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Alternative Model Simulations: CAMx vs. CMAQ and PSAT vs. TSSA. Ralph Morris, Greg Yarwood, Bonyoung Koo, Steven Lau and Abby Hoats ENVIRON International Corporation, Novato, CA Gail Tonnesen, Chao-Jung Chien and Zion Wang University of California, Riverside.

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Alternative model simulations camx vs cmaq and psat vs tssa

Alternative Model Simulations: CAMx vs. CMAQ and PSAT vs. TSSA

Ralph Morris, Greg Yarwood, Bonyoung Koo, Steven Lau and Abby Hoats

ENVIRON International Corporation, Novato, CA

Gail Tonnesen, Chao-Jung Chien and Zion Wang

University of California, Riverside

WRAP Modeling Forum Meeting, San Francisco, CA March 8-9 18, 2005


Content

Purpose TSSA

Approach

CAMx/CMAQ Model Performance Evaluation

PM Source Apportionment Technology (PSAT)

Formulation and Testing

WRAP Application

Comparisons with CMAQ TSSA

Conclusions on Alternative Models and PM Source Apportionment

Content


Purpose

Compare CMAQ and CAMx model performance for February and July 2002 using latest 2002 databases

Compared CMAQ Tagged Species Source Apportionment (TSSA) and CAMx PM Source Apportionment Technology (PSAT)

 Should we run alternative models for key 2002 simulations in 2005-2006?

Purpose


Approach 1

Develop CAMx modeling databases for February and July 2002 and the 36km Continental US Inter-RPO Domain

15 day spin-up period (45 day simulations)

MM5CAMx to process latest 2002 36 km MM5 data

Used CMAQ Kv vertical diffusivity option

CMAQ-to-CAMx Processors

IC/BC and Emissions

Develop other CAMx inputs

Photolysis rates (TUV), landuse and terrain, Albedo/Haze/Ozone column, etc.

Approach (1)


Approach 2

Perform February and July 2002 36 km CAMx Base D (pre02d) Base Case simulations

Model performance evaluation and comparison against CMAQ Base D (pre02d) Base Case

Set up CAMx PSAT PM Source Apportionment using same source regions and categories as CMAQ TSSA

Run for Sulfate and Nitrate source apportionment and compare with CMAQ TSSA

Approach (2)


Approach 3

Extract PSAT SO4 and NO3 Source Apportionment results at Class I areas

Generate 24-hour average Model performance evaluation and comparison against CMAQ Base D (pre02d) Base Case

Set up CAMx PSAT PM Source Apportionment using same source regions and categories as CMAQ TSSA

Run for Sulfate and Nitrate source apportionment and compare with CMAQ TSSA for 24-hour impacts at Class I areas

Approach (3)


Model evaluation camx cmaq

Continental US 36 km Inter-RPO Domain Class I areas

6 Subregions: All US, WRAP, CENRAP, MRPO, VISTAS and MANE-VU States

Three Networks: IMPROVE, CASTNet, STN

PM Species Components

SO4, NO3, EC, OC, Soil, CM and TCM

CAMx V4.20beta Base D (pre02d) vs. CMAQ V4.4 Base D (pre03d)

Model Evaluation – CAMx/CMAQ


SO4 July 2002 USA Class I areasCMAQ vs. CAMx BaseD

SO4 CASTNet

SO4 IMPROVE

IMPROVE


SO4 2002 USA Class I areasCMAQ vs. CAMx BaseD

Jan SO4 IMPROVE

Jul SO4 STN


SO4 Jan 2002 USA Class I areasCMAQ vs. CAMx BaseD

SO4 Jan STN

SO4 Jan CASTnet


NO3 July 2002 USA Class I areasCMAQ vs. CAMx BaseD

NO3 IMPROVE

NO3 CASTNet


NO3 July 2002 USA Class I areasCMAQ vs. CAMx BaseD

NO3 STN

HNO3 CASTNet


NO3 January 2002 USA Class I areasCMAQ vs. CAMx BaseD

NO3 IMPROVE

NO3 CASTNet


NO3 Jan 2002 USA Class I areasCMAQ vs. CAMx BaseD

NO3 STN

HNO3 CASTNet


Carbon July 2002 USA Class I areasCMAQ vs. CAMx BaseD

OC IMPROVE

TCM STN


Carbon Jan 2002 USA Class I areasCMAQ vs. CAMx BaseD

OC IMPROVE

TCM STN


EC IMPROVE USA Class I areasCMAQ vs. CAMx BaseD

July EC

January EC


Hourly TCM July 2002 at SEARCH Yorkville Class I areas

Observed, CMAQ and CAMx


SOIL IMPROVE USA Class I areasCMAQ vs. CAMx BaseD

July SOIL

January SOIL

Note that Crustal emissions were not modeled separately as normally done in CAMx due to use of CMAQ2CAMx processor


