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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) Russell Georgia Institute of Technology. With Special Thanks to:. Eric Edgerton, Ben Hartsell and John Jansen

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pm modeling and source apportionment

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) Russell

Georgia Institute of Technology

Georgia Institute of Technology

with special thanks to
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…

Georgia Institute of Technology

genesis
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

Georgia Institute of Technology

epidemiology
Epidemiology
  • Identify associations between air quality metrics and health endpoints:

Health endpoints

Statistical

Analysis

(e.g. time series)

Sulfate

Association

Georgia Institute of Technology

association between cvd visits and air quality
Association between CVD Visits and Air Quality

Georgia Institute of Technology

(See Tolbert et al., 9C2)

issues
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

Georgia Institute of Technology

use aq models to address issues link sources to impacts
Health Endpoints

Statistical

Analysis

Use AQ Models to Address Issues: Link Sources to Impacts

Data

Air Quality

Model

Source

Impacts

S(x,t)

Association between

Source Impact

and Health Endpoints

Georgia Institute of Technology

use aq models to address issues assess error s provide increased coverage
Use AQ Models to Address Issues: Assess Errors, Provide Increased Coverage

Data

Air Quality

Model

Air Quality

C(x,t)

Health Endpoints

Site

Representative?

Association between

Concentrations

and Health Endpoints

Monitored

Air Quality

Ci(x,t)

Georgia Institute of Technology

slide9
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?

Georgia Institute of Technology

pm modeling and source apportionment10
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

Georgia Institute of Technology

*On slide 10, the talk starts…

slide11
Emissions-

Based

Hybrid

Receptor

CMB

FA

Lag.

Eulerian (grid)

PMF

Molec. Mark.

Norm.

Source

Specific*

“Mixed PM”

UNMIX

PM (Source Apportionment) Models(those capable of providing some type of information as to how specific sources impact air quality)

PM Models

Georgia Institute of Technology

*Kleeman et al. See 1E1.

source based models
ChemistrySource-based Models

Air Quality Model

Emissions

Meteorology

Georgia Institute of Technology

source based models13
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

Georgia Institute of Technology

receptor models
Receptor Models

Obsserved

Air 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)

Georgia Institute of Technology

receptor models15
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

Georgia Institute of Technology

hybrid inverse model approach
Hybrid: Inverse Model Approach*

INPUTS

Emissions (Eij(x,t))

Ci(x,t), Fij(x,t), & Sj(x,t)

Air Quality

Model +

DDM-3D

Other Inputs

Receptor Model

New emissions:

Eij(x,t)

Observations taken

from routine measurement

networks or special

field studies

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

Georgia Institute of Technology

source apportionment application
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

Georgia Institute of Technology

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

search asaca
rural

suburban

urban

Yorkville (YRK)

North Birmingham (BHM)

Jefferson Street (JST)

Centreville (CTR)

Oak Grove (OAK)

Outlying Landing Field #8 (OLF)

Gulfport (GFP)

Pensacola (PNS)

SEARCH & ASACA

SEARCH

Funding from EPRI, Southern Company

ASACA

Georgia Institute of Technology

questions
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?

Georgia Institute of Technology

*On slide 10, the talk starts…

source apportionment results
Source Apportionment Results
  • Hopke and co-workers (Kim et al., 2003; 2004) for Jefferson Street SEARCH site (see, also 1PE4…)

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

Georgia Institute of Technology

slide21
Daily Variation

LGO-CMB: see

Marmur et al.,

6C1

PMF: See Liu et al., 5PC7

Georgia Institute of Technology

receptor models22
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

Georgia Institute of Technology

emissions based model ebm source apportionment
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)

Georgia Institute of Technology

slide24
Species of PM 2.5 in JST

MODEL(CMAQ) OBS

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

January 2002 July 2001

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

Winter problem largely nitrate + ammonium

Georgia Institute of Technology

sami urm
SAMI: URM

Georgia Institute of Technology

performance
Performance

Sulfate

EPI OC

FAQS*

Simulated a bit low:

