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Integrated Source/Receptor-Based Methods for Source Apportionment and Area of Influence Analysis. U.S. EPA STAR PM Source Apportionment Progress Review Workshop July 19, 2005 Jeameen Baek, Amit Marmur, Dan Cohan, Helena Park, Sangil Lee, Jim Boylan, Katie Wade,Jim Mulholland, …, - PowerPoint PPT Presentation
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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
Georgia Institute of Technology
Georgia Institute of Technology
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
Georgia Institute of Technology
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.
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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)
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
• 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
)()(
Georgia Institute of Technology
36-km
4-km
12-km
FAQS Model Application Domain
PM-SA applied within 12 km domain
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
Daily Variation: PMF vs. CMB-LGO
PMF
Georgia Institute of Technology
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…
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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.
Georgia Institute of Technology
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• 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
Georgia Institute of Technology
Association between CVD Visits and Air Quality
(See Tolbert et al., 9C2)
Georgia Institute of Technology
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
Data
Air Quality Model
SourceImpactsS(x,t)
Health Endpoints
StatisticalAnalysis
Association between Source Impact
and Health Endpoints
Georgia Institute of Technology
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?
Georgia Institute of Technology
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 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…
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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.
Georgia Institute of Technology
Source-based Models
Emissions
Chemistry
Air Quality Model
Meteorology
Georgia Institute of Technology
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
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)
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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…
Georgia Institute of Technology
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
Georgia Institute of Technology
Daily Variation
PMF: See Liu et al., 5PC7
LGO-CMB: see Marmur et al.,
6C1
Georgia Institute of Technology
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
Georgia Institute of Technology
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
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
Georgia Institute of Technology
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
BHM CTR GFP J ST OAK OLF PNS YRK
(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
Georgia Institute of Technology
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)
Georgia Institute of Technology
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
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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
Georgia Institute of Technology
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
Georgia Institute of Technology
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:
Georgia Institute of Technology
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
Georgia Institute of Technology
January 2002
0
10
20
30
BHM CTR GFP J ST OAK OLF PNS YRK
(mg/
m3)
July 2001
0
10
20
30
BHM CTR GFP J ST OAK OLF PNS YRK
(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
BHM CTR GFP J ST OAK OLF PNS YRK
Sulfate Nitrate AmmoniumDiesel (primary) Gasoline (primary) Roaddust (primary)Woodburning (primary) Other organic matter Other mass
CMB
CMAQ
Georgia Institute of Technology
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)
Georgia Institute of Technology
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
Georgia Institute of Technology
California (Kleeman et al.)
Georgia Institute of Technology
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
Georgia Institute of Technology
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: 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
Georgia Institute of Technology
What’s Best?
Air Qual.Data
Air Quality Model SA
Health Endpoints
Source-Health Associations
Data
Air Quality Model SA
Species-Health
Associations
Georgia Institute of Technology
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
Georgia Institute of Technology
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?
Georgia Institute of Technology
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
Georgia Institute of Technology
Effect of Grid Resolution
(4x too big)