georgia institute of technology pm modeling and source apportionment amit marmur, dan cohan, helena...
TRANSCRIPT
Georgia Institute of Technology
PM Modeling and Source Apportionment
Amit Marmur, Dan Cohan, Helena Park, Jeameen Baek, Sangil Lee, Mei Zhang, Jim
Boylan, Katie Wade,Jim Mulholland, …, and
Armistead (Ted) RussellGeorgia Institute of Technology
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:
Sulfate
0
2
4
6
8
10
m g / m
3
SDK
FTM
TUC
JST
YG
Sulfate
Health endpoints
StatisticalAnalysis
(e.g. time series)
Association
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: Assess Errors, Provide Increased Coverage
DataAir Quality
ModelAir Quality
C(x,t)
Health Endpoints
Association between Concentrations
and Health Endpoints
MonitoredAir Quality
Ci(x,t)
SiteRepresentative?
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…
Georgia Institute of Technology
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 (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
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*
Emissions (Eij(x,t)) Ci(x,t), Fij(x,t),
& Sj(x,t)Air Quality
Model +DDM-3D
Receptor Model Observations takenfrom routine measurement
networks or specialfield studies
New emissions:Eij(x,t)
Other Inputs
INPUTS
Main assumption in the formulation:
A major source for the discrepancy between predictions and observations are the emission estimates
*Other, probably better, hybrid approaches exist
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
Funding from EPRI, Southern Company
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… 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
PM 2.5 in JST 28.07(CMAQ, Jan, 2002)
11%
29%
13%6%
10%
31%
PM 2.5 in JST 29.42(CMAQ, Jul, 2001)
39%
5%14%
5%
10%
27%
PM 2.5 in JST 13.28(OBS, Jan, 2002)
17%
12%
10%
11%
34%
16%
PM 2.5 in JST 22.53(OBS, Jul, 2001)
36%
2%
14%5%
15%
28%
Species of PM 2.5 in JST
Jan
uary
2002 J
uly
2001
MODEL(CMAQ) OBS
29.42 (mg/m3) 22.53 (mg/m3)
28.07 (mg/m3) 13.28 (mg/m3)Sulf ate
Nitrate
Ammonium
Elemental Carbon
Organic carbon
Other mass
Winter problem largely nitrate + ammonium
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 (m
g/m
3)
SO4 NO3 NH4 ORG EC SOIL
02/09/94 03/24/93 04/26/95 08/04/93 08/07/93 08/11/93 07/12/95 07/31/91 07/15/95
Class 5Class 4Class 3Class 2Class 1
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
Combined Modeling Studies
0.00
50.00
100.00
150.00
200.00
0.0 4.0 8.0 12.0 16.0 20.0 24.0 28.0
Average Concentration (mg/m3)
Me
an
Fra
cti
on
al
Err
or
Sulfate
Nitrate
Ammonium
Ammonium Bi
Organics
EC
Soils
PM2.5
PM10
CM
Goal
Criteria
Mean Fractional Error: Combined Studies
Plot by J. Boylan
Georgia Institute of Technology
All Four Networks: 12 Months (2002)
0
50
100
150
200
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (mg/m3)
Me
an
Fra
cti
on
al
Err
or
Sulfate
Nitrate
Ammonium
Organics
EC
Soils
PM2.5
PM10
CM
Goal
Criteria
VISTAS PM Modeling Performance
Modeling conducted by ENVIRON, UC-Riverside. Plot by J. Boylan
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
(mg
/m3 )
Species of PM 2.5 in July 2001
0
10
20
30
BHM CTR GFP J ST OAK OLF PNS YRK
(mg
/m3 )
0
5
10
15
20
25
30
BHM CTR GFP JST OAK OLF PNS YRK
Sulfate Nitrate Ammonium Elemental Carbon Organic carbon Other mass
Jan
uary
2002 Ju
ly 2
001
Species of PM 2.5(OBS:Left column, MODEL(CMAQ): right column)
OBS
MODEL (CMAQ)
Too much simulated nitrate and soil dust in winter
Georgia Institute of Technology
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
Predicted vs. Estimated in Organic Aerosol in Pittsburgh
(Pandis and co-workers) Primary and Secondary OA
0
3
6
9
12
15
0 24 48 72 96 120 144 168
Simulation Hours
0
3
6
9
12
15
0 24 48 72 96 120 144 168
Secondary
Primary
7/12 7/13 7/14 7/15 7/16 7/17 7/18P
redi
cted
[mg
/m3 ]
Est
imat
ed [mg
/m3 ]
• EC Tracer Method (Cabada et al., 2003) See also 4D4, 5D2…
Georgia Institute of Technology
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 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 X has same emissions composition, independent of location, etc. (~)
On-road OC Emissions
Nonroad OC Emissions
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
Combined Modeling Studies
0.00
50.00
100.00
150.00
200.00
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Average Concentration (mg/m3)
Me
an
Fra
cti
on
al
Err
or
Sulfate
Goal
Criteria
Sulfate Mean Fractional Error
X Spatial variability limit?
