimeexperiment

BVOC Emissions Modeling: MEGANBVOC Emissions Modeling: MEGANModel of Emissions of Gases and Aerosols from Nature: Model of Emissions of Gases and Aerosols from Nature: MEGANMEGAN

1 km resolution1 km resolutionImproved evaluation of LAI and Land Cover inputsImproved evaluation of LAI and Land Cover inputsAvailable through the NCAR Community Data PortalAvailable through the NCAR Community Data Portal

ργγγε ••••= SMageCEEM TPLAICE γγγγ ••=

EM: Emission ( μg m-2 hr-1)ε: Emission Factor ( μg m-2 hr-1)ρ: Loss and Production within plant canopyγCE : Canopy Factorγage : Leaf Age FactorγSM : Soil Moisture FactorγLAI : Leaf Area Index FactorγP : PPFD Emission Activity Factor (light-dependence)γT : Temperature Response Factor

Guenther, 2006

MEGAN v2.0MEGAN v2.0Meteorological Data

TemperatureSolar Radiation

YX

Grid Information

Land Cover Data

4 PFT categories-Needle leaf

-Broad leaf-shrub-Bush

-Grass-HerbFractions of plants in grid cells

Emissions Data

Emission FactorsLAI

Speciation &Mechanism Conversion

1) CBMZ

2) SAPRC993) RACM 4) RADM2

MEGANMEGAN•Linux REDHAT WS•FORTAN PGF90•IOAPI 3.0•NETCDF 3.5.1

γCE

γage

γSM

γLAI

γP

γT

ASCII format

I/O API -

NETCDF

I/O API -

NETCDF

MEGAN v. BEIS3MEGAN v. BEIS3

Additional emission activity algorithmsAdditional emission activity algorithmsSensible heat flux, leaf age, long term effects of Sensible heat flux, leaf age, long term effects of temperature and PARtemperature and PAR

Simplified canopy model to account for leaf Simplified canopy model to account for leaf temperature and canopy light extinctiontemperature and canopy light extinctionUpdated emissions factors Updated emissions factors

Includes speciated SQT and MT emissions from Includes speciated SQT and MT emissions from measurementsmeasurementsEF for individual chemical species vary spatiallyEF for individual chemical species vary spatially

Multiple options for Multiple options for landcoverlandcover inputs including high inputs including high resolution satellite data (MODIS, SPOT)resolution satellite data (MODIS, SPOT)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Gray "D

igger"

Pine

Scots

pine

Dougla

s Fir

Ponde

rosa P

ine

Ponde

rosa P

ine

Wes

tern R

ed C

edar

Redwoo

dDou

glas F

irSho

rtleaf

PineBea

ch P

ineRed

wood

Grand F

ir

Baby B

lue S

pruce

Whit

e Pine

Whit

e Pine

Whit

e Pine

Red P

ineRed

Pine

Balsam

FirRed

Pine

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

white p

ine @

tower(

shad

e)

Loblo

lly pi

ne #

1

Loblo

lly pi

ne #

2A

Loblo

lly pi

ne #

2B

Loblo

lly pi

ne - (

CU grou

nd)

Loblo

lly pi

ne - (

EPA grou

nd)

Loblo

lly pi

ne - (

Tower

1)

Loblo

lly pi

ne - (

Tower

2)Mon

del P

ineMon

del P

ineJu

niper

Junip

erPiny

on P

inePine

eJu

niper

Blue S

pruce

Blue S

pruce

Easter

n Whit

e Pine

Easter

n Whit

e Pine

Easter

n Whit

e Pine

Basal SQT and MT Emissions Rates for Needle Leaf Treesμg

C/g

dw-h

rμg

C/g

dw-h

r

MTP ER

SQT ER

Helmig et al., 2007, Matsunaka, Potosnak et al.

