Georgia Institute of Technology PM Modeling and Source Apportionment Amit Marmur, Dan Cohan, Helena Park, Jeameen Baek, Sangil Lee, Mei Zhang, Jim Boylan,

Georgia Institute of Technology

PM Modeling and Source Apportionment

Amit Marmur, Dan Cohan, Helena Park, Jeameen Baek, Sangil Lee, Mei Zhang, Jim

Boylan, Katie Wade,Jim Mulholland, …, and

Armistead (Ted) RussellGeorgia Institute of Technology


With Special Thanks to:• Eric Edgerton, Ben Hartsell and John Jansen

– for making the required observations possible as part of SEARCH

• Southeastern Aerosol Research and Characterization study– Discussions and additional analyses

• Mike Kleeman– Additional source apportionment calculations (see also,

1PE11)

• Phil Hopke• Paige Tolbert and the Emory crew

– As part of ARIES, SOPHIA, and follow on studies

• NIEHS, US EPA, FHWA, Southern Company, SAMI– Financial assistance

• And more…


Genesis

• (How) Can we use “air quality models” to help identify associations between PM sources and health impacts?– Species vs. sources

• E.g., Laden et al., 2000


Epidemiology• Identify associations between air quality

metrics and health endpoints:

Sulfate

0

2

4

6

8

10

m g / m

3

SDK

FTM

TUC

JST

YG

Sulfate

Health endpoints

StatisticalAnalysis

(e.g. time series)

Association


Association between CVD Visits and Air Quality

(See Tolbert et al., 9C2)


Issues• May not be measuring the species primarily impacting

health– Observations limited to subset of compounds present

• Many species are correlated– Inhibits correctly isolating impacts of a species/primary actors

• Inhibits identifying the important source(s)

• Observations have errors– Traditional: Measurement is not perfect– Representativeness (is this an error? Yes, in an epi-sense)

• Observations are sparse– Limited spatially and temporally

• Multiple pollutants may combine to impact health– Statistical models can have trouble identifying such phenomena

• Ultimately want how a source impacts health– We control sources


Use AQ Models to Address Issues: Link Sources to Impacts

Data

Air Quality Model

SourceImpactsS(x,t)

Health Endpoints

StatisticalAnalysis

Association between Source Impact

and Health Endpoints


Use AQ Models to Address Issues: Assess Errors, Provide Increased Coverage

DataAir Quality

ModelAir Quality

C(x,t)

Health Endpoints

Association between Concentrations

and Health Endpoints

MonitoredAir Quality

Ci(x,t)

SiteRepresentative?


But!• Model errors are largely unknown

– Can assess performance (?), but that is but part of the concern• Perfect performance not expected

– Spatial variability– Errors– …

• Trading one set of problems for another?– Are the results any more useful?


PM Modeling and Source Apportionment*

• What types of models are out there?• How well do these models work?

– Reproducing species concentrations– Quantifying source impacts

• For what can we use them?• What are the issues to address?• How can we reconcile results?

– Between simulations and observations– Between models

*On slide 10, the talk starts…


PM (Source Apportionment) Models

(those capable of providing some type of information as to how specific sources impact air

quality)PM Models

Emissions-Based

Receptor

Lag. Eulerian (grid)CMB FA

PMF

UNMIXMolec. Mark. Norm.

“Mixed PM”SourceSpecific*

Hybrid

*Kleeman et al. See 1E1.


Source-based Models

Emissions

Chemistry

Air Quality Model

Meteorology


Source-based Models

• Strengths– Direct link between sources and air

quality– Provides spatial, temporal and chemical

coverage

• Weaknesses– Result accuracy limited by input data

accuracy (meteorology, emissions…)– Resource intensive


Receptor Models

n

jjjii SfC

1,

ObsservedAir Quality

Ci(t)

Source Impacts

Sj(t)

Ci - ambient concentration of specie i (mg/m3)

fi,j - fraction of specie i in emissions from source j

Sj - contribution (source-strength) of source j (mg/m3)


Receptor Models• Strengths

– Results tied to observed air quality• Reproduce observations reasonably well, but…

– Less resource intensive (provided data is available)• Weaknesses

– Data dependent (accuracy, availability, quantity, etc.)• Monitor• Source characteristics

– Not apparent how to calculate uncertainties– Do not add “coverage” directly


Hybrid: Inverse Model Approach*

Emissions (Eij(x,t)) Ci(x,t), Fij(x,t),

& Sj(x,t)Air Quality

Model +DDM-3D

Receptor Model Observations takenfrom routine measurement

networks or specialfield studies

New emissions:Eij(x,t)

Other Inputs

INPUTS

Main assumption in the formulation:

A major source for the discrepancy between predictions and observations are the emission estimates

*Other, probably better, hybrid approaches exist


Source Apportionment Application

• So, we have these tools… how well do they work?

