Integrated Source/Receptor-Based Methods for Source Apportionment and Area of Influence Analysis

Georgia Institute of Technology

Integrated Source/Receptor-Based Methods for Source Apportionment and Area of

Influence Analysis

U.S. EPA STAR PM Source ApportionmentProgress Review Workshop

July 19, 2005

Jeameen Baek, Amit Marmur, Dan Cohan, Helena Park, Sangil Lee, Jim Boylan, Katie Wade,Jim Mulholland, …, Talat Odman, Mei Zheng and Armistead (Ted) Russell



Outline• Format: Provide overview (minimal details) with

some suggestive results– Not yet definitive… comments desired.

• Objectives– Detailed vs. overarching

• Direct sensitivity analysis for source apportionment– Results for Atlanta

• Integrated source/receptor-based approach– Preliminary results

• Source apportionment of PM2.5– Comparison between CMAQ and receptor models

• Future activities


Proposal Objectives• Extend ozone source apportionment method to particulate matter.• Inter-compare results from a variety of source-apportionment

methods, including both receptor and source-oriented approaches.• Identify strengths and limitations of the approaches in the

applications, focusing on the reasons for disagreement and under what conditions the various approaches tend to agree and disagree most.

• Quantify uncertainties involved in the application of the various source apportionment methods.

• Further develop and assess the Area-of-Influence (AOI) analysis technique, and compare the results to those obtained using PSCF.

• Assess the relative strengths of using Supersite level data vs. routine monitoring data for source apportionment applications.

• Provide source apportionment results to health effects researchers.


Overarching Objective• Improve our ability to accurately identify how

current and future sources impact particulate matter– For use in air quality management and health effects

assessments– Spatial and temporal completeness– Compositional and size distribution detail– Quantified uncertainties– Preferably not overly burdensome and can be

conducted by various communities


Activities to Date

• Implemented DDM for PM source apportionment• Inverse Modeling for identifying PM emission biases

– Preliminary results• Added organic carbon (and other) PM source tracers• Comparison of SA approaches

– Compared CMAQ, CMB-reg, CMB-MM, CMB-LGO, PMF-2, PMF-8, PMF-PM+gases

• Improving SA analysis via environment-specific measurements– Prescribed forest emissions

• Analyzed for OC, EC, metals, organic species, ions– Freeway, 500 m away, forest (all summer) (analysis underway)

• Also measured water soluble OC

• Provided SA results to health effects researchers– Preliminary analysis conducted


Receptor vs. Source-oriented Model

Meteorology

Air Quality

Source-compositions (F)

Source-oriented Model (3D Air-quality Model)

Receptor (monitor)

Receptor Model

Source Impacts

Chemistry

Receptor model C=f(F,S)


Source-Oriented Source Apportionment

• Use first-principles, model to follow the emissions, transport, transformation and fate of contaminants– Typical air quality models include CMAQ, CAMX,

URM, UAM, EUMAC,…

• Identify source impacts by– Removing simulated source (brute force)– Instrument model to calculate impacts directly

• First and higher-order sensitivity analysis (e.g., DDM)• Can also use a receptor-oriented sensitivity approach

(adjoint method)

ConcentrationConcentration

ss

Emissions, Initial Conditions, Boundary

Conditions, etc.

Air Quality Model

∆

SensitivitiesSensitivities

Check scientific understandingExtend beyond observationsForecasting and prediction

∆ (e.g., Atlanta Emissions)

Air Quality Model

Atmospheric responseControl strategiesSource apportionment


Sensitivity analysis• Given a system, find how

the state (concentrations) responds to incremental changes in the input and model parameters:

Inputs (P)

ModelParameters

(P)

Model

Sensitivity Parameters:

State Variables: C x, t

S C

Piji

jx, t

If Pj are emission, Sij are the sensitivities/responses to emission changes, i.e., sensitivity of ozone to Atlanta NOx emissions