Georgia Institute of Technology
Combined Modeling Studies
0.00
50.00
100.00
150.00
200.00
0.0 1.0 2.0 3.0 4.0
Average Concentration (mg/m3)
Me
an
Fra
cti
on
al
Err
or
EC
Goal
Criteria
EC Mean Fractional Error
X
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
Science (chemistry/physics)
Mathematics
Computational implementation
Evaluation
Application
Now getting sufficient data
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
Science (chemistry/physics)
Mathematics
Computational implementation
Evaluation
Application
Now getting sufficient data:Holes will get filled
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
PM 2.5 in JST(CMAQ, Jan, 2002)
11%
29%
13%5%
2%
9%
10%
8%
13%
PM 2.5 in JST(CMAQ, Jul, 2001)
38%
5%14%5%
2%
7%
8%
11%
10%
PM 2.5 in JST(CMB, Jan, 2002)
17%
12%
10%
14%5%
0%
23%
11%
8%
PM 2.5 in JST(CMB, Jul, 2001)
36%
2%
14%
15%
24%
1%
0%2%
6%
Source apportionment of PM 2.5 in JST
CMAQ CMB
24.42 (mg/m3) 22.53 (mg/m3)
Sulfate
Nitrate
Ammonium
Diesel (primary)
Gasoline (primary)
Roaddust (primary)
Woodburning (primary)
Other organic matter
Other mass
28.07 (mg/m3) 13.28 (mg/m3)
Jan
uary
2002 J
uly
2001
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 with MM
CMAQ (12 km)
CMAQ (36 km)
0204060
7/1/2001 7/4/2001 7/7/2001 7/10/2001 7/13/2001
others
other_organics
vegetative detritus
natural gas combustion
Meat cooking
primary_woodburning
primary_roaddust
primary_powerplant
primary_gasoline
primary_diesel
Ammonium
Nitrate
Sulfate
(CMB: 1st column, CMAQ (12km): 2nd column, CMAQ (36km): 3rd column)
Reasonableagreement
…
Georgia Institute of Technology
0.00
10.00
20.00
30.00
40.00
50.00
1/14/02 1/17/02 1/20/02 1/23/02 1/26/02 1/29/02
Other mass
Gasoline
Diesel
Road dust
Sulfate
Coal-fired power plant
Nitrate
Ammonium
Wood burining
Source apportionment of PM 2.5 in JST (Jan 2001) CMB with
MM
CMAQ (12 km)
CMAQ (36 km)
(CMB: 1st column, CMAQ (12km): 2nd column, CMAQ (36km): 3rd column)
Remarkable agreement
…most
others not
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*Not using molecular markers
Georgia Institute of Technology
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
JST FTM SD TU CMAQ
Mean (mg/m3) 4.86 4.33 4.27 4.14 4.77
Correlation (R) 0.73 0.54 0.44 0.49 1.00
RMSE 2.30 3.02 3.41 3.31 -
0.0
4.0
8.0
12.0
16.0
20.0
1/1/00 1/31/00 3/1/00 3/31/00 4/30/00 5/30/00 6/29/00 7/29/00 8/28/00 9/27/00 10/27/00 11/26/00 12/26/00 1/25/01 2/24/01 3/26/01 4/25/01 5/25/01 6/24/01 7/24/01 8/23/01 9/22/01 10/22/01 11/21/01 12/21/01
ug
/m3
JST FTM SD TU CMAQ
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?
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, 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)
Mean Error (mg/m3)
Mean Normalized Bias (%) (-100% to +) Mean Normalized Error (%) (0% to +)
Normalized Mean Bias (%) (-100% to +) Normalized Mean Error (%) (0% to +)
Mean Fractional Bias (%) (-200% to +200%) Mean Fractional Error (%) (0% to +200%)
N
iom CC
NMB
1
1
N
i mo
om
CC
CC
NMFE
1
2
1
N
i mo
om
CCCC
NMFB
1
2
1
N
io
N
iom
C
CCNME
1
1
N
io
N
iom
C
CCNMB
1
1
N
i o
om
C
CC
NMNE
1
1
N
i o
om
C
CC
NMNB
1
1
N
iom CC
NME
1
1