Download Poirot_SA project

Source Apportionment Analysis of (Northeastern)
IMPROVE and CASTNET Data
Conducted by:
Battelle (Basil Coutant) with
Sonoma Tech. (Hillary Main)
Funded by:
(MARAMA for) MANE-VIEW and
Midwest Regional Planning Organization
Summarized here by:
Rich Poirot, VT DEC 4/19/02
Battelle SA Project
1. Background (Clarkson, Rutgers, VTDEC at
Underhill & Brigantine, with back trajectories)
2. Approach (16 NE IMPROVE & CASTNET sites,
Positive Matrix Factorization & UNMIX models)
3. Results (PMF results, SPECIATE comparison,
Surface Met, Watson Review, VT comparison)
4. Strengths/Weaknesses (ambitious, multi-site,
systematic, exploratory, application)
5. What’s Next? (Phase 2)
PMF (P. Paatero) and UNMIX (R. Henry) Required
Source Assumptions or Characteristics
• Constant, Unique Chemical Compositions &
• Varying, Unique Contributions at Receptor
And, Some Practical Considerations:
• Sources are “Virtual Sources” (at Receptor)
• Limited in Number - Typically < 10 - (Mixes)
• Constrained by “Resolving Power” of Data
• Influenced by Modeler Choice & Interpretation
• PMF allows (weighted) < MDL data, UNMIX Doesn’t
How UNMIX Works:
Fe and K are both components
of “soil” but correlate poorly
because of other “Non-Soil” K
Sources, but note the distinct
“Hard Edge” in the scatter plot.
UNMIX seeks & finds “hard
edges” in scatter plots (in NDimensional space) and these
define “Source Profiles” of
Constant Composition &
Varying Contribution.
Background (& Backspin): Comparative PMF & UNMIX
Analyses at Underhill, VT & Brigantine, NJ
Clarkson PMF (Polissar et al., 2001)
VTDEC UNMIX (Poirot et al., 2001)
(7 Common Sources in 2 Model Runs)
VU - VTDEC UNMIX (Poirot, 2001)
7 Common
Sources from
at least 4 of 5
Model Runs
{
CP - Clarkson PMF (Song et al., 2001)
RP - Rutgers PMF (Lee et al., 2002)
BP - Battelle PMF (Coutant, 2002)
BU - Battelle UNMIX (Coutant, 2002)
From Clarkson/ VTDEC study:
At Underhill VT, 7 of 11 PMF
Sources had Similar UNMIX
Counterparts - with highly
correlated mass contributions
& similar mass compositions.
VT UNMIX
Clarkson PMF
MW Coal Primary
0.52 (0.96)
MW Coal 2ndary
0.87 (0.94)
Residual Oil
0.87 (0.99)
Wood Smoke
0.82 (0.87)
Soil
1.58 (0.85)
Canadian MV
0.35 (0.93)
Canadian Smelter
1.06 (0.99)
Slopes & (Correlations - R) of similar sources
Trajectory Residence-Time Incremental Probability Fields for
Sources Identified by UNMIX & PMF Mathematical Models at
Underhill, VT(1989-95), but Not at Brigantine, NJ (1991-99)
Note: these 2 sources combined averaged < 5% of VT PM2.5 Mass
& their Impact in VT Declined by > 50% from 1989 through 1995
Unlike Other Identified Sources (at Underhill, VT),
the Two “Coal” Sources Come from the Same Region
So Why are there Two MW Coal Sources?
Battelle: 16 Sites (10 IMPROVE & 6 CASTNET) in
New England, Mid-Atlantic and Midwestern Regions
Screen Data, Apply PMF & UNMIX Models, Compare
Results,Compare with SPECIATE, Interpret “Sources”
Battelle SA Project: Design Considerations
• Very Ambitious – 2 Models, 16 Sites, 2 Networks,
Methods Changes, …
• Required Consistent, Systematic Data Screening,
Input Variable Selection, Model Options, Source
Interpretations, …
• Insufficient Resources (Time/Funds, etc.) for
additional meteorological interpretation
(Trajectories) – Phase 2…
• Interpretations by Battelle, STI, SPECIATE,
Locals (incomplete) are considered “Preliminary”
Example Battelle/STI Data QA Screening Results
Example Battelle/STI Data QA Screening Results,
So, Only 1996-98 CASTNET Data used
Flagged Data (obs. Not used as receptor model input)
(Could be useful QA Feedback to Data Generators…)
Source Strength Changes in a “Sodium Source”
(Sharp Drop after about 9/97)
IMPROVE-only
but Not All
IMPROVE?