• Define first order sensitivities as

• Take derivatives of

• Solve sensitivity equations simultaneously

jiij ECS /)1(

Sensitivity Analysis with Decoupled Direct Method (DDM)

iiiii ERCC

t

C K u

)()(

Advection Diffusion Chemistry Emissions

ijijijij ESSt

S JS K u

)()(


36-km

4-km

12-km

FAQS Model Application Domain

PM-SA applied within 12 km domain


Atlanta PM2.5 Source Apportionment

July, 2001

Atlanta Secondary PM2.5

-2

0

2

4

6

8

10

12

9.03 11.52 15.12 10.06 8.26 16.30 14.53 9.82 13.18 17.29 18.28 17.38 13.40

6-Jul 7-Jul 8-Jul 9-Jul 10-Jul 11-Jul 12-Jul 13-Jul 14-Jul 15-Jul 16-Jul 17-Julaverage

Date and Concentration (ug/m3)

Sen

sit

ivit

y (

ug

/m3)

OtherBC SO2SC VOCSC SO2SC NOxNC VOCNC SO2NC NOxTN VOCTN NH3TN SO2TN NOxAL NH3AL SO2AL NOxN.GA NH3N.GA NOxBranch SO2Branch NOxAtlanta VOCAtlanta NH3Atlanta SO2Atlanta NOx


Inverse Modeling Source Apportionment and Inventory

Analysis• Integrated observations and emissions-based air

quality modeling to identify biases in emissions inventories– Three-dimensional AQM (CMAQ model), direct sensitivity

analysis (DDM-3D), receptor model (ridge regression)– The AQM provides concentration fields– DDM-3D provides sensitivity fields, i.e., how simulated

concentrations vary as emissions are adjusted• Sensitivity field provides a chemically-evolved source

fingerprint– Ridge regression model, using predicted and observed

concentrations, as well as modeled sensitivities, determines optimal adjustment to emissions to derive emission scaling factors


Hybrid: Inverse Model Approach*

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

& Sj(x,t)Air Quality

Model +DDM-3D

Inverse Model:Minimize

differences

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


Application

• CMAQ w/ DDM• Continental US, 36 km domain

– 12 km in SE to come

• July 2001 & January 2002• AQS, IMPROVE, ASACA, SEARCH, Supersite

data• Divided US in to six regions

P

M

W N

S G


Regionally-Specific Emissions Scaling Factors

01234

P M W N S G01234

P M W N S G

01234

P M W N S G01234

P M W N S G

Weekday Weekend

SO2 (g)

Elemental Carbon

Pacific; Mountain; Midwest; Northeast; Southeast; Georgia


Comparison of SA Approaches

• Wish to compare/contract/dissect various source apportionment methods

• Have a “proponent” of each method apply approach as well as they knew how, and compare results– CMB-regular, CMB-molecular marker, CMB-Lipshitz Global

Optimizer, Positive Matrix Factorization (2 & 8 C; gas phase), CMAQ

• Apply models to same data/periods with extensive monitoring– July, 2001 & January 2002

• Eastern Supersite coordinated intensive periods– Additional data for PMF methods

– SEARCH and ASACA data

• Identify problems and how they might impact results– Uncertainty analysis


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


Many Methods, Many Answers

0

5

10

15

20

25

30

35

40

CMB-Reg

CMB-MM

CMB-LGO

PMF CMAQ

Co

ncen

trati

on

(u

g m

3 )

UnidentifiedOther ECSecondaryOMOther OMVeg. DetritusNat. GasUnpaveed Road DustPavedRoad DustIndustrialCementDustCoalMeat CookingNat. GasWoodBurningMotVehGasolineDieselAmm.NitrateNitrateAmmoniumBSulfAmmSulfateAmmoniumSulfate

Atlanta, July 17, 2001


Daily Variation: PMF vs. CMB-LGO

PMF


0

1

2

3

0 1 2 3

CMAQ (36km) [g m-3]

CM

B-M

M [ g

m-3

]

Averaged contribution over the eight SEARCH stations for July 2001 and January 2002

0

1

2

3

0 1 2 3

Diesel Gasoline

Power Plant Road Dust

Wood Burning Meat Cooking

Natural Gas Other organic mass

Other mass

r = 0.74CMB = 1.04 * CMAQ

Mass contributions to PM2.5:

Comparison of CMB-MM and CMAQ• Average across

months and locations of source contributions looks pretty good, but…


Disaggregatedsome: not so good

• If we look at the results by individual stations, not quite so good… and further