CM IMPROVE USA Class I areasCMAQ vs. CAMx BaseD

July Coarse Mass

January Coarse Mass


SO4 IMPROVE WRAP Class I areasCMAQ vs. CAMx BaseD

July SO4 WRAP

January SO4 WRAP


NO3 IMPROVE WRAP Class I areasCMAQ vs. CAMx BaseD

July NO3 WRAP

January NO3 WRAP


OC IMPROVE WRAP Class I areasCMAQ vs. CAMx BaseD

July OC WRAP

January OC WRAP


EC IMPROVE WRAP Class I areasCMAQ vs. CAMx BaseD

July EC WRAP

January EC WRAP


SOIL IMPROVE WRAP Class I areasCMAQ vs. CAMx BaseD

July SOIL WRAP

January SOIL WRAP


CM IMPROVE WRAP Class I areasCMAQ vs. CAMx BaseD

July Coarse Mass WRAP

January Coarse Mass WRAP


Conclusions cmaq vs camx performance

Both models exhibit very similar good model performance for SO4 in summer

Slight SO4 overestimation in winter, CAMx overestimation greater than CMAQ

Both models poor NO3 performance

Summer underestimation (CMAQ worse than CAMx)

Winter overestimation (CAMx worse than CMAQ)

OC, EC, TCM, Soil and CM performance mixed

Further analysis needed

Conclusions: CMAQ vs. CAMx Performance


Source apportionment approaches

CALPUFF: SO4 in summer “chemistry” highly simplified, incorrect and over 20 years old (1983)

SCICHEM: needs 3-D concentrations fields, currently computationally demanding

Photochemical Grid Models:

Zero-Out Runs (actually sensitivity approach)

Reactive Tracer PSAT/TSSA approaches shows promise for source apportionment modeling

Source Apportionment Approaches


Pm source apportionment technology psat

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 for:

Sulfate, Nitrate, Ammonium, Mercury, Primary PM (EC, POA, crustal and other)

PM Source Apportionment Technology (PSAT)


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

PSAT Conceptual Approach


Zero out comparisons for sulfate
Zero-Out Comparisons for Sulfate for user selected source “groups”

  • 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


Mrpo large source episode maximum so4 psat versus zero out

Difference due to oxidant limitation for user selected source “groups”

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

PSAT

Zero-Out



Oxidant limiting sulfate example
Oxidant Limiting Sulfate Example Out”

PSAT

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


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

PSAT obtains 50+ SO4 source contributions in time needed for 1 zero-out assessment

PSAT Sulfate Evaluation


Psat chemical scheme for noy gasses
PSAT Chemical Scheme for NOy Gasses between PSAT and zero-out

  • 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 partitioning of noy gasses
PSAT Partitioning of NOy Gasses between PSAT and zero-out

CAMx box model run with 20 ppb initial NO and 100 ppb NO emissions at a constant rate. Looks reasonable, is it correct?


Independent check for noy soem

SOEM: Source Oriented External Mixture between PSAT and zero-out

We only use part of the SOEM concept here

Duplicate all NOy reactions in the chemical mechanism

“blue NOy” and “red NOy”

affects NO, NO2, PAN, HNO3, etc.

difficulty for self-reactions, e.g., NO + NO --> 2 NO2

forms “red,” “blue” and “purple” NO2

SOEM may change the base result

Model initial conditions (ICs) as “blue NOy”

Model emissions as “red NOy”

Implemented in CAMx, run for 1-D case (box model)

Independent Check for NOy: SOEM


Comparing soem and psat for noy
Comparing SOEM and PSAT for NOy between PSAT and zero-out

  • The independent SOEM method agrees well with PSAT


Testing secondary organics soa

CAMx SOA scheme between PSAT and zero-out

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

Test implementation using another SOEM method

duplicate “red/blue” reactions and species, similar to NOy testing

Testing Secondary Organics (SOA)


Psat apportionment of soa to ics and emissions
PSAT apportionment of SOA to ICs and Emissions between PSAT and zero-out

Biogenic emissions

Biogenic ICs


Psat soa apportionment for emissions
PSAT SOA Apportionment for Emissions between PSAT and zero-out

  • Excellent 1:1 correspondence between SOEM and PSAT results


Psat soa apportionment for ics
PSAT SOA Apportionment for Ics between PSAT and zero-out

  • 1:1 correspondence for ICs as well as for Emissions (last slide)

  • Conclusion: PSAT implementation for SOA is accurate


Full scale application testing by mrpo
Full-Scale Application Testing by MRPO between PSAT and zero-out