• Approach– Apply to similar data sets

• Compare results• Try to understand differences

– Primary data set:• SEARCH1 + ASACA2

– Southeast… Atlanta focus– Daily, speciated, PM2.5 since 1999

1. Edgerton et al., 4C1; 2. Butler et al., 2001


SEARCH & ASACA

Oak Grove (OAK)

Centreville (CTR)

Pensacola (PNS)

Yorkville (YRK)

Jefferson Street (JST)

North Birmingham (BHM)

Gulfport (GFP)

Outlying Landing Field #8 (OLF)

rural urban suburban

ASACA

Funding from EPRI, Southern Company


Questions

• How consistent are the source apportionment results from various models?

• How well do the emissions-based models perform?

• How representative is a site?• What are the issues related to applying

source apportionment models in health assessment research?

• How can we reconcile results?

*On slide 10, the talk starts…


Source Apportionment Results• Hopke and co-workers (Kim et al., 2003; 2004) for

Jefferson Street SEARCH site (see, also 1PE4…)

Source PMF 2 PMF8 ME2 CMB-MM*

Sec. Sulf. 56 62 56 28

Diesel 15 11 19

Gasol. 5 15 3

Soil/dust 1 3 2 2

Wood Smoke 11 6 3 10

Nitr.-rich 7 8 9 5

Average Source Contribution

}22

Notes: •CMB-MM from Zheng et al., 2002 for different periods, given for comparison•Averaged results do not reflect day-to-day variations


Daily Variation

PMF: See Liu et al., 5PC7

LGO-CMB: see Marmur et al.,

6C1


Receptor Models

• Approaches do not give “same” source apportionment results… yet– Relative daily contributions vary

• Important for associations with health studies– Introduces additional uncertainty

– Long term averages more similar• More robust for attainment planning

• Using receptor-model results directly in epidemiological analysis has problem(s)– Results often driven by one species (e.g., EC for

DPM), so might as well use EC, and not introduce additional uncertainty

– No good way to quantify uncertainty


Emissions-based Model (EBM)Source Apportionment

• Southeast: Models 3– DDM-3D sensitivity/source apportionment tool– Modeled 3 years

• Application to health studies– Provides additional chemical, spatial and temporal

information– Allows receptor model testing

• Concentrate on July 01/Jan 02 ESP periods– Compare CMAQ with molecular marker CMB

• California: CIT (Kleeman)• But first… model performance comments

– CAMX-PM (Pandis), URM (SAMI), CMAQ (VISTAS)


PM 2.5 in JST 28.07(CMAQ, Jan, 2002)

11%

29%

13%6%

10%

31%

PM 2.5 in JST 29.42(CMAQ, Jul, 2001)

39%

5%14%

5%

10%

27%

PM 2.5 in JST 13.28(OBS, Jan, 2002)

17%

12%

10%

11%

34%

16%

PM 2.5 in JST 22.53(OBS, Jul, 2001)

36%

2%

14%5%

15%

28%

Species of PM 2.5 in JST

Jan

uary

2002 J

uly

2001

MODEL(CMAQ) OBS

29.42 (mg/m3) 22.53 (mg/m3)

28.07 (mg/m3) 13.28 (mg/m3)Sulf ate

Nitrate

Ammonium

Elemental Carbon

Organic carbon

Other mass

Winter problem largely nitrate + ammonium


SAMI: URM

Fine Mass at Great Smoky MountainsModel (L) vs. Observations (R)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

Co

nc

en

tra

tio

n (m

g/m

3)

SO4 NO3 NH4 ORG EC SOIL

02/09/94 03/24/93 04/26/95 08/04/93 08/07/93 08/11/93 07/12/95 07/31/91 07/15/95

Class 5Class 4Class 3Class 2Class 1


PerformanceSulfate

FAQS*

VISTAS

0.0

2.0

4.0

6.0

8.0

10.0

0.0 2.0 4.0 6.0 8.0 10.0

JST OC (ug/m3)

CM

AQ

36 O

C (

ug

/m3)

EPI OC

*Fall Line Air Quality Study, Epi: 3-year modeling, VISTAS: UCR/ENVIRON

Simulated a bit low:Analyses suggests

SOA low


Combined Modeling Studies

0.00

50.00

100.00

150.00

200.00

0.0 4.0 8.0 12.0 16.0 20.0 24.0 28.0

Average Concentration (mg/m3)

