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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
Georgia 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…
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:
Health endpoints
Sulfate
3
mg / m
10
Statistical
Analysis
Sulfate
SDK
8
FTM
6
TUC
4
JST
2
YG
4
4
04
/29
/0
4
04
/22
/0
4
04
/15
/0
4
04
/08
/0
4
04
/01
/0
4
03
/25
/0
4
03
/18
/0
4
03
/11
/0
4
03
/04
/0
4
02
/26
/0
4
02
/19
/0
4
02
/12
/0
4
02
/05
/0
4
01
/29
/0
4
01
/22
/0
4
01
/15
/0
01
/08
/0
4
0
01
/01
/0
(e.g. time series)
Georgia Institute of Technology
Association
Association between CVD Visits and
Air Quality
Georgia Institute of Technology
(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
Georgia Institute of Technology
Use AQ Models to Address Issues:
Link Sources to Impacts
Data
Air Quality
Model
Health Endpoints
Statistical
Analysis
Source
Impacts
S(x,t)
Association between
Source Impact
and Health Endpoints
Georgia Institute of Technology
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
Georgia Institute of Technology
Monitored
Air Quality
Ci(x,t)
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
Georgia Institute of Technology
*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
EmissionsBased
Lag.
Hybrid
Eulerian (grid)
Source
Specific*
Receptor
CMB
Molec. Mark.
“Mixed PM”
*Kleeman et al. See 1E1.
Georgia Institute of Technology
FA
PMF
Norm.
UNMIX
Source-based Models
Chemistry
Air Quality Model
Emissions
Meteorology
c i
( uci ) ( Kci ) Ri S i
t
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
Obsserved
Air Quality
Ci(t)
n
Ci f i , j S j
j 1
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)
Georgia Institute of Technology
• Strengths
Receptor Models
– 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*
INPUTS
Emissions
(Eij(x,t))
Other Inputs
New emissions:
Eij(x,t)
Air Quality
Model +
DDM-3D
Receptor Model
Ci(x,t), Fij(x,t),
& Sj(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
• 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)
Funding from EPRI,
Southern Company
Georgia Institute of Technology
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?
Georgia Institute of Technology
*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…)
Average Source Contribution
Source
PMF 2
PMF8
ME2
CMB-MM*
Sec. Sulf.
56
62
56
28
Diesel
11
19
Gasol.
}22
15
5
15
3
Soil/dust
1
3
2
2
Wood Smoke
11
6
3
10
Nitr.-rich
7
8
9
5
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
CMB Source Apportionment
M.V.
SDUST
30
PM2.5
OTHROC
25
CEM
20
Sulfate
15
CFPP
10
AMNITR
LGO-CMB: see
Marmur et al.,
6C1
BURN
5
Concentrations ( µg/m3)
Date
25
1/
30
/0
2
1/
28
/0
2
1/
26
/0
2
1/
24
/0
2
1/
22
/0
2
1/
20
/0
2
1/
18
/0
2
30
1/
16
/0
2
1/
14
/0
2
0
JST, Jan., 2002
Wood Smoke
Nitrate
Coal combustion
Sulfate
Industry factor 2
Soil
Industry factor 1
Motor Vehicle
Undetermined
Fine mass
20
15
10
5
0
PMF:
7 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 age
er
Av
y
l
n th
Georgia Institute of Technology
Mo
See Liu et al., 5PC7 Sampling date
1
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
• 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
Species of PM 2.5 in JST
MODEL(CMAQ)
OBS
PM 2.5 in JST 29.42(CMAQ, Jul, 2001)
PM 2.5 in JST 22.53(OBS, Jul, 2001)
29.42 (mg/m3)
22.53 (mg/m3)
28%
27%
36%
39%
PM 2.5 in BHM 21.56(CMAQ, Jan, 2002)
10%
2%
15%
5%
5%
14%
14%
5%
PM 2.5 in JST 28.07(CMAQ, Jan, 2002)
28.07 (mg/m3)33%
15%
PM 2.5 in JST 13.28(OBS, Jan, 2002)
13.28 (mg/m3)
11%
16%
17%
31%
12%
27%
29%
34%
9%
10%
6%
13%
3%
10%
13%
11%
Winter problem largely nitrate + ammonium
Georgia Institute of Technology
Sulfate
Nitrate
Ammonium
Elemental Carbon
Organic carbon
Other mass
