September 14-15, 2005 Attribution of Haze Workgroup Meeting San Francisco, California
description
Transcript of September 14-15, 2005 Attribution of Haze Workgroup Meeting San Francisco, California
ENVIRON International Corporation
University of California at Riverside
Review of WRAP Regional Modeling Center (RMC)
Deliverables Related to the Technical Support System (TSS)
September 14-15, 2005Attribution of Haze Workgroup Meeting
San Francisco, California
Ralph Morris and Gerry MansellENVIRON Corporation
Gail Tonnesen and Zion WangUniversity of California, Riverside
ENVIRON International Corporation
University of California at Riverside
Overview• 2002 Base A Base Case CMAQ/CAMx
Modeling and Model Evaluation• 2002 CAMx PSAT Source Apportionment
Modeling• PSAT/TSSA Comparisons• RMC BART Modeling Plans• 2018 Simulations and Visibility Projections• Modeling Elements of the Visibility SIP
Weight of Evidence (WOE) Reasonable Progress Goal (RPG) Demonstration
ENVIRON International Corporation
University of California at Riverside
2002 Base A Modeling• CMAQ emissions ready September 12, 2005• Start annual 2002 36 km CMAQ run September 19,
2005• CAMx emissions ready September 19, 2005• Compare Jan/Jul 2002 CMAQ/CAMx October 3, 2005
– Make decisions on model for 12 km modeling and control strategy evaluation
• Finish annual 2002 36 km CAMx run October 10, 2005• Perform PSAT PM Source Apportionment using
CAMx October 31, 2005• 2018 Emission Inventories October 31, 2005
ENVIRON International Corporation
University of California at Riverside
2002 Base A Modeling• Example of Model Performance Evaluation (MPE)
displays of use to the TSS• UCR MPE Tool
– Scatter & Time Series Plots by subregion• allsite_allday (SO4 example for WRAP States)• allday_onesite (SO4 example for Canyonlands)• onesite_allday
– Monthly Bias/Error plots• By subregion (Bias example for SO4 in WRAP States)
– Stacked 24-hr average extinction plots• Observed vs. Model (Canyonlands example)
• Comparisons for Worst/Best 20% Days
ENVIRON International Corporation
University of California at Riverside
Example UCR Tool MPE Plots, CMAQ vs. CAMx for January & July 2002 allsite_allday for WRAP States
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MPE Plots for SO4 at Canyonlands and July 2002 CMAQ vs. CAMx Scatter Plot and Stats
Observed, CMAQ, and CAMx Time Series Plot
ENVIRON International Corporation
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IMPROVE STN CASTNET
SO4 IMPROVE in WRAP States
Monthly Fractional Bias
CAMx
CMAQ
ENVIRON International Corporation
University of California at Riverside
2002 Reconstructed Extinction
Canyonlands National Park, UT
Observations (top) vs. CMAQ Model Results (bottom)Ammonium Sulfate
Coarse Material
Soil
Elemental Carbon
Organic Material
Ammonium Nitrate
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Model Case: 2002 baseA 36k
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2002 Reconstructed Extinction
Canyonlands National Park, UT
Observations (top) vs. CMAQ Model Results (bottom)Ammonium Sulfate
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Soil
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Ammonium Nitrate
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Observed vs. Modeled Daily Extinction @ Canyonlands
Observed Observed
CMAQ CAMx
ENVIRON International Corporation
University of California at Riverside
Source Apportionment Approaches• CALPUFF: Lagrangian non-steady-state puff
model “Chemistry” highly simplified, incorrect and over 20
years old (1983) Fails to adequately account for wind shear
• SCICHEM: Lagrangian model with full chemistry Needs 3-D concentrations fields Currently computationally demanding
• Photochemical Grid Models: CMAQ/CAMx Zero-Out Runs (actually sensitivity approach) Reactive Tracer PSAT/TSSA approaches
ENVIRON International Corporation
University of California at Riverside
PM Source Apportionment Technology (PSAT) in CAMx
• Reactive tracer approach that operates in parallel to the host model to track PM precursor emissions and formation
• Set up to operate with families of tracers that can operate separately or together
• Sulfate (SO4)
• Nitrate (NO3)
• Ammonium (NH4)
• Secondary Organic Aerosols (SOA)
• Mercury (Hg)
• Primary PM (EC, OC, Soil, CM)
ENVIRON International Corporation
University of California at Riverside
PSAT Conceptual Approach • Modify CAMx to include families of tracers
(tagged species) for user selected source “groups”– Source group = source category and/or geographic area
• Build on CAMx ozone apportionment schemes (OSAT, APCA)
• Tag primary species as they enter the model – SO2i , NOi , VOCi , primary PM (crustal, EC, etc.)