Mostly UNMIX but
also PMF?
No Obvious
Spatial Pattern
& No obvious
data artifacts?
Is it “Real” or
Is It “Artifact”?
Aerosol Species used (& not used) as UNMIX Input
(Green Boxes indicate Species Not Used at Most sites)
SPECIATE Comparison
Was Ambiguous…
Source Profile/ SPECIATE Comparison
• Worked Great on PMF & UNMIX with artificial data
• Worked Poorly (at first) on these “real-world” results
• Improved somewhat with 2ndary species deleted, but
Reasons for / Lessons Learned
• UNMIX & PMF yield “virtual” not “emissions” profiles
• SPECIATE profiles mostly Western and Out-of-Date
• Need to Update Local/Regional Source Profiles
• Need better methods/tools for handling Secondaries
On-Site Met data
at Acadia, Great
Smoky Mountain,
Shenandoah, &
Mammoth Cave.
Some sources at
some sites show
strong directional
tendencies.
Residual oil combustion
Road salt
Sea salt
Secondary OC
Secondary sulfate
Smelter
Vegetative burning
Woodsmoke
Boundary
Waters
Brigantine
NWR
Dolly Sods
WA
Great Smoky
Mountains
NP
James River
Face WA
Lye Brook
WA
Mammoth
Cave NP
Shenandoah
NP
Washington
D.C.
73
203
112
796
556
510
502
752
177
199
399
651
226
155
405
193
328
267
200
231
394
935
190
330
142
474
619
53
686
289
71
Preliminary…
Probable Source
Crustal
Crustal limestone
Diesel
Fe mining
Incinerator
Industrial
Pulp mill contribution
Acadia NP
PMF results at IMPROVE sites (mean PM-2.5 in ng/m3)
393
663 1163 177
328 311
3936 2188 3482 4521 6629 3701 376 6100 3222 7765
2263 2354 5704 5042 4808 7197 3557 4932 4471 7543
457
341 184
729 708
2422 2998 3480 278
2164
Shading indicates “significant” (> 15%) bext impact on Worst
(20%) visibility days, where bext was apportioned by regression
37
492
7331
208
8124
318
1867
369
3919
6260
228
48
4213
5786
1956
6291
437
25
86
133
Quaker City
84
M.K.
Goddard
Connecticut
Hill
102
Livonia
Bondville
111
918
7891
41
2019
6216
272
5362
60
829
1513
5534
Preliminary…
Probable Source
Crustal
Crustal limestone
Diesel
Fe mining
Incinerator
Industrial
Pulp mill contribution
Residual oil combustion
Road salt
Sea salt
Secondary OC
Secondary sulfate
Smelter
Vegetative burning
Woodsmoke
Arendstville
PMF results at CASTNET sites (mean PM-2.5 in ng/m3)
Shading indicates “significant” (> 15%) bext impact on Worst
(20%) visibility days, where bext was apportioned by regression
Source Contributions to
Extinction derived by
regressing daily source
contributions vs.
reconstructed extinction.
Typically, multiple sources
are important on the best
visibility days,
While the “Secondary
Sulfate” source is dominant
on the Haziest days at
(nearly) All sites.
Didn’t we already
know this?
Source Trends from 9 IMPROVE sites,
(excluding Jeff/James which started 9/94):
• Largest (Secondary Sulfate) Source Decreasing at 7/9
(no significant change at Gt. Smoky Mtn. & Lye Brook,
• 2nd Largest (Secondary Organics) Source Unchanged
at 7/9 (Increasing at Shenandoah, Decreasing in DC),
• Small Crustal Source (typically < 0.5 ug/m3) either
Unchanged (4/8) or Increasing (4/8),
• Small Sea Salt Source (typically < 0.5 ug/m3) Increasing
at all 5 sites (Brigantine, Acadia, Shenandoah, GRSM &
DC) where it was identified.
