Receptor Modeling Source
Apportionment for Air Quality
Management
John G. Watson (john.watson@dri.edu)
Judith C. Chow
Desert Research Institute
Reno, Nevada, USA
Presented at:
The Workshop on Air Quality Management,
Measurement, Modeling, and Health Effects
University of Zagreb, Zagreb, Croatia
24 May 2007
Objectives
• Review receptor models and data
requirements
• Summarize prior uses of receptor
models in air quality
management
• Describe strategies for separating
primary and secondary source
contributions
The First Receptor Model
What you can see or smell
Black Carbon (BC) Remains
at Mesa Verde National Park,
Colorado, USA
• Not all BC is from diesel and other vehicular
emissions
• “Marker” is a better term than “tracer”
• There’s something of everything in everything
Source and Receptor Models
The source model uses source emissions as inputs and calculates
ambient concentrations. The receptor model uses ambient
concentrations as inputs and calculates source contributions.
(From Watson, 1979.)
Lagrangian Source Model
Cikl = ΣjΣmΣn TijklmnDklnFijQjkmn
CALCULATED
AT RECEPTOR
CALCULATED
BY CHEMICAL
MODEL
CALCULATED
BY MET MODEL
MEASURED
AT SOURCE
(INVENTORY)
CMB Receptor Model
Cikl = ΣjTijklFijΣmΣn DklnQjkmn
MEASURED
AT RECEPTOR
MEASURED
AT SOURCE
(T=1 OR
ESTIMATED BY
OTHER METHOD
Sijkl, SOURCE
CONTRIBUTION
ESTIMATE
Chemical Mass Balance
Equation:
Ci 
Input:
J
F
j1
ij
Sj
for i  1 to N
• Ambient concentrations (Ci)
and uncertainties (sCj),
source profiles (Fij),
and uncertainties (sFij).
Output:
• Source contributions (Sj)
and uncertainties (sSj).
Measurements:
• Size-classified mass, elements, ions, and carbon
concentrations on both ambient and source samples.
CMB Solutions
Minimize differences between calculated and measured values
for overdetermined set of equations
ϰ2 = minΣi [(Ci-Ci)2/ϭCi2] + ΣiΣi [(Fij-Fij)2/ϭFij2 ]
Britt and Luecke, (1973), single sample, bold=true value
ϰ2 =minΣi [(Ci-ΣjFijSj)2/(ϭCi2+ΣjϭFij2Sj2)]
Effective Variance, Watson et al., (1984), single sample
ϰ2 =minΣi [(Ci-ΣjFijSj)2/ϭCi2)]
Ordinary Weighted Least Squares, Friedlander (1973), single
sample
Other CMB Solutions
Sj=Ci/Fij
Tracer solution, Hidy and Friedlander (1971), Winchester and
Nifong (1971), single sample
ϰ2 =minΣk [(Massk-ΣiCik/Fii)2]
Multiple Linear Regression, Kleinman et al (1980), multiple
samples
ϰ2 =minΣi Σk [(Cik-ΣjFijSjk)2/ϭCik2)]
Positive Matrix Factorization, Paatero (1997), multiple samples
Receptor Models are Not Statistical
• They don’t test hypotheses or determine statistical
significance
• Receptor models should be physically based with
statements of simplifying assumptions and evaluation
of deviations from assumptions
• They infer mechanisms and interactions rather than
explicitly calculate them
• Receptor models recognize and elucidate patterns in
measured components, space and time that bound
the types, quantities, and locations of source
contributions
• Some of them explicitly use input data uncertainties
to weight influence of inputs and estimate
uncertainties of outputs
Types of “Modern” Receptor Models
• Chemical Mass Balance  CMB with
various solutions including marker (trace
method, effective variance (EV), principal
component analysis (PCA), UNMIX, abd
positive matrix factorization (PMF) solutions
• Aerosol Evolution and Equilibrium 
Estimates how reduction in one precursor
will affect PM end-products
• Back Trajectory  estimates source areas
for different pollutants or source
contributions
Chemical Mass Balance
Equation:
Ci 
Input:
J
F
j1
ij
Sj
for i  1 to N
• Ambient concentrations (Ci)
and uncertainties (sCj),
source profiles (Fij),
and uncertainties (sFij).
Output:
• Source contributions (Sj)
and uncertainties (sSj).
Measurements:
• Size-classified mass, elements, ions, and carbon
concentrations on both ambient and source samples.