• When secondary species form, tag them according to their parent primary species– SO4i , NO3i , SOAi
ENVIRON International Corporation
University of California at Riverside
Zero-Out Comparisons for Sulfate
• Use Eastern US/Canada modeling domain
• Add four hypothetical point sources to base emissions
• Test large and small emission rates to investigate signal/noise
Large: SOx = 850 TPD
Small: SOx = 0.85 TPD
X
X
X
X
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University of California at Riverside
Difference due to oxidant limitation
PSAT Zero-Out
MRPO Large Source: Episode Maximum SO4 PSAT versus “Zero Out”
ENVIRON International Corporation
University of California at Riverside
• PSAT attributes 50% of SO4 to source A (and 50% to B)• Zero-out attributes zero SO4 to source A (no source is culpable)• Zero-out result (sensitivity) is not a reasonable apportionment for this
example
Base case with sources A and B
SO2 + H2O2 = PSO4
A 5 1
B 5
2 1
2
Zero out source A A 0 0 B 5
2 2
2
Zero out apportionment for PSO4 for source A = 2 – 2 = 0 PSAT apportionment for PSO4 for source A = 1
PSAT
Zero-Out
Oxidant Limiting Sulfate Example
ENVIRON International Corporation
University of California at Riverside
PSAT Sulfate Evaluation• Good agreement for extent and magnitude of
sulfate impacts between PSAT and zero-out– Comparing the outer plume edge is a stringent
test
• Zero-out impacts can be smaller or larger due to oxidant limited sulfate formation and changes in oxidant levels.
• Run times look very good– Two tracers per source group for sulfate
– PSAT obtains 50+ SO4 source contributions in time needed for 1 zero-out assessment
ENVIRON International Corporation
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PSAT Chemical Scheme for NOy Gasses
• PSAT tracks 4 groups of NOy gasses– RGN– TPN– HN3– NTR
• Conversion of RGN to HN3 and NTR is slowly reversible
• Conversion of RGN to TPN is reversible – rapidly or slowly
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PSAT for SOA• CAMx SOA scheme
– VOC -- OH, O3, NO3 --> Condensable Gas (CG) <==> SOA
– CGs partition to an SOA solution phase
– PSAT implementation straightforward, but many terms
• Three types of VOC precursor
– alkanes, aromatics, terpenes
• Five pairs of CG/SOA
– four anthropogenic, one biogenic
– low/high volatility products
• PSAT tracers for VOC, CG and SOA species– 14 tracers per source group
ENVIRON International Corporation
University of California at Riverside
PSAT Evaluation for NO3 and SOA
• Independent check against SOME– Source Oriented External Mixture (Kleiman et al at UC
David)– SOME uses explicit species for each source group that are
integrated in the model• Highly computationally demanding
– Zero-Out comparisons not appropriate for VOC/NOx due to nonlinear chemistry
• Good agreement between PSAT and SOEM for NO3 and SOA– http://pah.cert.ucr.edu/aqm/308/meetings/March_2005/03-
08_09-05.SF_CA/Alternative_Model_Mar8-9_2005_MF_Meeting.ppt
ENVIRON International Corporation
University of California at Riverside
CAMx/PSAT and CMAQ/TSSA Comparisons Feb/Jul 2002
• PSAT Configuration– 15 source regions– 5 Source Categories: (1) Biogenic; (2) On-Road Mobile; (3)
Points; (4) Fires and (5) Area+Non-Road– Initial and Boundary Concentrations– 77 Source Groups (77=15 x 5 + 2)– SO4, NO3 and NH4 families of tracers
• Did not run SOA, Hg and Primary PM tracers
• TSSA Configuration– Differences in source group source categories (e.g., mv =
on-road + non-road, fires?, BC??)– “Other” category in TSSA for unattributable PM
ENVIRON International Corporation
University of California at Riverside
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PSAT/TSSA Source Region MapCA, NV, OR, WA, ID, UT, AZ, NM, CO, WY, MT, ND,
