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Regional Air Quality Modeling:From Source Identification to
Health ImpactsAmit Marmur, … , many great students
and senior researchers, and Armistead (Ted) Russell
Georgia Institute of TechnologyAtlanta, Georgia USA
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With Special Thanks to:• Paige Tolbert and the Emory crew
– As part of ARIES, SOPHIA, and follow on studies
• NIEHS, US EPA, FHWA, Southern Company, SAMI– Financial assistance
• JGSEE of Thailand• And more…
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Issues• Approximately 799,000 excess deaths per year occur per year due
to air pollution – 487,000 in Asia (S, SE and W. Pacific)
• Variety of health impacts in Thailand tied to air pollutants
– Primarily due to:• PM2.5: small particles, Range of health impacts, visibility impairment, …• Ozone • PAPA Studies show strong associations with PM and ozone in Asia
• Most of PM2.5 burden comes from combustion to transform energy– Primary and secondary emissions
• Need reliable approaches to identify how energy sources impact air quality: Source Apportionment– Air quality management– Health impact assessment
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Ozone Formation
h (sunlight)
O3
NOx
oxides of nitrogen(NO + NO2)
VOCs Volatile organic compounds
EEVOCVOC
EENOxNOx
Low OLow O33
High OHigh O33
Ozone Isopleth
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PM Formation
h (sunlight)
PMNOx
VOCs,OC & EC
SO2
Sulfur dioxide
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Particulate Matter
• Complex mixture of solid and liquid particles suspended in the ambient air
• Size classifications– “super-coarse” > 10μm– “coarse” (PM10) < 10μm– “fine” (PM2.5) < 2.5μm– “ultrafine” < 0.1μm
• Many sources• Many chemical species:
BRIG, New Jersey (measured)SulfateNitrateAmmoniumOrganic Carbon
Elemental CarbonSoils and crustals
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Fine Particles: Why should we care ?
Airway Inflammation
Effects on Lung Function
RESPIRATORY EFFECTS CARDIOVASCULAR EFFECTS
Effects on Cardiovascular Function
Vascular Inflammation
Image courtesy of the U.S. EPA
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Outline• Atmospheric modeling
– Types– Basics– Approaches
• Advanced approaches
• Applications– Source impacts– health impact assessment
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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
First-principle Statistical
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Role of Atmospheric Modeling In Air Quality Assessment
Emissions
Air Quality/Health Impacts
Controls
Pollutant Distributions
Air Quality Model
Air Quality Goals
or
n
jjjii SfC
1,
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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)
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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
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Emissions-based Air Quality Model
• Representation of physical and chemical processes – Numerical integration
routines• Scientifically most sound
method to link future emissions changes to air quality
ct
Lx, tc fx, t
ct2t Lxt Ly t Lcz2t Lyt Lxt ct
ComputationalPlanes
5-20
50-200
Air Quality Model
200 species x 10000 hor. grids x 20 layers= 40 million coupled, stiff non-linear differential equations
c
tc c R Si
i i i i ( ) ( )u K
Atmospheric Diffusion Equation
Discretize
Operator splitting
50-100
Emissions
Chemistry
Meteorology
NumericsC=AxB+E
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Air Quality Model
Air Quality
Temperature Radiation
CloudCover Wind
Emissions1. Anthropogenic2. Geogenic3. Biogenic
Sources
TransportedPollutants
Sources (E,S, BCs)
GeographicalFeatures
Transport(U, K, Vd)
Turbulence
NumericalSolution
Techniques
SurfaceDeposition Sink Processes
Topography &Land use
PhotochemicalReactions
ThermochemicalReactions
HomogeneousProcesses
HeterogeneousProcesses
Computed Concentrations
Meteorology
Aerosol Dynamics
ChemicalProcesses
(R)
Chemistry and Aerosol Dynamics
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Atmospheric Modeling Process
Foundation
Pollutant DistributionsEvolving: Sensitivities Uncertainties
EmissionsModel
Meteorological InputsHistorical 2- or 3-D winds; Ground level T, RH; Mixing height, Land use Evolving: 3-D Winds, Diffusivities, Temp., RH, , Solar Insolation (UV & total solar)...
Chemical MechanismHistorical: SpecifiedEvolving: Compiler
NumericalRoutinesHistorical: Advection Chem. Kinet. Evolving Sens. Anal. Proc. Integ. Unc. Anal.