0

2

4

0 2 4

CMAQ (36km)[g m-3]

CM

B-M

M [

g m

-3]

Monthly contributions in SEARCH stations

for July 2001 and January 2002

0

2

4

0 2 4

Diesel

Gasoline

Road dust

Wood burning

r = 0.39


0

5

10

15

20

25

7/16/2001 7/23/2001 7/30/2001

[g

m-3

]

05

1015

2025

7/1/2001 7/8/2001 7/15/2001

[g

m-3

]

Daily average mass contributions to PM2.5 in July 2001

CMB-MM and CMAQ (left to right)

0

2

4

6

8

10

12

14

J ST YRK BH CTR GFP OA OLF PN

Diesel Gasoline Power Plant

Road Dust Wood Burning Meat Cooking

Natural Gas Other organic mass Other mass


Area-of-Influence (AOI)

• Invert DDM fields to identify how a specific amount of emissions will impact a receptor sight– DDM is source-oriented– Sometimes want a

receptor-oriented impact (e.g., specific monitor)

• Approach– Calculate forward

sensitivities– Interpolate between

“sources” to provide sensitivity field coverage

– Invert interpolated field to derive receptor-oriented sensitivity

AOI

Interpolation

Field of sensitivities to point emissions

Inversion

Interpolatedsensitivity

Exact

Comp. vs. exact

Sensitivity of A-NO3 to NO2


Other Activities

• Field Measurements– Prescribed burning (separate contract)

• New source profiles: significantly different than current– Highway, urban, rural

• Highway almost solely gasoline-fueled vehicles– Metals, EC/OC, organics, water soluble, ions

• Plans to go back out in winter, include diesel-laden highway

• Uncertainty analysis– Monte Carlo and other methods

• Continued collaborations with Emory’s Rollins School of Public Health– Use SA results for epidemiologic analyses

• Lots of interesting issues• Definitely more involved than traditional use in AQ

management


Proposal Objectives• Extend ozone source apportionment method to particulate matter.

– Done (though some improvements possible)

• Inter-compare results from a variety of source-apportionment – Initial results of interest

• Identify strengths and limitations of the approaches – Results suggestive

• Quantify uncertainties of the various methods.– Applied MC, expert elicitation, etc.: more to come

• Further develop and assess the Area-of-Influence (AOI)– Initial AOI’s completed (similar to adjoint sensitivity field)

• Assess the relative strengths of using Supersite level data vs. routine monitoring data for source apportionment applications.– Underway

• Provide source apportionment results to health effects researchers.– Initial results provided to Emory colleagues


Questions?

• As I say to my students… results from all of the approaches are wrong, but we need to find out how wrong, when most wrong, and how should we not use them.


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

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: Address 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 (g/m3)

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

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


Receptor Models• Strengths

– Results tied to observed air quality– 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


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


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– 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)


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 (

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


Checklist• Improved inventories

– Meat cooking, forest fires

• DDM-SA: Done• Applied CMAQ for July 2001, January 2002

– Initial evaluation completed– Also applied for 1999-2001

• Inverse Modeling: First set, done• Added tracers: Done• Environment specific observations

– Analyzed for OC, EC, metals, organic species, ions– Prescribed forest emissions– Freeway, 500 m away, forest (all summer) (analysis

underway)• Also measured water soluble OC


Species of PM 2.5 in January 2002

0

10

20

30

BHM CTR GFP J ST OAK OLF PNS YRK

(g

/m3 )

Species of PM 2.5 in July 2001

0

10

20

30


(g

/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


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

[g

/m3 ]

Est

imat

ed [g

/m3 ]

• EC Tracer Method (Cabada et al., 2003)


Limitations on Model Performance

• The are 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

• 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 has same emissions composition


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:


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)• This is not an evaluation of source-apportionment accuracy

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


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

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)


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


California (Kleeman et al.)


EBM Application• 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– Not apparent which is best


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 (g/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


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, 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 Research teams.

• SAMI• GA DNR• Georgia Power• US EPA• NIEHS• Georgia Tech


Effect of Grid Resolution

(4x too big)

Documents

Integrated Source/Receptor-Based Methods for Source Apportionment and Area of Influence Analysis