Receptor Measurements from
Ambient Samplers
Airmetrics portable
MiniVol sampler
BGI FRM Omni
PM2.5 and PM10
PM1, PM2.5, and PM10
Source profiles from source testing
Many contributors not inventoried
Real-World Cooking
Simulated Cooking
More source profiles could be
obtained from certification tests
Roadside compliance test in India
Material balance says much about sources
(Mexico City, Feb/Mar 1997) (Chow et al., 2002)
Commonly measured elements, ions, and carbon (Zielinska et al., 1998)
10
1
0.1
0.01
Percent of PM2.5 Mass
10
1
0.1
0.01
Percent of PM2.5 Mass
a) Fugitive Dust
c) Gas Veh. Exhaust
Average Abundance
Average Abundance
Ions, Carbon Fractions, Elements, and Inorganic Gases
C
hl
o
So
N rid
lu A S itra e
b
O le mm ulfate
rg P o o t
a t n e
Bl nic assium
a c C iu
k ar m
C bo
a
M S rbon
ag od n
Al nesium
u m iu
Ph inum
os Sili m
ph c o
or n
C S us
Po hl ulfu
ta orin r
C ssiu e
a
T lc m
Va itanium
C na iu
M hro dium
an m m
g a iu
ne m
se
N Iron
i
C ck
op el
pe
Ar Zin r
Se se c
l n
B eni ic
R romum
u
St bid ine
Zi ron ium
rc tiu
C
on m
O arb Me ium
xi on
de m rcu
s o L ry
Su of no ead
lfu nitr xid
r d og e
io en
xi
de
C
hl
o
So
N rid
lu A S itra e
bl m u t
O e m lfa e
rg P o o t
a t n e
Bl nic assium
a c C iu
k ar m
C bo
a
M S rbon
ag od n
n
Al esium
u m iu
Ph inum
os Sili m
ph c o
or n
C Sul us
Po hl fu
ta orin r
C ssiu e
a
T lc m
Va itanium
C na iu
M hro dium
an m m
g a iu
ne m
se
N Iro
C i ck n
op el
pe
A Z r
Se rseinc
l n
B eni ic
R romum
u
St bid ine
Zi ron ium
rc tiu
C
on m
O arb Me ium
xi on
de m rcu
s o L ry
Su of no ead
lfu nitr xid
r d og e
io en
xi
de
Percent of PM2.5 Mass
1000
100
1000
100
C
hl
o
So
N rid
lu A S itra e
bl m u t
O e m lfa e
rg P o o t
a t n e
Bl nic assium
ac C iu
k ar m
C bo
a
M S rbon
ag od n
n
Al esium
um iu
Ph inum
os Sili m
ph c o
or n
C Sul us
Po hl fu
ta orin r
C ssiu e
a
T lc m
Va itanium
C na iu
M hro dium
an m m
ga iu
ne m
se
N Iro
C ick n
op el
pe
A Z r
Se rseinc
l n
B eni ic
R romum
u
St bid ine
Zi ron ium
rc tiu
C
on m
O arb Me ium
xi on
de m rcu
s o L ry
Su of no ead
lfu nitr xid
r d og e
io en
xi
de
C
hl
o
So
N rid
lu A S itra e
b
O le mm ulfate
rg P o o t
a t n e
Bl nic assium
ac C iu
k ar m
C bo
a
M S rbon
ag od n
Al nesium
um iu
Ph inum
os Sili m
ph c o
or n
C S us
Po hl ulfu
ta orin r
C ssiu e
a
T lc m
Va itanium
C na iu
M hro dium
an m m
ga iu
ne m
se
N Iron
i
C ck
op el
pe
Ar Zin r
Se se c
l n
B eni ic
R romum
u
St bid ine
Zi ron ium
rc tiu
C
on m
O arb Me ium
xi on
de m rcu
s o L ry
Su of no ead
lfu nitr xid
r d og e
io en
xi
de
Percent of PM2.5 Mass
More specificity obtained with source profiles
Variability
1000
100
b) Coal-Fired Boiler
0.001
0.001
0.0001
0.0001
Ions, Carbon Fractions, Elements, and Inorganic Gases
Variability
d) Hardwood Burning
0.001
0.001
0.0001
0.0001
Average Abundance
Average Abundance
Variability
7200±1400
10
1
0.1
0.01
Ions, Carbon Fractions, Elements, and Inorganic Gases
1000
100
Variability
10
1
0.1
0.01
Ions, Carbon Fractions, Elements, and Inorganic Gases
PM2.5 Mass Fraction
PM2.5 Mass Fraction
PM2.5 Mass Fraction
PM2.5 Mass Fraction
0.1
10
1
10
1
10
1
1
0.1
ClNO3SO4=
NH4+
Na+
K+
OC1
OC2
OC3
OC4
OP
OC
EC1
EC2
EC3
EC
TC
Al
Si
P
S
Cl
K
Ca
Ti
Mn
Fe
Cu
Zn
As
Se
Br
Rb
Sr
Pb
Retene
Indeno[123Benzo(ghi)p
Coronene
ster35
ster45
ster48
ster49
hop17
hop19
hop24
hop26
4_allyl_guaia
levoglucosan
palmitoleic
palmitic acid
oleic acid
stearic acid
cholesterol
phthalic acid
Norfarnesan
Farnesane
Norpristane
Pristane
Phytane
(Chow et al. 2006)
PM2.5 Mass Fraction
Many toxic
elements
have been
removed
from
emissions.