Me

an

Fra

cti

on

al

Err

or

Sulfate

Nitrate

Ammonium

Ammonium Bi

Organics

EC

Soils

PM2.5

PM10

CM

Goal

Criteria

Mean Fractional Error: Combined Studies

Plot by J. Boylan


All Four Networks: 12 Months (2002)

0

50

100

150

200

0.0 4.0 8.0 12.0 16.0 20.0


Me

an

Fra

cti

on

al

Err

or

Sulfate

Nitrate

Ammonium

Organics

EC

Soils

PM2.5

PM10

CM

Goal

Criteria

VISTAS PM Modeling Performance

Modeling conducted by ENVIRON, UC-Riverside. Plot by J. Boylan


Species of PM 2.5 in January 2002

0

10

20

30

BHM CTR GFP J ST OAK OLF PNS YRK

(mg

/m3 )

Species of PM 2.5 in July 2001

0

10

20

30


(mg

/m3 )

0

5

10

15

20

25

30

BHM CTR GFP JST OAK OLF PNS YRK

Sulfate Nitrate Ammonium Elemental Carbon Organic carbon Other mass

Jan

uary

2002 Ju

ly 2

001

Species of PM 2.5(OBS:Left column, MODEL(CMAQ): right column)

OBS

MODEL (CMAQ)

Too much simulated nitrate and soil dust in winter


Performance• PM Performance (Seignuer et

al., 2003; see also 6C2)– Errors from recent studies

using CMAQ, REMSAD• Organic carbon: 50-140%

error• Nitrate: 50-2000% error

– Understand the reason for much of the error in nitrate

• Deposition, heterogeneous reaction

• Ammonia emissions still rather uncertain

– OC more difficult• Understand part

– Heteorgenous paths not included

• More complex mixture• Primary/precursor emissions

more uncertain

Nitrate


Predicted vs. Estimated in Organic Aerosol in Pittsburgh

(Pandis and co-workers) Primary and Secondary OA

0

3

6

9

12

15

0 24 48 72 96 120 144 168

Simulation Hours

0

3

6

9

12

15

0 24 48 72 96 120 144 168

Secondary

Primary

7/12 7/13 7/14 7/15 7/16 7/17 7/18P

redi

cted

[mg

/m3 ]

Est

imat

ed [mg

/m3 ]

• EC Tracer Method (Cabada et al., 2003) See also 4D4, 5D2…


Limitations on Model Performance

• The are (should be) real limits on model performance expectations– Spatial variability in concentrations – Spatial, temporal and compositional “diffusion” of

emissions – Met model removal of fine scale (temporal and

spatial) fluctuations (Rao and co-workers) – Stochastic, poorly captured, events (wildfires, traffic

jams, upsets, etc.) – Uncertainty in process descriptions and other inputs

• Heterogeneous formation routes


Spatial Variability

• Spatial correlation vs. temporal correlation (Wade et al., 2004)– Power to distinguish health

associations in temporal health studies

– Sulfate uniform, EC loses correlation rapidly

• Data withholding using ASACA data:– Interpolate from three other

stations, compare to obs.– EC: Norm. Error=0.6

• TC: 0.2!– Sulfate: NE = 0.12

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100distance (km)

sp

ati

al

sd

/ t

em

po

ral

sd

24-hr EC

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100distance (km)

sp

ati

al

sd

/ t

em

po

ral

sd

24-hr SO42-

EC

Sulfate


Emissions “Diffusion”

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00

Time (hr)

Fra

ctio

n

SMOKE

Hartsfield

Dial Variation of ATL emissions

Default profile (black) vs. plane/engine dependent operations (red)

Chemical dilution: assume source X has same emissions composition, independent of location, etc. (~)

On-road OC Emissions

Nonroad OC Emissions


Wildfire and Prescribed burn

PM2.5 Emissions (tons/month)

0.21115.54 6.7655.763

12.58

55.815.763

3.937

0.0467.623

6.107

65.115

33.40815.687

32.363

47.477

50.99551.681

65.10348.337

14.544

68.263

135.654

Legend

GA

aug00.TOTAL_PM25

0.000 - 0.039

0.040 - 0.390

0.391 - 1.928

1.929 - 3.856

3.857 - 71.533

3.4

19 19

Black: estimates based on fire recordsRed: estimates based on satellite images (Ito and Penner, 2004)

32

56

51

Capturing stochastic events using satellites:



0.00

50.00

100.00

150.00

200.00

0.0 2.0 4.0 6.0 8.0 10.0 12.0


Me

an

Fra

cti

on

al

Err

or

Sulfate

Goal

Criteria

Sulfate Mean Fractional Error

X Spatial variability limit?