SD, Eastern States and Mex/Can/Ocean
ENVIRON International Corporation
University of California at Riverside
24-hr average contributions to SO4 at GRCA on 2002 182
0.00.10.20.30.40.50.60.70.80.91.0
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Grand Canyon, Arizona
Day 182 (07/01/02) [2nd Worst Visibility
Day in 2002]
NV Points Highest
AZ Points (5xsmall)
“Mex” Points
TSSA Units???
TSSA Other???
ENVIRON International Corporation
University of California at Riverside
24-hr average contributions to SO4 at GRCA on 2002 188
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Grand Canyon, Arizona
Day 188 (07/07/02) [15th Worst Visibility
Day in 2002]
Some differences TSSA and PSAT
Pts_Mex, Other, BC
ENVIRON International Corporation
University of California at Riverside
24-hr average contributions to SO4 at GRCA on 2002 032
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Grand Canyon, Arizona
Day 32 (02/01/02) [8th Best Visibility
Day in 2002]
PSAT: UT_Points; BC; AZ_Points; UT_NonRoad; NM_Points
TSSA: UT_Points; Other; OR_Points; WA_Points; ID_Points
ENVIRON International Corporation
University of California at Riverside
24-hr average contributions to SO4 at RMHQ on 2002 182
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Rocky Mtn. NP, Colorado
Day 182 (07/01/05) Worst Day of 2002
PSAT: UT_Fires; CO_Pts; NV_Pts;
CO_Fires; UT_Pts.
TSSA: Other; CO_Pts; UT_Pts; NV_Pts;
If Fires in “Other” then fairly good agreement
ENVIRON International Corporation
University of California at Riverside
Conclusions – PM Source Apportionment
• PSAT results mostly consistent with TSSASome differences, TSSA “Other” category makes it hard to
interpretVersion of CMAQ with TSSA has known mass
conservation problems
• Powerful diagnostic tool that can be used for source culpability (e.g., BART) and to design optimally effective control PM/visibility control strategies
• PSAT explains 100% of the PM, doesn’t suffer “Other” unexplained portion of PM like TSSATSSA being implemented in latest versions of CMAQ
ENVIRON International Corporation
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PSAT Plans for WRAP• 2002 Base A Emissions
– Source Regions• WRAP States plus others and IC/BC
– Source Categories• Anthropogenic versus “Natural” emissions
– SO4, NO3 and NH4 initially, test SOA and primary PM
• 2018 Base Case emissions– Source regions and categories TBD
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22 Pre-Merged Emission Files1. Argts: Area sources except dust sources2. Arfgts: Area fires from CENRAP3. Awfgts3d: WRAP wild, prescribed and agricultural fires4. Bsfgts3d: Canadian Wild fires/Blue Sky algorithm5. fdgts_RPO: Fugitive dust (Ag & construction) for entire domain6. mbgts_WRAP: On road mobile sources for WRAP RPO 7. mbgts_CANDA_MEX: On road mobile sources for Can/Mex8. mbvgts_CENRAP36: On-road mobile sources for CENRAP states9. mbvgts_RPO_US36: On road mobile sources for MW, VISTAS, & MAINE-VU10. nh3gts_RPO36: Ammonia from agricultural sources for CENRAP/MW states11. nh3gts_WRAP36: Ammonia emissions ag sources for WRAP GIS model12. Nrygts: Off road mobile with annual IDA files13. Nrmgts: Off road mobile with monthly or seasonal IDA files 14. Nwfgts3d: Point sources fires from non WRAP states (CENRAP and VESTAS)
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22 Pre-Merged Emission Files15. Ofsgts3d: Off shore point sources in the Gulf of Mexico16. Ofsmagts: Off shore Marines shipping in the Pacific Ocean17. Ofsargts: Off shore area sources in the Gulf of Mexico18. ptgts3d_RPO_US36: Point sources emissions for all RPOs, Can & Mex19. rdgts_RPO: Road dust for the entire domain20. B3gts_RPO: Biogenc emissions from BIES3 for the entire domain21. wb_dus: Wind blown dust for entire domain22. Oggts3d: Oil and gas for WRAP states (except CA)
