ENVIRON International Corporation University of California at Riverside Review of WRAP Regional...

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



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



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



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



Example UCR Tool MPE Plots, CMAQ vs. CAMx for January & July 2002 allsite_allday for WRAP States



MPE Plots for SO4 at Canyonlands and July 2002 CMAQ vs. CAMx Scatter Plot and Stats

Observed, CMAQ, and CAMx Time Series Plot



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IMPROVE STN CASTNET

SO4 IMPROVE in WRAP States

Monthly Fractional Bias

CAMx

CMAQ



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

Observations

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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

Coarse Material

Soil

Elemental Carbon

Organic Material

Ammonium Nitrate

Observations

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Model Case: 2002 baseA 36k

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Observed vs. Modeled Daily Extinction @ Canyonlands

Observed Observed

CMAQ CAMx



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



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)



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



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



Difference due to oxidant limitation

PSAT Zero-Out

MRPO Large Source: Episode Maximum SO4 PSAT versus “Zero Out”



• 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



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



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



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



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



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



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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



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???




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Day 188 (07/07/02) [15th Worst Visibility

Day in 2002]

Some differences TSSA and PSAT

Pts_Mex, Other, BC




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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



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



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



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



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)



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



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



• 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



CALPUFF estimates higher visibility impacts than CAMx/PSAT and consequently generally more days and

larger spatial extent of dV > 0.5 deciview

CALPUFF PSAT



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?



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



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



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)



Uniform Rate of Reasonable Progress Glide PathGreat Smoky Mountains NP (TN) - 20% Worst Days

28.9427.77

24.86

21.94

19.02

16.11

13.1911.44

25.2323.76

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Glide Path Natural Condition (Worst Days) Observation Method 1B Prediction

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



Worst 20% Obs vs 36km Typical Run3 at GRSM1

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56 122 128 137 152 155 164 170 173 182 188 191 197 215 218 221 224 227 233 248 251 254 260 266 _

Julian Day in Worst 20% group

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bSO4

Great Smoky Mountains Obs vs. Model Extinction W20%

> 80% extinction due to SO4



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



-100%

0%

100%

1B 1C 3C 6B 6C

Five examples of WRAP visibility projections:WHIT, NMGRCA, AZCRLA, ORSAGO, CADENA, AK



Dust



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White Mountain, NM – Worst 20% Days in 2002 Observations vs. Predictions

Obs Dust Fires

Nitrate





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Grand Canyon, AZ – Worst 20% Days in 2002Observations vs. Predictions

Fires in model

Dust in obs









10.03 9.869.42

8.998.55

8.127.68 7.42

0

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1989 1994 1999 2004 2009 2014 2019 2024 2029 2034 2039 2044 2049 2054 2059 2064

Year

Ha

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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



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

0

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1989 1994 1999 2004 2009 2014 2019 2024 2029 2034 2039 2044 2049 2054 2059 2064

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Glide Path Natural Condition (Best Days) Observation



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



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



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???



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

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