Organic
markers
take their
place
1
PVRD
UNC
0.01
0.001
0.0001
GAS
UNC
0.1
0.01
0.001
0.0001
DIESEL
UNC
0.1
0.01
0.001
0.0001
COOK
UNC
0.1
0.01
0.001
0.0001
10
BURNING
UNC
0.01
0.0001
0.001
Carbon fractions have been found useful and can
be obtained from existing samples
(Watson et al., 1994)
Gasoline-fueled vehicles
Diesel-fueled vehicles
Thermally-evolved material can be separated by
chromatography and mass spectrometry
Challenge is to extract information that separates sources
Gasoline
Coal power plant
Diesel
Roadside dust
Examples of U.S. CMB Model Air
Quality Findings and Results
• Oregon wood stove emissions standard (Watson,
1979)
• Midwest contributions to east coast sulfate and
ozone (Wolff et al., 1977, Lioy et al., 1980, Mueller
et al., 1983, Rahn and Lowenthal, 1984)
• Washoe County, Nevada, stove changeout, burning
ban, and “squealer” number (Chow et al., 1989)
• California EMFAC emissions model revisions (Fujita
et al., 1992, 1994)
• SCAQMD (Los Angeles) grilling emission standard
(Rogge, 1993)
• SCAQMD (Los Angeles) street sweeper specification
(Chow et al., 1990)
Examples of U.S. CMB Model Air Quality
Findings and Results (continued)
• SCAQMD (Los Angeles) Chino dairy reduction (NH3)
regulation (SCAQMD, 1996)
• PM10 SIP implementation of wood burning, road
dust, and industrial emission reductions (Davis and
Maughan, 1984, Houck et al., 1981, 1982, Cooper
et al., 1988, 1989)
• Navajo Generating Station SO2 scrubbers (Malm et
al., 1989)
• Hayden Generating Station SO2 scrubbers (Watson
et al., 1996)
• Mohave Generating Station shutdown (Pitchford et
al., 1999)
• Denver Colorado urban visibility standard (Watson
et al., 1988)
0% 0%
11%
19%
Czech Republic PM2.5, winter '93
Worldwide PM Source
Contribution Estimates by
Chianjen, Taiwan, PM2.5, Feb/Mar '99
10%
23%
19%
3%
3%
0%
0% 3%
0%
0%
4%
18%
Chemical Mass Balance
(Chow and Watson, 2002)
0%
0%
24%
22%
Toronto PM2.5, Summer '98
0%0%
19%
0%
0%
40%
11%
Industry
Transportation
Vegetative Burning (RWC)
Geological
Marine aerosol/Sea salt
Sulfate/Secondary ammonium sulfate
Secondary ammonium nitrate
Secondary organics
Other/Unidentified
4%
63%
17%
Sulfate/Secondary ammonium sulfate
Secondary ammonium nitrate
Secondary organics
Other/Unidentified 2.5
Sihwa, Korea, PM , 1998-99