Air QualityModel
EmissionsInputsHistorical: NO, NO2, HONO Lumped VOCs CO, SO2
Evolving: PM, NH3, Detailed VOCs, Adv. Biogenics
Inputs:Emissions InventoryPopulationRoadsLand UseIndustryMeteorology
ModelParameter Calculation
Temperature, Solar Insolation
Chemical Mechanism
Specification
Chemical Mechanism
Specification
Air Quality Data Analysis and ProcessingAir Quality Data Analysis and Processing
Meteorological Model(Diagnostic or Prognostic)
Meteorological Model(Diagnostic or Prognostic)
Model Evaluation
Air Quality Observations
Air Quality Observations
Meteorological Observations
Meteorological Observations
Emissions, Industry and
Human Activity Data
Emissions, Industry and
Human Activity Data
TopographicalData
TopographicalData
Emissions Inventory
Development
Emissions Inventory
Development
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Grids
Nested
Multiscale(Odman et al.)
Adaptive(Odman et al.)
About 15 verticalLayers up to 15 km (many in first 1 km)
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AIRS Station 47-037-0011; Nashville, Davidson Co, TN (urban)
0
20
40
60
80
100
120
0 24 48 72 96 120 144 168 192 216
Time (starting July 11, 1995)
Ozo
ne (p
pb)
AIRS Station 47-099-0101; Look Rock, Blount Co, TN(high elevation)
020406080
100120
0 24 48 72 96 120 144 168 192 216
Time (starting July 11, 1995)
Ozo
ne (p
pb)
a) Observed
12%
3%
28%
4%
37%
16%
Average PM 2.5 concentration
28.4 g/m3
b) Simulated
11%
3%
34%
3%
28%
21%
Ammonium
Nitrate
Sulfate
EC
OC
Other
Average PM 2.5 concentration
31.6 g/m3
How well do they work?*
*Performance relies on quality of inputs. US has spent decades on emissions inventory development. Meteorological modeling also contributes significantly to errors
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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
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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
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What’s next?• Emissions-based air quality models work pretty well, how
might we use them:– Identify, quantitatively, how specific sources impact air quality.– Develop and test control strategies
• Decoupled direct method (implemented in CIT, URM, MAQSIP, CMAQ, CAMX)– Dunker: initial applications– Yang et al.: large scale application, comp. efficient (CIT, URM)– Hakami et al. ,Cohan et al: Higher order, with applications
(MAQSIP, CMAQ)– Napelenok eet al., : PM
• Control strategy assessment– Least cost approach to attainment for Macon, GA (Cohan et al.)
• Assessing impacts of individual sources• Area of Influence analysis (AOI) (similar information as
developing the adjoint)– Or AOPI (potential influence)
• Application to health assessment
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Emissions reductions lead to about a 12 ppb ozone reduction:Atlanta and Macon do not attain ozone standard (Macon by 6ppb)
Example Results : Impact of Planned Controls: 2000 vs. 2007
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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 emissions, Sij are the sensitivities/responses to emission changes, e.g.., the sensitivity of ozone to Atlanta NOx emissions
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• Define first order sensitivities as
• Take derivatives of
• Solve sensitivity equations simultaneously
jiij ECS /)1(
Sensitivity Analysis with Decoupled Direct Method (DDM):
The Power of the Derivative
iiiii ERCC
t
C K u
)()(
Advection Diffusion Chemistry Emissions
ijijijij
ESSt
Sij )( )(
JSKu
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3-DAir
QualityModel
NOo
NO2o
VOCio
...TKu, v, wEi
ki
BCi
...
O3(t,x,y,z)NO(t,x,y,z)NO2(t,x,y,z)VOCi(t,x,y,z)...