• 2002 PSAT run need to define “natural” emissions– Arfgts: Area fires from CENRAP– Awfgts3d: WRAP wild, prescribed and agricultural fires (will need to process
wildfires separately)– Bsfgts3d: Canadian Wild fires/Blue Sky algorithm– Nwfgts3d: Point sources fires from non WRAP states (CENRAP and VESTAS)?– B3gts_RPO: Biogenc emissions from BIES3 for the entire domain– wb_dus: Wind blown dust for entire domain
ENVIRON International Corporation
University of California at Riverside
WRAP RMC “BART” Modeling• RMC will perform regional photochemical grid
model of alternative regional strategies using CMAQ and/or CAMx with PSAT
• RMC will assist States who desire to perform source-specific CALPUFF modeling– Provide States with 3-tears of CALMET ready MM5
fields (2001, 2002 and 2003)
• May perform source-specific modeling using PSAT for 2002
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• Midwest RPO (MRPO)• Use combination of
photochemical grid and CALPUFF modeling in the BART analysis
• Comprehensive Air-quality Model with extensions (CAMx) PM Source Apportionment Technology (PSAT)
Example of BART Modeling using Grid Models
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University of California at Riverside
CALPUFF estimates higher visibility impacts than CAMx/PSAT and consequently generally more days and
larger spatial extent of dV > 0.5 deciview
CALPUFF PSAT
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CAMx PSAT CALPUFF
July 19, 2002 24-Hour SO4 Concentrations IN Source (isgburn)
CALPUFF much higher concentrations away from source. Why secondary CALPUFF SO4 peak over Cape Cod?
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CALPUFF More Conservative than Grid Models
• CALPUFF chemistry overstates NO3 and SO4 in winter
• CALPUFF understates dispersion because it fails to adequately account for wind shear and wind variations across the puff– Uses just one wind to advect entire column of puff
– IWAQM found CALPUFF overestimation bias of a factor of 3-4 at distances beyond 200-300 km
• When encountering stagnant conditions, puffs pile up on each other and stop dispersing– Violates 2nd Law of Thermodynamics
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Surface Winds0600
Surface Winds1200
300 AGL Winds0600
CALPUFF puff column advected north by winds at 300 m AGL even though surface winds from east and north
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2018 Modeling/Visibility Projections
• Visibility projections use 2018 and 2002 modeling results in relative sense to scale observed 2000-2004 visibility to 2018– Draft EPA Guidance (2001)
• 2018 Visibility Goal based on Glide Path from current (2000-2004) observed visibility to Natural Conditions in 2064– EPA Guidance for default Natural Conditions
(2003)
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University of California at Riverside
Uniform Rate of Reasonable Progress Glide PathGreat Smoky Mountains NP (TN) - 20% Worst Days
28.9427.77
24.86
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Baseline Conditions = 28.9 dvNatural Conditions = 11.4 dv2018 Visibility Goal = 24.9 dv
2018 Reduction Goal = 4.1 dv2018 Modeled Reduction = 5.2
dvGRSM achieves 2018 Vis Goal
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Worst 20% Obs vs 36km Typical Run3 at GRSM1
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Great Smoky Mountains Obs vs. Model Extinction W20%
> 80% extinction due to SO4
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University of California at Riverside
Modeled Visibility Goal Test will be Difficult for WRAP Class I Areas