  • 13 Source Regions

  • 6 Emission Categories

  • Boundary Conditions

  • Initial Conditions

  • Source apportionment to 90 groups for SO4, NO3, NH4, SOA and 6 primary species

  • Results courtesy of Kirk Baker, LADCO/MRPO

Canada

WRAP

MANE-VU

MRPO

CENRAP

VISTAS





Wrap psat source categories

15 Source Regions between PSAT and zero-out

5 Source Categories

Biogenic

On-Road Mobile

Points

Fires

Area+Non-Road

Initial Concentrations

Boundary Conditions

77 Source Groups (77=15 x 5 + 2)

Sulfate Family (2)

SO2 (SO2)

PS4 (SO4)

Nitrate Family (5)

RGN (NOx+NO3+HONO+N2O5)

TPN (PAN+PNA)

NTR (RNO3)

HN3 (HNO3)

PN3 (PM NO3 )

Ammonium Family (2)

NH3 (NH3)

PN4 (NH4)

SOA (14), Hg (3) and Primary PM (6) Not Run

WRAP PSAT Source Categories


PSAT/TSSA Source Region Map between PSAT and zero-out

CA, NV, OR, WA, ID, UT, AZ, NM, CO, WY, MT, ND, SD, Eastern States and Mex/Can/Ocean


Psat vs tssa

24-hour Sulfate contributions ay Class I areas in the WRAP States

February and July 2002

Bar charts of Sulfate contributions by source group = Category_Area

Category = Bio, Mob, Pts, Fir, ANR

Area = CA, NV, OR, WA, …, SD, EST, Mex

Pts_NM = Point sources from New Mexico

ANR_AZ = Area+Non-Road sources from Arizona

Some differences in TSSA/PSAT Categories

TSSA mv = on-road + non-road; fires???; BCs???

PSAT vs. TSSA


TSSA/PSAT results for selected sites States

ARCH, FLAT, FOPE, GRCA, LOPE, LYND, MEAD, NOPL, ORPI, RMHQ, SAAN, SALM, SCOV, SEQI, SOLA, STPE, THBA


Grand Canyon, Arizona States

Day 182 (07/01/02) [2nd Worst Visibility Day in 2002]

NV Points Highest

AZ Points (5xsmall)

“Mex” Points

TSSA Units???

TSSA Other???


Grand Canyon, Arizona States

Day 188 (07/07/02) [15th Worst Visibility Day in 2002]

Some differences TSSA and PSAT

Pts_Mex, Other, BC


Grand Canyon, Arizona States

Day 194 (07/13/02) [7th Worst Visibility Day in 2002]

Pts_NV by far largest contributor for both TSSA and PSAT


Grand Canyon, Arizona States

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


FOPE, Fort Peck, Montana States

Day 185 (07/04/05) 6th Worst Day during 2002

Generally good agreement between PSAT and TSSA


Rocky Mtn. NP, Colorado States

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


Rocky Mtn. NP, Colorado States

Day 185 (07/04/05) 14th Worst Day of 2002

PSAT: CO_Pts; CO_NonRoad; UT_Fires, East_Pts

TSSA: CO_Pts; CO_Mobile; Other; East_Pts


Rocky Mtn. NP, Colorado States

Day 191 (07/04/05) 11th Worst Day of 2002


SALM, Idaho States

Day 182 (07/01/05)

With exception of Other (TSSA) and BC (PSAT) agree on top contributors


Conclusions alternative model

Alternative Model to CMAQ (CAMx) States

Addresses some model uncertainty using corroborative model (EPA, 2001)

Uses alternative science algorithms

Powerful diagnostic tool

Small additional work to operate as can use same MM5 (MM5CAMx) and SMOKE (CMAQ-to-CAMx) output

Conclusions – Alternative Model


Conclusions – PM Source Apportionment States

  • PM Source Apportionment Technology (PSAT) results mostly consistent with TSSA

    • Some differences, TSSA “Other” category makes it hard to interpret

  • Powerful diagnostic tool that can be used for source culpability (e.g., BART) and to design optimally effective control PM/visibility control strategies

  • Explains 100% of the PM sulfate and nitrate, doesn’t suffer “Other” unexplained portion of PM like TSSA


Bart modeling using grid models
BART Modeling using Grid Models States

  • 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)


CALPUFF estimates higher visibility impacts than CAMx/PSAT and consequently generally more days and larger spatial extent of dV > 0.5 deciview

PSAT

CALPUFF


July 19, 2002 24-Hour SO4 Concentrations IN Source (isgburn) and consequently generally more days and larger spatial extent of dV > 0.5 deciview

CALPUFF much higher concentrations away from source. Why secondary CALPUFF SO4 peak over Cape Cod?

CAMx PSAT

CALPUFF


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