DDM-3DSensitivityAnalysis
s (t)c (t)
piji
j
DDM-3D
J
decoupled
R
ki
j
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DDM compared to Brute Force
Emissions of SO2
Sul
fate
j
iij
CS
EB EA
CB
CA
bA
BAij
CCS
C
E
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Consistency of first-order sensitivities
Brute Force (20% change) DDM-3D
R2 > 0.99Low bias & error
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Advantages of DDM-3D
• Computes sensitivities of all modeled species to many different parameters in one simulation– Can “tell” model to give sensitivities to 10s of
parameters in the same run
• Captures small perturbations in input parameters– Strangely wonderful
• Avoids numerical errors sometimes present in sensitivities calculated with Brute Force
• Lowers the requirement for computational resources
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Evidence of Numerical Errors in
BF
NH4 sensitivity to domain-wide SO2 reductions
NOx reductions at a point
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Efficiency of DDM-3D
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Control Strategy Development
• Macon out of attainment by 6 ppb in 2007
• Want to identify least cost control strategy
• Process:– Identify possible controls and
costs ($/ton of VOC or NOx)– Simulate response to controls
([O3]/ton VOC or NOx)– Calculate control
effectiveness([O3]/$)– Choose most effective controls
until get 6 ppb– Test strategy
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Sources of Sources of Macon’s Macon’s ozoneozone
Macon Scherer
Atlanta Branch
8-hr ozone, Aug. 17, 2000(2007 emissions)
MM
AA
SS BB
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Sensitivity of 8-hr Ozone in Macon
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0.030
12-Aug 13-Aug 14-Aug 15-Aug 16-Aug 17-Aug 18-Aug 19-Aug
Atlanta NOX
Branch
Scherer
Macon Non-mobile
Macon Mobile
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0
150
300
450
600
750
PlantScherer
Rest ofMacon
"MaconBuffer"
Atlanta(20 cnty)
PlantBranch
Rest ofGA
Yea
r 20
07 N
Ox
Em
issi
on
s (t
pd
) Area
Point
Non-Road
On-Road
NOx emission rates(tpd)
Macon ozone sensitivity (ppt/tpd)
2007 Emissions 2007 Emissions and Sensitivitiesand Sensitivities
0
50
100
150
200
250
300
PlantScherer
Rest ofMacon
MaconBuffer
Atlanta(20 cnty)
PlantBranch
Rest ofGA
S(1
) Mac
on
8-h
r o
zon
e (p
pt/
tpd
)
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Source-Receptor Response
Marginal Abatement Costs by Region
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
0% 10% 20% 30% 40%
Percentage emission reduction in Bibb County
Ma
rgin
al c
ost p
er to
n (Y
ear
20
00$) NOx
VOC
Cost-optimizationCost-optimizationChoose options with
leastmarginal $/impact until: (1) attain a.q. goal, or (2) reach budget
constraint
Cost
Impa
ct
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Strategies for Macon attainment (need 6.5 ppb)
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
0 2 4 6 8 10
Reduction in 8-hour ozone near Macon monitor (ppb)
An
nu
al C
os
t (Y
ea
r 2
00
0$
, in
mill
ion
s)
Macon only
All Georgia
Key Measures• Zero-cost options (PRB coal, burning ban, ...): 1.72 ppb, $0
• Bibb industrial NOx: 0.82 ppb, $2.6 million
• Locomotive controls: 0.77 ppb, $7.3 million
• SCRs at Scherer: 1.63 ppb, $20.9 million
• Vehicle I&M in Bibb: 0.25 ppb, $4.9 million
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Provide a technique to evaluate the impacts from a single large emissions source on regional air quality, incorporating non-linear processes and multi-day effects in estimating pollutant responses to relatively small emissions perturbations.
Single-Source Impact Analysis (Bergin et al.)
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Motivation and Application
• The ability to evaluate regional secondary pollution impacts from large single sources would provide a valuable tool for more effective air quality management practices, such as refining programs (e.g. emissions trading, regional planning), and supporting more effective compliance enforcement.
• Typical modeling approach (removing the emissions from a single source) has numerical errors.
• Court case led to need to assess impact of a single power plant (Sammis) in Ohio on downwind areas (a distance of up to about 1000 km)
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Average Day Elevated NOx Emissions
W. H. Sammis Power Plant(court estimated emissions)
0
500
1000
1500
2000
2500
May-95 Jul-95 Aug-00 OhioElevated EGU
Jul-95Model Inventory
NO
x E
mis
sio
ns
(avg
to
ns/
day
)
excess
allowable
Court Estimated from W.H. Sammis Plant
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ApproachTwo air quality models and grids, three ozone episodes, and three sensitivity techniques (brute-force, DDM, higher order DDM)
CMAQ, 36x36 kmAug. 12-20, 20002-ord. DDM
URM, multiscale from 24x24 km2
July 11-19 and May 24-29, 1995DDM
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Maximum increase in 1-hr avg O3
Comparison of the maximum increase in hourly-averaged ozone concentrations due to excess NOx emissions from the Sammis plant.
(a) July 11-19, 1995 (b) May 24-29, 1995 (c) August 12-20, 2000
URM with DDMCMAQ with 2nd order DDM
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When O3 > 0.060 ppm
1-hr O3 cell responses to excess emissionsAll hours
Max. increases
Max.decreases
maximum = 2.2 maximum = 2.2
minimum = -3.6 minimum = -1.2
CMAQ, 2nd ord DDM, August
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Conclusions
• Single-source simulation results agree with past field experiments, indicating that appropriate modeling techniques are available for quantifying single-source regional air quality impacts.