Concentration (m g/m3)
SAMI: URM
Fine Mass at Great Smoky Mountains
Model (L) vs. Observations (R)
35.00
Class 1
Class 2
Class 3
Class 4
Class 5
30.00
25.00
20.00
15.00
10.00
5.00
0.00
02/09/94
03/24/93
04/26/95
SO4
08/04/93
NO3
08/07/93
NH4
08/11/93
ORG
Georgia Institute of Technology
07/12/95
EC
07/31/91
SOIL
07/15/95
Performance
Sulfate
EPI OC
10.0
FAQS*
CMAQ36 OC (ug/m3)
8.0
6.0
4.0
2.0
0.0
0.0
2.0
4.0
6.0
8.0
10.0
JST OC (ug/m3)
VISTAS
*Fall Line Air Quality Study,
Epi: 3-year modeling,
VISTAS: UCR/ENVIRON
Georgia Institute of Technology
Simulated a bit low:
Analyses suggests
SOA low
Mean Fractional Error: Combined Studies
Mean Fractional Error
Combined Modeling Studies
200.00
Sulfate
Nitrate
Ammonium
150.00
Ammonium Bi
Organics
EC
100.00
Soils
PM2.5
PM10
50.00
CM
Goal
0.00
Criteria
0.0 4.0 8.0 12.0 16.0 20.0 24.0 28.0
Average Concentration (m g/m3)
Georgia Institute of Technology
Plot by J. Boylan
VISTAS PM Modeling Performance
Mean Fractional Error
All Four Networks: 12 Months (2002)
200
Sulfate
Nitrate
150
Ammonium
Organics
EC
100
Soils
PM2.5
PM10
50
CM
Goal
0
Criteria
0.0
4.0
8.0
12.0
16.0
Average Concentration (m g/m3)
Georgia Institute of Technology
Modeling conducted by ENVIRON, UC-Riverside. Plot by J. Boylan
20.0
30
Species of PM 2.5
25
Species of PM 2.5 in July 2001
(OBS:Left column, MODEL(CMAQ): right column)
20
30
OBS
(m g/m3)
15
10
5
MODEL (CMAQ)
20
10
0
CTR
BHM
0
BHM
Sulfate
GFP
CTR
GFP
JST
JST
OAK
OAK
OLF
Species of PM 2.5 in January 2002
Nitrate
Ammonium
Elemental Carbon
OLF
PNS YRK
PNS
Organic carbon
YRK
Other mass
(m g/m3)
30
20
Too much
simulated
nitrate and
soil dust in
winter
10
0
BHM
CTR
GFP
JST
OAK
OLF
Georgia Institute of Technology
PNS
YRK
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
Georgia Institute of Technology
Nitrate
Predicted vs. Estimated in Organic Aerosol in
Pittsburgh
(Pandis and co-workers)
Primary and Secondary OA
7/12
Predicted [mg/m3]
15
7/13
7/14
7/15
7/16
7/17
7/18
Secondary
12
Primary
9
6
3
0
0
24
48
72
96
120
144
168
0
24
48
72
96
120
144
168
Estimated [mg/m3]
15
12
9
6
3
0
Simulation Hours
• EC Tracer Method (Cabada et al., 2003)
Georgia Institute of Technology 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
Georgia Institute of Technology
– 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
1
EC
0.8
0.6
0.4
0.2
24-hr EC
0
0
spatial sd / temporal sd
• Spatial correlation vs.
temporal correlation (Wade et
al., 2004)
spatial sd / temporal sd
Spatial Variability
20
40
60
distance (km)
80
100
1
0.8
Sulfate
0.6
0.4
0.2
24-hr SO42-
0
0
Georgia Institute of Technology
20
40
60
distance (km)
80
100
Emissions “Diffusion”
Dial Variation of ATL emissions
On-road OC Emissions
SMOKE
0.08
Hartsfield
0.07
0.06
Fraction
0.05
0.04
0.03
0.02
0.01
0
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
Default profile (black) vs. plane/engine
dependent operations (red)
Time (hr)
Chemical dilution: assume
source X has same emissions
composition, independent of
location, etc. (~)
Georgia Institute of Technology
Nonroad OC Emissions
Capturing stochastic
events using
satellites:
Wildfire and
Prescribed burn
PM2.5 Emissions (tons/month)
15.54
5.763
6.765
0.211
14.544
7.623
32.363
65.115
0.046
65.103
3.937
32
3.4
6.107
68.263
48.337
56
5.763
51.681
55.81
135.654
19
15.687
33.408
50.995
19
47.477
12.58
Black: estimates based on fire records
Red: estimates based on satellite images (Ito and Penner, 2004)
Legend
GA
0.040 - 0.390
aug00.TOTAL_PM25
3.857 - 71.533
0.391 - 1.928
1.929
Georgia Institute
of- 3.856
Technology
0.000 - 0.039
51
Sulfate Mean Fractional Error
Mean Fractional Error
Combined Modeling Studies
200.00
150.00
Sulfate
100.00
Goal
Criteria
50.00
X
Spatial variability limit?