0.00

50.00

100.00

150.00

200.00

0.0 1.0 2.0 3.0 4.0


Me

an

Fra

cti

on

al

Err

or

EC

Goal

Criteria

EC Mean Fractional Error

X


How Good Are They?

• All evidence suggests that they describe the processes most affecting the evolution of ozone and (if equipped) particulate matter (o.k., many components of PM) after pollutant emission

Science (chemistry/physics)

Mathematics

Computational implementation

Evaluation

Application

Now getting sufficient data


How Good Are They?

• All evidence suggests that they describe the processes most affecting the evolution of ozone and (if equipped) particulate matter (o.k., many components of PM) after pollutant emission

Science (chemistry/physics)

Mathematics

Computational implementation

Evaluation

Application

Now getting sufficient data:Holes will get filled


Emissions-based Model Performance

• Some species well captured– Sulfate, ammonium, EC(?)

• “Routine” modeling has performance issues– Multiple causes

• Species dependent– OC tends to be a little low

• Heterogeneous formation? (or emissions or meteorology)• Some “research-detail” modeling appears to capture observed

levels relatively well– Finer temporal variation captured as well

• Real limits on performance– Data with-holding and statistical analysis suggests model performance

may be limited due to spatial variability (5PC5)• Longer term averages look reasonable for most species

– Nitrate high• This is not an evaluation of source-apportionment accuracy

– But it is an indication of how well one might do


PM 2.5 in JST(CMAQ, Jan, 2002)

11%

29%

13%5%

2%

9%

10%

8%

13%

PM 2.5 in JST(CMAQ, Jul, 2001)

38%

5%14%5%

2%

7%

8%

11%

10%

PM 2.5 in JST(CMB, Jan, 2002)

17%

12%

10%

14%5%

0%

23%

11%

8%

PM 2.5 in JST(CMB, Jul, 2001)

36%

2%

14%

15%

24%

1%

0%2%

6%

Source apportionment of PM 2.5 in JST

CMAQ CMB

24.42 (mg/m3) 22.53 (mg/m3)

Sulfate

Nitrate

Ammonium

Diesel (primary)

Gasoline (primary)

Roaddust (primary)

Woodburning (primary)

Other organic matter

Other mass

28.07 (mg/m3) 13.28 (mg/m3)

Jan

uary

2002 J

uly

2001


January 2002

0

10

20

30


(mg/

m3)

July 2001

0

10

20

30


(mg/

m3)

Source apportionment of PM 2.5(CMB:Left column, CMAQ: right column)

Jan

uary

2002 Ju

ly 2

001

0

5

10

15

20

25

30


Sulfate Nitrate AmmoniumDiesel (primary) Gasoline (primary) Roaddust (primary)Woodburning (primary) Other organic matter Other mass

CMB

CMAQ


Note. CMB data are missing on July 1, 2, 5, 11, 22, 24, and 28.

Source apportionment of PM 2.5 in JST (July 2001)

0

20

40

60

7/1/2001 7/4/2001 7/7/2001 7/10/2001 7/13/2001

[ug

/m3

]

0

20

40

60

7/16/2001 7/19/2001 7/22/2001 7/25/2001 7/28/2001

[ug

/m3

]

CMB with MM

CMAQ (12 km)

CMAQ (36 km)

0204060

7/1/2001 7/4/2001 7/7/2001 7/10/2001 7/13/2001

others

other_organics

vegetative detritus

natural gas combustion

Meat cooking

primary_woodburning

primary_roaddust

primary_powerplant

primary_gasoline

primary_diesel

Ammonium

Nitrate

Sulfate

(CMB: 1st column, CMAQ (12km): 2nd column, CMAQ (36km): 3rd column)

Reasonableagreement

…


0.00

10.00

20.00

30.00

40.00

50.00

1/14/02 1/17/02 1/20/02 1/23/02 1/26/02 1/29/02

Other mass

Gasoline

Diesel

Road dust

Sulfate

Coal-fired power plant

Nitrate

Ammonium

Wood burining

Source apportionment of PM 2.5 in JST (Jan 2001) CMB with

MM

CMAQ (12 km)

CMAQ (36 km)

(CMB: 1st column, CMAQ (12km): 2nd column, CMAQ (36km): 3rd column)