Analyses suggests

SOA low

VISTAS

*Fall Line Air Quality Study,

Epi: 3-year modeling,

VISTAS: UCR/ENVIRON

Georgia Institute of Technology

slide27
Mean Fractional Error: Combined Studies

Georgia Institute of Technology

Plot by J. Boylan

slide28
VISTAS PM Modeling Performance

Georgia Institute of Technology

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

slide29
Species of PM 2.5

(OBS:Left column, MODEL(CMAQ): right column)

OBS

MODEL (CMAQ)

January 2002 July 2001

Too much simulated nitrate and soil dust in winter

Georgia Institute of Technology

performance30
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

Georgia Institute of Technology

slide31
Predicted vs. Estimated in Organic Aerosol in Pittsburgh (Pandis and co-workers)Primary and Secondary OA

Predicted [g/m3]

Estimated [g/m3]

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

See also 4D4, 5D2…

Georgia Institute of Technology

limitations on model performance
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

Georgia Institute of Technology

spatial variability
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

EC

Sulfate

Georgia Institute of Technology

emissions diffusion
Emissions “Diffusion”

On-road OC Emissions

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. (~)

Nonroad OC Emissions

Georgia Institute of Technology

slide35
Wildfire and Prescribed burn

Capturing stochastic events using satellites:

32

3.4

56

19

19

51

Black: estimates based on fire records

Red: estimates based on satellite images (Ito and Penner, 2004)

Georgia Institute of Technology

slide36
Sulfate Mean Fractional Error

X

Spatial variability limit?

Georgia Institute of Technology

slide37
EC Mean Fractional Error

X

Georgia Institute of Technology

how good are they
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

Now getting sufficient data

Evaluation

Application

Computational

implementation

Mathematics

Science (chemistry/physics)

Georgia Institute of Technology

how good are they39
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

Now getting sufficient data:

Holes will get filled

Evaluation

Application

Computational

implementation

Mathematics

Science (chemistry/physics)

Georgia Institute of Technology

emissions based model performance
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

Georgia Institute of Technology

slide41
Source apportionment of PM 2.5 in JST

CMAQ CMB

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

January 2002 July 2001

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

Georgia Institute of Technology

slide42
Source apportionment of PM 2.5

(CMB:Left column, CMAQ: right column)

CMB

CMAQ

January 2002 July 2001

Georgia Institute of Technology

slide43
Source apportionment of PM 2.5 in JST (July 2001)

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

CMB with MM

CMAQ (12 km)

CMAQ (36 km)

Reasonable

agreement…

Georgia Institute of Technology

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

slide44
Source apportionment of PM 2.5 in JST (Jan 2001)

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

CMB with MM

CMAQ (12 km)

CMAQ (36 km)

Remarkable agreement…

most

others not

Georgia Institute of Technology

cmaq vs cmb primary pm source fractions
CMAQ vs. CMB* Primary PM Source Fractions

More variation than I would expect in emissions and large volume average

Georgia Institute of Technology

*Not using molecular markers

california kleeman see 1pe11
California (Kleeman: see 1PE11)

Georgia Institute of Technology

ebm application site representativeness
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

Georgia Institute of Technology

results so 4 2
Results: SO4-2

Georgia Institute of Technology

emissions based models
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

Georgia Institute of Technology

getting back to health association application what s best
Getting back to Health Association Application: What’s Best?

Air Quality

Model SA

Data

Health

Endpoints

Source-Health

Associations

Air Qual.

Data

Species-

Health

Associations

Air Quality

Model SA

Georgia Institute of Technology

slide51
Or?

Observd

Air Quality

C(x,t)

Air Quality

Model

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

Understanding

Of AQM & Obs.

Limitations

Data

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

Health

Endpoints

Source/Species

Health Associations

Georgia Institute of Technology

summary
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?

Georgia Institute of Technology

acknowledgements
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

Georgia Institute of Technology

effect of grid resolution
Effect of Grid Resolution

(4x too big)

Georgia Institute of Technology

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