0.00
0.0
2.0
4.0
6.0
8.0
10.0 12.0
Average Concentration (m g/m3)
Georgia Institute of Technology
EC Mean Fractional Error
Mean Fractional Error
Combined Modeling Studies
200.00
150.00
EC
100.00
Goal
Criteria
X
50.00
0.00
0.0
1.0
2.0
3.0
Average Concentration (m g/m3)
Georgia Institute of Technology
4.0
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 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
Evaluation
Now getting sufficient data:
Holes will get filled
Application
Computational
implementation
Mathematics
Science (chemistry/physics)
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)
• 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
Source apportionment of PM 2.5 in JST
CMAQ
CMB
PM 2.5 in JST(CMAQ, Jul, 2001)
PM 2.5 in JST(CMB, Jul, 2001)
24.42 (mg/m3)
10%
22.53 (mg/m3)
24%
36%
11%
38%
PM 2.5 in BHM(CMAQ, Jan, 2002)
8%
15%
7%
2%
2%
5%
14%
5%
PM 2.5 in JST(CMAQ, Jan, 2002)
28.07
13%
1%
2%
14%
0% 6%
PM 2.5 in JST(CMB, Jan, 2002)
(mg/m3)
13.28
(mg/m3)
8%
11%
Diesel (primary)
17%
Gasoline (primary)
8%
12%
29%
9%
10%
5%
0%
13%
Roaddust (primary)
Woodburning (primary)
23%
2%
Nitrate
Ammonium
11%
10%
Sulfate
5%
14%
Georgia Institute of Technology
Other organic matter
Other mass
Source apportionment of PM 2.5
July 2001
(CMB:Left column, CMAQ: right column)
30
30
25
20
15
(mg/m3)
July 2001
10
CMB
20
CMAQ
10
5
0
BHM
0
CTR
BHM
GFP
JST
JST
January 2002
Sulfate
Diesel (primary)
Woodburning (primary)
30
(mg/m3)
CTR
GFP
OAK
OAK
OLF
PNS
Nitrate
Gasoline (primary)
Other organic matter
OLF
YRK
Ammonium
Roaddust (primary)
Other mass
20
10
0
BHM
CTR
GFP
JST
OAK
OLF
Georgia Institute of Technology
PNS
YRK
PNS
Source apportionment of PM 2.5 in JST (July 2001)
(CMB: 1st column, CMAQ (12km): 2nd column, CMAQ (36km): 3rd column)
60
CMB with MM
CMAQ (12 km)
CMAQ (36 km)
[ug/m3]
40
20
0
7/1/2001
7/4/2001
7/7/2001
[ug/m3]
60
7/13/2001
Reasonable
agreement…
60
40
20
0
7/1/2001 7/4/2001 7/7/2001 7/10/2001 7/13/2001
[ug/m3]
40
7/10/2001
20
0
7/16/2001
7/19/2001
7/22/2001
7/25/2001
7/28/2001
Georgia Institute of Technology
Note. CMB data are missing on July 1, 2, 5, 11, 22, 24, and 28.
others
other_organics
vegetative detritus
natural gas combustion
Meat cooking
primary_woodburning
primary_roaddust
primary_powerplant
primary_gasoline
primary_diesel
Ammonium
Nitrate
Sulfate
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
50.00
Other mass
40.00
Gasoline
Diesel
Road dust
30.00
Sulfate
Coal-fired power plant
Nitrate
20.00
Ammonium
Wood burining
10.00
0.00
1/14/02
1/17/02
1/20/02
1/23/02
1/26/02
Georgia Institute of Technology
1/29/02
CMAQ vs. CMB* Primary PM Source Fractions
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
CMAQ other_organics
CMAQ powerplant
CMAQ woodburning
CMAQ roaddust
CMAQ diesel
sat
mon
wed
fri
sun
tue
thu
sat
mon
wed
fri
sun
CMAQ gasoline
1.00
LGO JST OTHROC
0.80
LGO JST CFPP
0.60
LGO JST BURN
0.40
LGO JST SDUST
LGO JST MDDT
0.20
LGO JST CATGV
sat
mon
wed
fri
sun
tue
thu
sat
mon
wed
fri
sun
0.00
Georgia Institute of Technology
*Not using molecular markers
More variation than I
would expect in emissions
and large volume average
California (Kleeman: see 1PE11)
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
20.0
16.0
ug/m3
12.0
8.0
4.0
0.0
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
JST
FTM
SD
TU
CMAQ
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
-
Georgia Institute of Technology
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?
Data
Air Quality
Model SA
Health
Endpoints
Air Qual.
Data
Source-Health
Associations
SpeciesHealth
Associations
Georgia Institute of Technology
Air Quality
Model SA
Observd
Air Quality
C(x,t)
Or?
Data
Health
Endpoints
Air Quality
Model
C(x,t), S(x,t)
Understanding
Of AQM & Obs.
Limitations
C(x,t), S(x,t)
Source/Species
Health 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, 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
• 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
(4x too big)
Georgia Institute of Technology
Performance Metrics
Equation
Mean Bias (mg/m3)
1 N
MB Cm Co
N i 1
1 N
ME Cm Co
N i 1
Mean Error (mg/m3)
Mean Normalized Bias (%)
(-100% to +)
Mean Normalized Error (%)
(0% to +)
1 N Cm Co
MNB
N i 1 Co
Normalized Mean Bias (%)
(-100% to +)
Normalized Mean Error (%)
(0% to +)
NMB
Mean Fractional Bias (%)
(-200% to +200%)
Mean Fractional Error (%)
(0% to +200%)
1
MFB
N
N
C
i 1
m
C
i 1
NME
i 1
i 1
o
C m Co
Co
C
i 1
m
C
i 1
Cm
2
Georgia Institute of Technology
1
MFE
N
Co
N
o
Cm Co
C
N
N
Co
N
N
1
MNE
N
o
C m Co
i 1 Co C m
2
N