Battelle SA Project: Plusses & Minuses
+ Data QA Screening rigorous & informative
- UNMIX vs. PMF comparison limited by exclusion
of “key” variables for UNMIX input
+ Although PMF “counterparts” were typically
found for more than half the UNMIX sources
- Poor matches with SPECIATE profiles
+ Surface Met Interpretations Helpful (but few)
+ Source Trend Info Encouraging (& Puzzling)
- Fuzziest Resolution of Most Important Sources
In the Northeast Study Region,
Including all or parts of MANEVU, MW RPO & VISTAS,
70 % of SO2 emitted is from
Coal-fired Electric Utilities,
In Battelle Study:
• Many Sulfate-Contributing Sources were identified:
Oil Combustion, Diesel Exhaust, MV Exhaust, Smelters,
Incinerators, Pulp Mills, Other Industrial, Sea Salt, and…
• “Secondary Sulfate” was the most Important Source at
all sites on Poor Visibility days, but…
• “Coal Burning” was not specifically identified at any site.
7 Similar Sources in 5 Model Runs at Brigantine, NJ
Source Names, Slopes & (Correlations) vs. VTDEC UNMIX Results
VT-UNMIX Names Rutgers-PMF
MW Coal Primary
0.97 (0.82)
MW Coal 2ndary
1.30 (0.93)
Residual Oil
0.84 (0.97)
2ndary Nitrates
0.87 (0.99)
Zn/Pb/EC Source 0.60 (0.87)
Soil
Sea Salt
1.01* (0.88)*
Clarkson-PMF
0.62 (0.97)
1.27 (0.94)
0.40 (0.97)
1.13 (0.99)
0.55 (0.88)
0.23 (0.83)
0.95 (0.96)
Battelle-PMF Battelle-UNMIX
0.62 (0.98) #6
1.32 (0.97) #7 1.31 (0.80) #4
1.13 (0.99) #4
1.84 (0.99) #2
0.63 (0.93) #2
0.27 (0.93) #9 1.71 (0.92) #5
0.77 (0.99) #5 1.12 (0.99) #1
Brigantine Sea Salt
Source, evaluated by
2 Local Surface Wind
Techniques and
2 Ensemble Back
Trajectory Techniques
(Provides confidence in these
met assessment methods for an
“unambiguous” source location)
VT Oil slightly
> than NJ Oil.
Both show Strong
Winter Maximum
(Consistent with
Utility fuel switch
to Natural Gas in
Summer)
Brigantine Surface Met
analyses point North
Trajectory Probability Fields for
Brigantine & Underhill
In VT/Clarkson/Rutgers work, Primary & Secondary Coal
sources were initially named “Winter Coal” & “Summer Coal”
because of Strong and Opposite Seasonal Patterns, but...
What are these Two Sources, really?
One Little Problem: These Receptor Models Can’t Identify
Sources with Variable Compositions (like Secondary Aerosols)
Primary & Secondary Species
from a Source are mixed in
different ratios on different days.
For example: “Primary”
Selenium and “Secondary”
Sulfate are Poorly correlated.
The Models Can’t find One Source, so they find Two. The so-called
Winter & Summer Coal sources bound the Outer Extremes (hard edges)
of the S:Se ratios (for both models at both sites), approximating “pure”
Primary and Secondary Aerosol Impacts from the same source region.
Within the Northeast AQR, Where are the Major
“Midwest Coal” & “East Coast Oil” Emission Regions?
Given the Strong Regional Spatial Patterns and the
Distinctly Different Trace Element Emissions from
Oil (Ni, V) & Coal (Se, S), we Should Be Able to
Discern these Sources by receptor & trajectory methods
Trajectory Probability Fields for Oil & (2) Coal Sources
by both PMF and UNMIX receptor models
at both Underhill, VT and Brigantine, NJ
Compared to Emissions-Based Definitions of the Major
Utility Coal and Oil Burning Regions in the Northeast