Transcript NPS Source Attribution Modeling

NPS Source Attribution Modeling
Deterministic Models
Dispersion or deterministic models
Receptor Models
Analysis of Spatial & Temporal Patterns
Back Trajectory Analyses
Statistical/Regression Based
“Deterministic” or “Source-based”
Models
Meteorology
Emissions
Model(s)
Chemistry
Problems:
• Lack of input data
• Expense
Predictions of
concentrations
and source
attributions at a
receptor of
interest
Receptor Models
Measured Data at
and/or near a
receptor
Sometimes
Meteorology
and/or
Source
Characterization
Simple Statistical
Analysis
Qualitative
and/or
Quantitative
Source
Attribution
Problems:
• Assumptions of linear relationships
• Average results only.
Regression Techniques of Source Apportionment
Assumption: The concentration measured at the receptor is some linear
combination of the contributions of several sources
Concentration = a1 Source1 + a2 Source2 + …
Variations:
CMB (Chemical Mass Balance)
Use source profiles and concentrations of several species to predict
attributions for 1 measurement period.
TMBR (Tracer Mass Balance Regression)
Use measurements of tracer concentrations over time to predict an
average attribution of 1 traced source.
TrMB ( Trajectory Mass Balance)
Use many concentrations of 1 species and counts of trajectory endpoints
in source regions to predict average attributions over a long period.
Others...
Chemical Mass Balance (CMB) Model
All equations are for data at a single measurement period.
“Source” refers to a “source profile” (ratios of species emitted to total
mass emitted).
CAs = a1 Source1,As + a2,As Source2,As + …an Sourcen,As
CSe = a1 Source1,Se + a2 Source2,Se + …an Sourcen,Se
.
.
.
Cspecies m = a1 Source1,m + a2 Source2,m + …an Sourcen,m
Solutions for a1…an are found by solving the set of linear equations by
regression or other means.
Result is fractional amount of each species attributed to each source
profile for the single measurement period.
Secondary Particulates & CMB
• Assumption of CMB: Sources are linearly related to
concentrations.
• Solutions for secondary particles
• Obtain attributions for the primary fraction only
• Use secondary source profiles, i.e. AMSO4=1
• Adjust the source profiles based on other known
factors such as meteorology and simple chemistry.
CMB, PMF, and UNMIX
Similar models with a few differences…
• Source Profiles – In CMB, user determines number and
characterization of sources. PMF and UNMIX calculate
them for you. UNMIX has upper limit of 7.
• Solution Techniques – UNMIX uses factor analysis (SVD).
CMB and PMF use weighted regressions.
• Cost – PMF $400-700, CMB and UNMIX are free, though
UNMIX currently requires MATLAB.
• Averaging Time of Solution – CMB is per observation time,
PMF and UNMIX are averages over all times.
• Uncertainties – Explicitly included for each obs in
PMF and CMB, not in UNMIX.
Analyses of Spatial and Temporal
Patterns in Measured
Concentrations or Other Variables
4
2
0
ug/m^3
6
8
BRAVO Study Big Bend
24-hour Avg SO4
180
200
220
240
Jday 1999
260
280
300
Empirical Orthogonal Functions (EOFs)
What is it?
A method of finding common spatial & temporal
patterns in the data.
Why do it?
Simplifies many patterns into a few that
explain most of the variance in the data.
EOF patterns often qualitatively reveal
influential source areas.
Using certain assumptions,
quantitative average apportionment
can be made to these source areas
(SAFER Model).
When is it
used?
Often in special studies.
Occassionally for routine monitoring data.
Empirical Orthogonal Functions
Location
 S 11 S 12 ... S 1N  Time
 S 21 S 22 ... S 2 N 


.
. 
 .
 SM 1 SM 2 ... SMN 


Array of measured concentrations
Express this array as a product of two arrays.
Location