Remarkable agreement

…most

others not


CMAQ vs. CMB* Primary PM Source Fractions

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

sun

fri

wed

mon

sat

thu

tue

sun

fri

wed

mon

sat

CMAQ other_organics

CMAQ powerplant

CMAQ woodburning

CMAQ roaddust

CMAQ diesel

CMAQ gasoline

0.00

0.20

0.40

0.60

0.80

1.00

su

n

fri

we

d

mo

n

sa

t

thu

tue

su

n

fri

we

d

mo

n

sa

t

LGO JST OTHROC

LGO JST CFPP

LGO JST BURN

LGO JST SDUST

LGO JST MDDT

LGO JST CATGV

More variation than I would expect in emissions and large volume average*Not using molecular markers


California (Kleeman: see 1PE11)


EBM Application: Site Representativeness

• Compare observations to each other and to model results to help assess site representativeness– Grid model provides volume-averaged

concentrations• Desired for health study

• Assessed representativeness of Jefferson Street site used in epidemiological studies– Found it better correlated with simulations for most

species than other Atlanta sites


Results: SO4-2

JST FTM SD TU CMAQ

Mean (mg/m3) 4.86 4.33 4.27 4.14 4.77

Correlation (R) 0.73 0.54 0.44 0.49 1.00

RMSE 2.30 3.02 3.41 3.31 -

0.0

4.0

8.0

12.0

16.0

20.0

1/1/00 1/31/00 3/1/00 3/31/00 4/30/00 5/30/00 6/29/00 7/29/00 8/28/00 9/27/00 10/27/00 11/26/00 12/26/00 1/25/01 2/24/01 3/26/01 4/25/01 5/25/01 6/24/01 7/24/01 8/23/01 9/22/01 10/22/01 11/21/01 12/21/01

ug

/m3

JST FTM SD TU CMAQ


Emissions-Based Models• EBM’s can provide additional information

– Coverage (chemical, spatial and temporal)• Intelligent interpolator

– Source contributions • Relatively little day-to-day variation in source fractions

from EBM– Reflects inventory– May not be capturing sub-grid(?... Not really grid) scale effects

• Inventory is spatially and temporally averaged• May inhibit use for health studies

• Agreement between EBM and CBM good, at times, less so at others– Longer term averages look reasonable:

• Applicable for control strategy guidance, with care – understand limitations

– Not apparent which is best


Getting back to Health Association Application: What’s Best?

Air Qual.Data

Air Quality Model SA

Health Endpoints

Source-Health Associations

Data

Air Quality Model SA

Species-Health

Associations


Or?

Air Quality Model

C(x,t), S(x,t)

Health Endpoints

DataUnderstandingOf AQM & Obs.

Limitations

ObservdAir Quality

C(x,t)

C(x,t), S(x,t)

Source/SpeciesHealth Associations


Summary• Application of PM Source apportionment models in health studies

more demanding than traditional “attainment-type” modeling– New (and relatively unexplored) set of issues

• Receptor models do not, yet, give same results– Nor do they agree with emissions-based model results (that’s o.k. for

now)– Need a way to better quantify uncertainty– If results driven by a single species, little is gained, for epi application

• Receptor models (probably) lead to excess variability for application in health studies– Representativeness error– Not yet clear if model application, itself, decreases or increases

representativeness error over directly using observations• Emissions-based models

– Likely underestimate variability (too tied to minimally varying inventory)

– Performance is spotty• Groups actively trying to reconcile differences

– Focus on emissions, range of observations, applying different models– Hybrid approaches?


Acknowledgements

• Staff and students in the Air Resources Engineering Center of Georgia Tech

• SEARCH, Emory, Clarkson, UC Davis teams.• SAMI• GA DNR• Georgia Power• US EPA• NIEHS• Georgia Tech


Effect of Grid Resolution

(4x too big)


Performance Metrics Equation

Mean Bias (mg/m3)

Mean Error (mg/m3)

Mean Normalized Bias (%) (-100% to +) Mean Normalized Error (%) (0% to +)

Normalized Mean Bias (%) (-100% to +) Normalized Mean Error (%) (0% to +)

Mean Fractional Bias (%) (-200% to +200%) Mean Fractional Error (%) (0% to +200%)

N

iom CC

NMB

1

1

N

i mo

om

CC

CC

NMFE

1

2

1

N

i mo

om

CCCC

NMFB

1

2

1

N

io

N

iom

C

CCNME

1

1

N

io

N

iom

C

CCNMB

1

1

N

i o

om

C

CC

NMNE

1

1

N

i o

om

C

CC

NMNB

1

1

N

iom CC

NME

1

1

Documents

Georgia Institute of Technology PM Modeling and Source Apportionment Amit Marmur, Dan Cohan, Helena Park, Jeameen Baek, Sangil Lee, Mei Zhang, Jim Boylan,