Measured PM2.5 mass = 12.4 µg/m3
17%
PM2.5 mass = 48.2 µg/m3
3
PM2.5 mass = 51.1±2.8 µg/m
Industry
Transportation
Vegetative burning (RWC)
Geological
Marine aerosol/Sea salt
Sulfate/Secondary ammonium sulfate
Secondary
ammonium nitrate
Industry
Transportation
Secondary
organics
Vegetative
Burning (RWC)
Geological
Other/Unidentified
Marine aerosol/Sea salt
6%
9%
29%
11%
3%
0%
0%
4%
Industry
Transportation
Vegetative burning (RWC)
Geological
Marine aerosol/Sea salt
Sulfate/Secondary ammonium sulfate
Secondary ammonium nitrate
Secondary organics
Other/Unidentified
0%
6%
1%
16%
22%
63%
PM2.5 mass = 35.6±2.7 µg/m3
PM2.5 mass = 12.4 µg/m3
Downtown Los Angeles PM10, 1995
Mexico City PM2.5, 1989-90
5% 0%0%
8% 0% 4%
10%
13%
0%
20%
36%
4%
PM10 mass = 48.1±3.1 µg/m3
0%3%
10%1%
1% 0%
15%
19%
0%
14%
0%
0%
0%
24%
11%
Antarctica PM10, 1995-97
South Africa PM2.5, winter '97
Industry
Transportation
Vegetative burning (RWC)
Geological
Marine aerosol/Sea salt
Sulfate/Secondary ammonium sulfate
Secondary ammonium nitrate
Secondary organics
Other/Unidentified
0%
1%
6%
0%
1%
Industry
Transportation
Vegetative burning (RWC)
Geological
Marine aerosol/Sea salt
Sulfate/Secondary ammonium sulfate
Secondary ammonium nitrate
Secondary organics
Other/Unidentified
Industry
Transportation
Vegetative Burning (RWC)
Geological
Marine aerosol/Sea salt
Sulfate/Secondary ammonium sulfate
Secondary ammonium nitrate
Secondary organics
Other/Unidentified
72%
50%
PM2.5 mass = 118.9 µg/m3
15%
PM2.5 mass = 126 µg/m3
57%
PM10 mass = 3.4±0.2 µg/m3
Receptor Model Results Need to be
Challenged
CMB Sensitivity Test
c
c
c
Case
PVRD
GAS
DIES
1a
2b
3a
4b
5a
6b
7a
8b
0
0
0
0.04±0.3
0
0
0
0
1.9±1.3
0
2.2±1.4
0
1.0±0.9
0
2.4±1.4
30±7
6.6±2.2
7.1±2.3
7.6±2.2
8.5±2.2
3.0±1.6
3.2±1.6
8.2±2.4
0
Source Contribution Estimates (µg/m3) by Source Type
BURN-Hc
BURN-Sc COOKc MARINEc AMSULc
16±3
15±3
18±2
17±2
19±3
18±2
5.8±6.2
7.0±6.4
37±3
36±3
10±6
0
20±5
23±6
21±6
25±6
23±5
24±6
-
0
0
0
0
0.49±0.12
0.49±0.12
0
0.05±0.20
1.1±0.4
1.3±0.3
1.1±0.4
1.3±0.4
1.3±0.3
1.4±0.3
1.0±0.4
0
AMNITc
PMASSd
R2e
CHIf
18±2
18±2
18±2
18±2
18±2
18±2
18±2
17±2
92
94
89
91
110
109
77
85
0.96
0.98
0.96
0.97
0.88
0.91
0.92
0.97
0.6
0.7
0.6
0.7
3.0
4.1
1.2
0.4
a
With organics.
Without organics.
c
Source Types.