• Worst days not always dominated by SO4 -- OMC, NO3 and/or CM can be more important than SO4 at many sites– California NO3 issue– Southwestern Desert dust (CM)– Fires, Fires, Fires, Fires
• Posses unique and special conditions for modeling visibility projections
• May be more difficult to model achievement of visibility goal– Many sites dominated by fires for Worst 20% days and assumed to remain
unchanged from 2002 to 2018– Don’t CAIR states– Point source SO2 and NOx controls much less effective at reducing
visibility in west compared to east
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-100%
0%
100%
1B 1C 3C 6B 6C
Five examples of WRAP visibility projections:WHIT, NMGRCA, AZCRLA, ORSAGO, CADENA, AK
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Dust
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White Mountain, NM – Worst 20% Days in 2002 Observations vs. Predictions
Obs Dust Fires
Nitrate
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University of California at Riverside
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Grand Canyon, AZ – Worst 20% Days in 2002Observations vs. Predictions
Fires in model
Dust in obs
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University of California at Riverside
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University of California at Riverside
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University of California at Riverside
10.03 9.869.42
8.998.55
8.127.68 7.42
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Glide Path Natural Condition (Worst Days) Observation
Denali Glide Path to Natural Conditions, Baseline for Current Worst Days (10 dv) > 2064 Natural Conditions for
many eastern Class I areas (e.g., GRSM @ 11 dv) Denali 2018 RPG Reduction = 0.61 dv
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Denali National Park Best 20% Days (B20)Current 5-Year Average for B20 Days (1.91 dv) lower than EPA
default natural conditions for best days (2.30 dv)
1.91 1.94 2.00 2.07 2.13 2.20 2.26 2.30
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Conclusions – WRAP Vis Projections (1)• Much more diverse PM mixture in western
US on Worst 20% days than in the east• Fires and wind blown dust much more
important – little opportunity to control– Focus reasonable progress on days with high
anthropogenic contributions?– Incorporate fires and dust in Natural Conditions
endpoint?
• Mexico, Canada and global transport can have large influence at some Class I areas
• Modeled visibility goal test will likely not be achieved at many WRAP Class I areas
ENVIRON International Corporation
University of California at Riverside
Conclusions – WRAP Vis Projections (2)
• Need to start developing strategy for demonstrating reasonable progress for WRAP– Weight of Evidence (WOE) RPG demo needed
• Enforceable emission reductions• Treatment of extreme events (fires/dust/international)• Visibility improvements on days due to US anthro
sources– Examine extinction improvements by species?
• Smoke management plan• Modeled visibility changes are just one element of WOE
RPG demonstration
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Modeled WOE RPG Elements• Glide paths and modeled RPG test (EPA)
• Eliminate days dominated by “natural” events in modeled RPG test (e.g., fires, dust)
• 2018 projections for species dominated by anthropogenic emissions (e.g., SO4, NO3)
• 2018 projections for modeled worst visibility days, worst sulfate days, etc.
• Other???
ENVIRON International Corporation
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RMC 2018 Modeling Schedule• 2018 SMOKE Emissions Modeling Oct’05
• 2018 36 km CMAQ/CAMx Modeling Nov’05– Preliminary 2018 visibility projections Dec’05
• 2018 12 km modeling Nov-Dec’05
• 2018 Source Apportionment Modeling Jan+’06
• 2018 Control Strategy Modeling 2006