EOF
Location
 Time





Original Matrix
=






 EOF





Spatial Patterns
X






 Time





Time factors
Selenium Rotated EOF 2
Guadalupe Mountains
0
Ft. Lancaster
0
Ojinaga
0
Big Bend
0
Esmeralda
0
Everton Ranch
2
Amistad
0
2
1.5
2Colombia
2
2 1.5
Lake Corpus Christi
1
1
1
Villaldama
0
1
27% of the variance
Matrix: centered
Rotation: varimax 3 factors
0.5
Linares
0
-0.6 -0.2
0.2
0.6
Soto la Marina
0
260
265
270
Julian Day
275
280
285
Factor Analysis
What species vary similarly? Do they suggest source types?
Table 5. Factor loadings for data at Big Bend National Park. |Values| > 0.4 are shaded.
Factor 1
0.935
Factor 2
0.073
Factor 3
0.116
Factor 4
0.021
Fine Mass
0.898
0.077
-0.088
-0.098
EC
0.840
0.276
-0.063
-0.076
Se
0.835
0.015
-0.142
-0.087
BABS
0.698
0.557
-0.206
-0.097
Coarse Mass
0.693
0.086
-0.032
0.176
Na
0.653
0.192
0.084
0.461
OC
0.596
0.394
-0.418
-0.450
Si
0.408
0.762
-0.027
0.169
Br
0.378
0.417
-0.541
0.105
Ti
0.337
-0.095
-0.325
0.092
Zn
0.304
0.728
0.031
0.173
NO3
0.272
0.304
0.015
0.260
K
0.266
0.621
-0.466
-0.327
Ni
0.135
0.039
0.496
-0.271
Pb
0.133
0.793
0.178
0.090
Mn
0.127
0.165
0.245
0.058
Cl
0.119
-0.019
-0.305
-0.222
Cu
0.118
0.240
-0.166
0.603
Fe
0.065
0.902
-0.085
0.004
Ca
-0.007
0.964
0.094
0.049
As
-0.017
0.097
0.020
0.676
Cr
-0.022
-0.038
0.121
0.010
V
-0.041
0.194
0.659
-0.112
SO2
-0.111
0.647
0.088
0.167
Rb
-0.143
-0.251
0.203
-0.268
Sum of Squares
5.614
5.358
1.859
1.799
S
Example from Big Bend
1996 Scoping Study
Factor 1 - 38% of variance
S, Se, Na
(TX, marine, industry)
Factor 2 - 37% of variance
Soil elements, Zn, Pb,
SO2
(Power plant, smelter)
Factor 3 - 13% of variance
V, Ni & OC, K
(Oil & fires)
Factor 4 - 12% of variance
As, Cu (smelter)
Trajectory Analysis Methods
• Residence Time Type Analyses
•
•
•
•
Residence Time
Conditional Probability
Source Contribution Function
Average and Maximum Fields
• Trajectory Apportionment Model
• Quadrant Assignment
• Cluster Analysis
• Hit - No Hit
• Emissions Estimation
Upper Air Sites
in North
America used
by ATAD Model
Simple
Graphical
Back
Trajectory
Analysis
Big Bend
National
Park
July 9-11,
1983
Big Bend
National
Park
Nov. 19-22,
1983
Residence Time vs. Source Contribution Function
Big Bend Low Concentration Residence Time
Big Bend Low Concentration Source Contribution
Percent of Endpoints
Fraction of Normal
0 to 0.01
0.01 to 0.02
0.02 to 0.04
0.04 to 0.08
0.08 to 0.16
0.16 to 0.32
0.32 to 10
0 to 1
1 to 1.2
1.2 to 1.4
1.4 to 1.8
1.8 to 2
2 to 3
3 to 4
Jan 1, 1983 - Dec 31, 1998
For S < 324 ng/m3 (lowest 10%)
Jan 1, 1983 - Dec 31, 1998
For S < 324 ng/m3 (lowest 10%)
Conditional Probability...
If an air mass arrived from a given area, what is the
probability that the concentration met a given condition?
Big Bend High Concentration Conditional Probability
Probability of Condition
Probability of Condition
0 to 0.05
0.05 to 0.1
0.1 to 0.15
0.15 to 0.2
0.2 to 0.25
0.25 to 0.3
0.3 to 1
Big Bend Low Concentration Conditional Probability
0 to 0.05
0.05 to 0.1
0.1 to 0.15
0.15 to 0.2
0.2 to 0.25
0.25 to 0.3
0.3 to 1
Jan 1, 1983 - Dec 31, 1998
For S > 1098 ng/m3 (top 10%)
Includes All Months
Jan 1, 1983 - Dec 31, 1998
For S < 324 ng/m3 (Lowest 10%)
Includes All Months
Average and Maximum Fields
Examples for sulfur at Big Bend National Park, 1983 - 1998
Max Concentration
Mean Concentration
S ng/m^3
S ng/m^3
0 to 300
300 to 600
600 to 900
900 to 1200
1200 to 1500
1500 to 1800
1800 to 2000
0 to 300
300 to 600
600 to 900
900 to 1200
1200 to 1500
1500 to 1800
1800 to 3000
Jan 1, 1983 - Dec 31, 1998
Jan 1, 1983 - Dec 31, 1998
Includes All Months
Includes All Months
Quadrant Assignment
Apportionment to Quadrants
For a day
assigned to a
quadrant,
100% of the
measured
concentration
is
apportioned
to that
quadrant.
Country Assignment
Five Transport Patterns
1996 Scoping Study, Big Bend National Park
Transport Pattern 1 (Monterrey)
Transport Pattern 2 (Due S)
Transport Pattern 4 (From N)
Transport Pattern 5 (E TX)
Transport Pattern 3 (W/SW)
Boxplots of Concentrations by Transport Patterns Based on Clustered
Trajectories
1996 Scoping Study - Big Bend National Park
Big Bend Arsenic by Transport Pattern
0.0
0.5
500 1000
1.0
1.5
2.0
2000
2.5
Big Bend Sulfur by Transport Pattern
Monterrey Due S
W/SW
From N
E TX
W/SW
From N
E TX
Big Bend Vanadium by Transport Pattern
0
10
2
20
4
30
6
40
50
Big Bend Iron by Transport Pattern
Monterrey Due S
Monterrey Due S
W/SW
From N
E TX
Monterrey Due S
W/SW
From N
E TX
Trajectory Hit or No-Hit Analysis
Example from 1991 PREVENT Study, Mt.
Rainier National Park, Washington
Trajectory Mass Balance
J
Cit   Qijt Tijt Njt Eijt
j 1
C = concentration
Q = emission rate
T = transformation/deposition factor to account for
deposition, diffusion, and chemical conversion
N = trajectory endpoints
E = entrainment factor to account for coupling
between the transport layer and layer in which
pollutant is emitted
Subscripts:
i = species
j = source
t = time
Percent of sulfur apportioned to each source area by
trajectory mass balance, 1989 - 1991
Percent of organics apportioned to each source area by
trajectory mass balance, 1989 - 1991.
Going Backwards…
Estimation of Source Strength from Concentrations
Receptor Models in Our Past…
Peer-reviewed literature
Regulatory decisions
Education & Exhibits
Interpretation of Data
“Reality” checks on models
Etc……
Receptor Models in the Future
Here’s what we’re working on right now…
Evaluation of wind fields & trajectory models
Better Data Visualization
More sophisticated statistical models
User-friendliness
Keeping current with hardware, software, internet
Integration & reconciliation of models