d
Percent mass explained by the model run
e
R-square
f
Chi-Square
b
(Chow et al. 2006)
CMB Pseudo-Inverse Normalized (MPIN)
(Chow et al. 2006)
Matrix
Species
Code/Nameb
NO3SO4=
NH4+
Na+
K+
OC3
OC4
OC
EC2
EC3
EC
Al
Si
Cl
K
Fe
Se
Br
Pb
Indeno[123-cd]pyrene (INCDPY)
Benzo(ghi)perylene (BGHIPE)
Coronene (CORONE)
17α(H), 21β(H)-29-Hopane (HOP19)
Levoglucosan (LEVGU)
Syringaldehyde (SYRALD)
Palmitoleic acid (PALOL)
Oleic acid (OLAC)
Cholesterol (CHOL)
Norfarnesane (NORFAR)
Farnesane (FARNES)
Norpristane (NORPRI)
Pristane (PRIST)
Phytane (PHYTAN)
GAS
0.00
0.00
0.00
-0.07
-0.04
-0.04
0.00
-0.07
0.06
0.01
-0.23
-0.09
0.55
0.03
-0.09
-0.19
0.00
0.13
0.03
0.54
0.93
1.00
0.57
0.05
0.08
-0.06
-0.02
-0.03
0.11
0.04
0.04
-0.02
-0.02
DIES
0.00
0.00
0.00
-0.06
0.00
0.02
0.04
-0.03
1.00
0.00
0.22
-0.07
-0.17
0.02
-0.07
-0.12
0.01
0.15
-0.01
-0.14
-0.16
-0.14
-0.02
0.06
0.10
0.00
-0.01
-0.02
0.07
0.11
0.23
0.01
0.18
BURN-H
0.01
0.00
-0.01
0.04
1.00
0.10
0.12
-0.01
0.37
-0.03
-0.51
-0.20
-0.27
0.21
0.58
-0.59
0.01
0.12
-0.01
0.00
0.09
0.13
0.09
0.50
0.73
-0.06
0.00
-0.05
0.06
0.06
0.10
-0.03
0.08
Source Code
BURN-S
COOK
-0.01
0.00
0.00
0.00
0.01
0.00
0.09
0.10
0.00
-0.30
-0.20
0.52
0.00
0.14
-0.10
1.00
-0.64
-0.15
0.03
0.03
0.80
-0.17
0.43
-0.10
0.44
-0.10
-0.12
0.01
0.23
-0.24
1.00
-0.14
-0.01
0.00
-0.16
-0.03
-0.03
0.09
0.06
-0.05
-0.16
-0.03
-0.23
-0.06
-0.15
-0.07
-0.25
-0.08
-0.38
-0.11
-0.19
0.49
-0.08
0.20
-0.07
0.22
-0.09
-0.02
-0.09
-0.02
-0.20
0.02
-0.06
0.16
-0.13
-0.02
AMSUL
-0.10
1.00
0.10
0.01
-0.06
0.00
-0.02
0.00
-0.16
0.00
-0.01
0.01
-0.08
-0.02
-0.03
0.03
0.00
-0.05
0.00
-0.08
-0.14
-0.15
-0.10
-0.04
-0.06
0.02
0.01
0.01
-0.03
-0.02
-0.04
0.01
-0.02
AMNIT
1.00
-0.18
0.92
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-0.01
0.00
0.00
0.00
0.00
0.00
0.00
-0.01
-0.01
-0.01
-0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
One Atmosphere (Gases and Particles) Also Works
for Receptor Models (Gertler et al., 1996)
Light Duty
Emission
Rates
Heavy Duty
Emission
Rates
Hourly (VOC) data provide temporal
corroboration of emissions and reveal
unknown sources
Unknown event
Morning traffic
200
150
100
50
Exhaust
Liq. Gasoline
Gasoline Vapor
Industrial
Biogenic
CNG
18
12
6
8/21- 0
18
12
6
8/20- 0
CDT
18
12
6
8/19- 0
18
12
6
8/18- 0
18
12
6
0
8/17- 0
Contributions (mg/m3)
(Houston, TX, 1993) (Lu, 1996)
Unexpl.
High Time Resolution is Desired
Spikes indicate local sources
(Watson and Chow, 2001)
MER BC
PED BC
MER WS
MER WD
360
15
270
10
180
5
90
0
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour (CST)
Wind Direction (WD) (deg from N)
Black Carbon (BC) (µg/m3)
& Wind Speed (WS) (m/s)
b) Monday 3/10/97
20
Wind Direction is Suggestive for Local
Sources
Conditional
Probability
Function (CPF)
for a Selenium
Factor at the
Pittsburg
Supersite
(Pekney et al., 2006)
These must be associated
with measured source
profiles
(Chen et al., 2006)
3
Source factors
derived from
ambient data by
UNMIX and
PMF
High PM2.5 Period Average Contribution (g/m )
10
1
0.1
0.01
1E-3
10
1
0.1
0.01
1E-3
10
1
0.1
0.01
1E-3
10
1
0.1
0.01
1E-3
10
1
0.1
0.01
1E-3
10
1
0.1
0.01
1E-3
10
1
0.1
0.01
1E-3
10
1
0.1
0.01
1E-3
Marine
Zinc
Resuspended Dust
Agriculture
Vegetative Burning
Secondary Aerosol
Motor Vehicle
RWC
Pb
Cd
Sr
Br
Se
As
Zn
Ni
Fe
Mn
Cr
Va
Ca
K
Si
Al
TC
EC
EC3
EC2
EC1
OC
POC
OC4
OC3
OC2
OC1
K+
Na+
T-NH3
NH4+
SO4=
NO3ClPM2.5
Markers for Biogenic SOA
(Pandis, 2001)
• Pinic acid, pinonic acid, norpinic acid, and
norpinonic acid are products of the oxidation of
most monoterpenes
• There are some (apparently) unique tracers:
• Hydropinonaldehydes for α-pinene
• Nopinone for β-pinene
• 3-caric acid for carene
• Sabinic acid for sabenene
• Several of these compounds measured in field
studies in forests (usually a few nanograms per
cubic meter, sometimes as much as 0.1 µg m-3)
SO4=/SO2 Ratio changes during Aerosol Aging (and
should be Reflected in Source Profiles)
(Watson et al., 2002)
Back trajectories indicate source regions
(Xu et al., 2006)
Regression parameters for Grand Canyon National Park
(2000–2002). Percent of time the parcel is in a horizontal grid
cell based on back trajectories starting at 500 m.