SQT and MT Emissions Rates Summer 2006

0.00

0.50

1.00

1.50

2.00

2.50

Pin Oak

#5Pin

Oak #6

Cottonle

ss C

otton

wood #1

Cottonle

ss C

otton

wood #2

Narrow Le

af Cott

onwood

#1

Narrow Le

af Cott

onwood

#2

Blue Spruc

e #1

Blue Spruc

e #2

Easter

n Whit

e Pine

#1

Easter

n Whit

e Pine

#2

Easter

n Whit

e Pine

#3

Octber

Glory R

ed M

aple

#1

Octber

Glory R

ed M

aple

#2Silve

r map

le

Sunflo

wer #1

Sunflo

wer #2

Sunflo

wer #3

Sunflo

wer #4

Corn #1

Corn #2

Corn#3

Corn #4

Bean P

lant #

1

Bean P

lant #

2Whea

t #1

Wheat #

2Tob

acco #

1Tob

acco #

2μ

g C

g(d

w)-1

h-1

MT

SQT

Helmig and Daly, 2007

Creekside NurseryJune – Aug. 2006

NCAR GreenhouseAug. 2006

Participants Affiliation Sample Collection Analysis

Detlev Helmig CU/INSTAAR On-line On-line Tenax

adsorbent

GC-MSGC-FIDGC-FID

Peter Harley NCAR Tenax

adsorbent GC-FID

Alex Guenther NCAR On-line GC-MS

Thomas Karl NCAR On-line PTR-MS

Jim Greenberg NCAR On-line O3 Reactivity

Sou

Matsunaga NCAR Super Q GC-FID

Tiffany Duhl NCAR Super Q GC-FID

Monica Madronich NCAR Super Q GC-FID

Nicole Bouvier-Brown UC Berkeley SPME Fibers GC-MS

Rei

Rasmussen Oregon Health & Science Univ.

Tenax

adsorbentCanisters

GC-MSGC-MS; GC-FID

Chris GeronBob Arnts

USEPA Tenax

adsorbent GC-MS

Hannele

Hakola Finnish Meteor.Institute

Tenax

adsorbent GC-FID

NCAR SQT Measurement Comparison, April 30 - May 4, 2007

Courtesy of P. Harley

Emission Factors for 4 PFTs

0.050.0

100.0150.0200.0250.0300.0350.0400.0450.0500.0

MEGAN2.0-06b SQT MEGAN2.0-L SQT

ug/m2-hr

BroadleafNeedle LeafShrub-BushGrass-Crop

Comparison of SQT Emission Factors from RecentMeasurements v. Prior Literature

Recent data Literature through 2004

Emission Factors for 4 PFTs

0.0100.0200.0300.0400.0500.0600.0700.0800.0900.0

1000.0

MEGAN2.0-06b SQT MEGAN2.0-L SQT MEGAN2.0 MTP

ug/m2-hr

BroadleafNeedle LeafShrub-BushGrass-Crop

SQT and MT Emission Factors by Plant Functional Type

Recent data Literature through 2004 Recent data

Max 465.8 g/km2-hr Max 154.4 g/km2-hr Max 154.4 g/km2-hr

Emissions Modeling Results -- JanuaryBEIS 3.0 MEGAN2.0-06b MEGAN2.0-L

Emissions Modeling Results – JulyBEIS 3.0 MEGAN2.0-06b MEGAN2.0-L

SOA Precursor Speciation for SAPRC99-S, July Average Emissions - CMAQ Domain

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

BEIS3.0 M EGAN2.0-06b M EGAN2.0-L

TRP1

SSQT

AHUM UL

BCARL

CRES

ARO2

ARO1

OLE2

ALK5

Emissions Modeling Results

ConclusionsConclusionsSQT emissions are highly variableSQT emissions are highly variable

Emissions likely dependent on leaf age and other environmental Emissions likely dependent on leaf age and other environmental variablesvariablesSeasonal dependence is uncertain but maybe importantSeasonal dependence is uncertain but maybe important

Measured SQTs appear to have stronger temperature dependency Measured SQTs appear to have stronger temperature dependency than MT emissionsthan MT emissionsLightLight--dependency observed in some dependency observed in some MTsMTs and and SQTsSQTsSome crops appear to be strong SQT emitters Some crops appear to be strong SQT emitters –– need more need more measurementsmeasurementsMEGAN provides an easily adaptable framework for BVOC emissions MEGAN provides an easily adaptable framework for BVOC emissions estimationestimationSpeciation schemes available for most popular chemical mechanismSpeciation schemes available for most popular chemical mechanismssSQT estimated to contribute 7 SQT estimated to contribute 7 –– 16% of SOA precursor emissions 16% of SOA precursor emissions (anthropogenic and biogenic, excluding isoprene) for continental(anthropogenic and biogenic, excluding isoprene) for continental U.S. U.S. in Julyin JulySQT estimated to contribute 1 SQT estimated to contribute 1 –– 2% of SOA precursor emissions in 2% of SOA precursor emissions in JanuaryJanuarySOA contributions to be determined!SOA contributions to be determined!

DisclaimerDisclaimer

Although the research described in this presentation has Although the research described in this presentation has been funded in part by the United States Environmental been funded in part by the United States Environmental Protection Agency, it has not been subjected to the Protection Agency, it has not been subjected to the AgencyAgency’’s required peer and policy review and therefore s required peer and policy review and therefore does not necessarily reflect the views of the Agency and does not necessarily reflect the views of the Agency and no official endorsement should be inferred.no official endorsement should be inferred.