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Air Quality Models and Health Impact Assessment
• (How) Can we use “air quality models” to help identify associations between ozone PM sources and health impacts?– Species vs. sources– Very different than
for traditional air quality management
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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
Association
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Epidemiologic Analysis
log{E(CVD)} = + [PM2.5] + covariate terms
Covariates: time trend (mo. knots), day-of-week, holidays, hospital entry/exit, temperature, dew point
70
35
0
140
70
01998 2001 2004 1998 2001 2004
Exposure: daily PM2.5 (g/m3); lag 0, 1, 2 Outcome: daily ED visit counts for CVD
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Association between CVD Visits and Air Quality
(Tolbert et al., 2004)
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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
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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
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Source Impacts on Air Quality(Nov 1998 – Aug 2000)
CMB Source Impacts
8%
9%
2%
6%
1%
38%
3%
9%
19%
5%
LDGV
HDDV
SDUST
BURN
CFPP
AMSULF
AMBSLF
AMNITR
Other OM
UnSpec
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Power-plant derived SO4-2
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
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Diesel Elemental Carbon Particulate Matter
0.0
1.0
2.0
3.0
4.0
5.0
6.0
Au
g-9
9
Se
p-9
9
Oct
-99
No
v-9
9
De
c-9
9
Jan
-00
Fe
b-0
0
Ma
r-0
0
Ap
r-0
0
Ma
y-0
0
Jun
-00
Jul-
00
Au
g-0
0
Se
p-0
0
Oct
-00
No
v-0
0
De
c-0
0
Jan
-01
Fe
b-0
1
Ma
r-0
1
Ap
r-0
1
Ma
y-0
1
Jun
-01
Jul-
01
Au
g-0
1
ug
/m3
CMAQ36km JST FTM TU SD
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All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)
0.90
0.95
1.00
1.05
1.10
Wood - PMF Wood -CMB-LGO
K OC PM2.5 Wood - PMF Wood -CMB-LGO
K OC PM2.5
Source-specific RRs: Wood burning
RR
RR
95% CI
All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)
RR significant if CI does not cross unity (RR=1.0)
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All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)
0.90
0.95
1.00
1.05
1.10
Wood - PMF Wood -CMB-LGO
K OC PM2.5 Wood - PMF Wood -CMB-LGO
K OC PM2.5
Source-specific RRs: Wood burning
RR
All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)
Wood-PMF
K
Wood-CMB-LGO
OC PM2.5
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All respiratory (262 daily ED visits) Upper Respiratory Infection (161 daily ED visits)
0.90
0.95
1.00
1.05
1.10
Diesel
- PM
F
Diesel
- CM
B-LGO
Gasoli
ne -
PMF
Gasoli
ne -
CMB-L
GO
Mob
ile -
PMF
Mob
ile -
CMB-L
GO Zn FeEC
PM2.
5CO
Diesel
- PM
F
Diesel
- CM
B-LGO
Gasoli
ne -
PMF
Gasoli
ne -
CMB-L
GO
Mob
ile -
PMF
Mob
ile -
CMB-L
GO Zn FeEC
PM2.
5CO
Source-specific RRs: Mobile sources
All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)
Diesel-PMF,CMB-LGO
Mobile-PMF
PM2.5CO
EC
Fe
Gas-PMF
CO
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Source-specific RRs: Soil dust
All resp. (263 ED visits) Asthma/Wheeze (54 ED visits) All CVD (86 ED visits)
0.90
0.95
1.00
1.05
1.10
Soil -PMF
Soil -CMB-LGO
Si CoarsePM
PM2.5 Soil -PMF
Soil -CMB-LGO
Si CoarsePM
PM2.5 Soil -PMF
Soil -CMB-LGO
Si CoarsePM
PM2.5
Soil- CMB-LGO
All resp. (263 ED visits) All CVD (86 ED visits)Asthma (54 ED visits)
Soil-PMF
Si
Soil- CMB-LGO
PM2.5
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Source-specific RRs: “Other” OC
0.90
0.95
1.00
1.05
1.10
1.15
1.20
Other
OC OC
PM2.
5
Gasoli
ne -
PMF
Other
OC OC
PM2.
5
Gasoli
ne -
PMF
Other
OC OC
PM2.
5
Gasoli
ne -
PMF
Other
OC OC
PM2.
5
Gasoli
ne -
PMF
Other
OC OC
PM2.
5
Gasoli
ne -
PMF
Other
OC OC
PM2.
5
Gasoli
ne -
PMF
Asthma/ Wheeze (54)
COPD (13) URI (161) Pneumonia(34)
All respiratory(263)
All CVD (86)
“Other” OC
“Other” OC
“Other” OC
“Other” OC
“Oth
er”
OC OC
PM 2.5
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Summary• Air quality models provide powerful tools to link how
energy conversion and utilization impact air quality, health and the environment.– Emissions-based (First principles)– Receptor (statistical) models
• Advanced techniques provide means to efficiently assess impacts from individual sources and non-linear interactions– DDM
• Application of PM Source apportionment models in health studies more demanding than traditional modeling– Provide additional power and insight to identifying which
sources impact health, not just which species• Particularly important for organic carbon that comes from many
sources
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Thanks… Questions?