Receptor Models Can Estimate the Future
in Some Circumstances
200%
200%
180%
180%
Fractional Change in Particle Nitrate Concentration
Fractional Change in Particle Nitrate Concentration
(Denver, CO, 1997) (Watson et al., 1998)
160%
140%
120%
100%
80%
60%
40%
20%
0%
160%
140%
120%
100%
80%
60%
40%
20%
0%
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
Fractional Change of Particle Plus Gas Ammonia Concentrations
Effect of ammonia
reductions on ammonium
nitrate particles
200%
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
Fractional Change of Particle Plus Gas Nitrate Concentrations
Effect of nitric acid
reductions on ammonium
nitrate particles
200%
Emission Reduction Effectiveness
Long-Term Trends in SO2 Emissions and SO4= Levels
(Malm et al., 2002)
Murphy’s Law of Reproducibility
“If reproducibility is a problem, just use one model”
Mohave Generating Station contributions to Meadview sulfate
(Pitchford et al., 1999)
1,000
MCMB
TMBR
HAZEPUFF
CALPUFF-Dry
3
Sulfate Concentration (ng/m )
b)
800
DMBR
TAGIT
ROME
CALPUFF-Clouds
600
400
200
0
8/5
8/6
8/7
8/8
8/9
8/10 8/11 8/12 8/13 8/14 8/15
Day in 1992 (Samples begin at 0700 and 1700 MDT)
8/16
Po
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-m
Source Contribution (%)
Model discrepancies help to improve inventories
PM2.5 Inventory/Receptor Model Comparison, Denver, CO (1997)
(Watson et al., 2002)
50
Emissions Inventory
40
Receptor Model, %Total
Receptor Model, %Primary
30
20
10
0
Source Category
SIP Guidance “Weight of Evidence” Approach
(EPA, 2001)
• Form a conceptual model of the emissions,
meteorology, and chemical transformations
that are likely to affect exceedances
• Develop a modeling/data analysis protocol
with stakeholders consistent with available
science, measurements, and the
conceptual model
• Construct and evaluate emission inventory
for the domain as indicated by the
conceptual model
SIP Guidance “Weight of Evidence” Approach (continued)
• Assemble and evaluate
meteorological measurements for
the domain
• Apply source and receptor models
and to determine contributions
• Apply diagnostic tests and justify
discarding results that are not
physically reasonable
SIP Guidance “Weight of Evidence” Approach (continued)
• Modify the inventory to reflect different
emission reduction strategies in consultation
with stakeholders, and evaluate the effects of
reductions at receptors
• Make models, input data, and results
available to others for external review
• Judge the weight of evidence supporting or
opposing the selected emission reduction
strategy prior to implementation
Receptor Model Needs:
A Summary
• Source properties that identify and quantify
source contributions at a receptor
(Daisey et al., 1986, Gordon et al., 1984)
• Better designed networks (Chow et al., 2002,
Demerjian, 2000) with respect to
•
•
•
•
•
•
•
Sampling locations
Sampling periods
Sample durations
Particle sizes
Precursor gases
Chemical and physical components
Meteorology
Receptor Model Needs (continued)
• Emissions profiles (with cooling and
dilution including marker species and
gases, (England et al., 2000)
• More convenient availability and
documentation of source profile and
ambient data (U.S. EPA, 1999)
• More evaluation, validation, and
reconciliation of receptor and source
modeling results (Javitz et al., 1988)
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