WRAP Regional Modeling Center, Modeling Forum-AoH Meeting, San Diego, CA Nov 26, 2006 Overview of...

WRAP Regional Modeling Center, Modeling Forum-AoH Meeting, San Diego, CA Nov 26, 2006

Overview of Other RPO

Modeling Work

Ralph MorrisENVIRON International Corporation

Joint Modeling Forum and Attribution of Haze Workgroup Meeting

San Diego, CaliforniaNovember 2, 2006


Other RPO Modeling

• CENRAP– ENVIRON and UCR

• MRPO– LADCO w/ assistance from contractors

• VISTAS– ENVIRON, UCR and Alpine

• MANE-VU– NESCAUM, OTC, MARAMA, States


CENRAP Update• 2002 Actual Base Case

– MPE CMAQ and CAMx @ 36 km

• 2002 Typical Base Case (Base F latest)• 2018 Base E2 Emissions and CMAQ

– Working on 2018 Base F– Preliminary 2018 CAMx/PSAT runs

• Identify contributions of International Transport• How to work into Reasonable Progress

• 2018 Base E2 Visibility Projections– Comparisons with WRAP, MRPO and VISTAS

• 2018 Base F visibility projections ongoing– Ready in about 4 weeks


Treatment of International Transport• Modeled Uniform Rate of Progress (URP) test

compares against 2018 Goal from Glide Slope 2000-2004 Baseline to 2064 Natural Conditions– Modeled 2018 visibility projection includes contributions

from International Transport and Natural Sources that are not completely accounted for in 2064 Natural Conditions

– Regional Haze Rule goal is no man-made visibility impairment in 2064

• For demonstrating Reasonable Progress does this just apply to US man-made (controllable) sources?

• CENRAP Visibility Projections found Class I areas on US international border fail to meet URP goal– How to treat International Transport in modeled URP test?


How to Treat International Transport in Reasonable Progress

• Approach 1: Include International Transport with the 2064 Natural Conditions Goal– Can use different estimates of International Transport

(GEOS-CHEM, PSAT, etc.)

– Simple to implement

– Keeps Glide Slope in deciview

– Inconsistent with Regional Haze Rule 2064 Natural Conditions goal?

– Not liked by FLMs


How to Treat International Transport in Reasonable Progress

• Approach 2: Define 2064 goal as Elimination of U.S. Anthropogenic Emissions Contribution to Visibility Impairment– Interpretation of the “no man-made impairment” as

U.S. man-made impairment– Need approach to track U.S. anthropogenic

contribution – 2064 goal is zero– Must use Extinction (Mm-1) to calculate


Intl Transport & Reasonable Progress

• Approach 3: Adjust modeled 2018 visibility projection to account for International Transport (FLM suggestion)– Consistent with Regional Haze Rule– How and what to do?

• (3A) Assume International Transport component is reduced same amount as U.S. anthropogenic emissions component

– If International Transport is above and beyond Natural Conditions then this seems reasonable

– Can keep deciview– Promotes fairness across States with Class I areas in center versus

border of U.S.

• (3B) Other???


CENRAP PM Source Apportionment

• PM Source Apportionment Technology (PSAT)• 2018 Base D CAMx Database• State Level Geographic Regions

– CENRAP and Adjacent States

• Track Three Families – SO4; NO3 & Primary PM [No SOA or Hg]

• Use standard model output to split SOA into anthropogenic and biogenic SOA (SOA_A & SOA_B)– No geographic source apportionment for SOA


-2500 -2000 -1500 -1000 -500 0 500 1000 1500 2000 2500

-2000

-1500

-1000

-500

0

500

1000

150022 Separate States; rest of West and East US; Canada; Mexico GulfMex ; IC; & BC


Geographic PSAT• 2018 Base D Emissions Scenario

• Class I areas for W20% Days

• Convert to Extinction (Bext) and determine State’s contribution to Visibility Impairment on Worst 20% – Can also partition by RPO and split International

vs. U.S. Sources– Only geographic Source Apportionment at this time

• Can not Separate Natural from Anthropogenic U.S. (except for SOA_B)


0

10

20

30

40

50

60

70

80

BIBE1 GICL1 GUMO1 WIMO1

SOA_B & SOA_A All Sources BCs (Global Transport) Mexico

CENRAP

VISTAS + MANE-VU

0

10

20

30

40

50

60

70

80


MN IA NE MO KS AK OK LA TX ND SD

WY CO NM West WI IL MI IN KY TN MS

AL East CANADA MEXICO Gulf of MX IC BC SO_Anthro SO_Biog

0

10

20

30

40

50

60

70

80





Wichita Mountains, Oklahoma Visibility Extinction (Mm-1) Source Apportionment for the Worst 20% Days

70


Big Bend National Park, Texas Visibility Extinction Apportionment Worst 20% Days

0

10

20

30

40

50

60

70

80


bE

xt(1

/Mm

)

0

10

20

30

40

50

60

70

80





SOA_B & SOA_A All Sources

BCs (Global Transport)

Mexico

CENRAP States (Texas largest)

~60% of visibility extinction on average of Worst 20% Days due to international

transport


Wichita Mtns Oklahoma

22% due to non-US Anthro Sources

Bext


24% due to non-US Sources

BSO4


13% due to non-US Sources

BNO3


International Transport Methods• Use PM Source Apportionment Technology (PSAT)

to separately track contributions due to International Transport– Initial results for 2018 Base D

• Zero-Out GEOS-CHEM global chemistry model (eliminate U.S. sources or eliminate International sources)– Initial results from EPRI study with Harvard

• Two “independent” approaches for estimating contributions of International Transport to PM concentrations at Class I areas.– How do PSAT and GEOS-CHEM results compare?


0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

AC

AD

LYB

R

BR

IG

BO

WA

ISLE

BIB

E

CA

CR

MIN

G

UP

BU

EV

ER

CH

AS

SA

MA

CO

HU

OK

EF

RO

MA

SIP

S

SH

RO

GR

SM

LIGO

MA

CA

SW

AN

JAR

I

DO

SO

SH

EN

Am

mo

niu

m S

ulfa

te (

g

/m3 )

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

AC

AD

1

LYB

R1

BR

IG1

BO

WA

1

ISLE

1

BIB

E1

CA

CR

1

MIN

G1

UP

BU

1

EV

ER

1

CH

AS

1

SA

MA

1

CO

HU

1

OK

EF

1

RO

MA

1

SIP

S1

SH

RO

1

GR

SM

1

LIGO

1

MA

CA

1

SW

AN

1

JAR

I1

DO

SO

1

SH

EN

1

Am

mo

niu

m S

ulfa

te (

g

/m3 )Sulfate (SO4)

Annual Average International Transport by

CAMx/PSAT and GEOS-CHEM

models

Excellent to Good Agreement of Two

Methods

Not truly “independent”

evaluation since CAMx/PSAT runs

used GEOS-CHEM BCs, but results

encouraging

CAMx/PSAT

GEOS-CHEM


0.00

0.05

0.10

0.15

0.20

0.25

0.30

AC

AD

LYB

R

BR

IG

BO

WA

ISLE

BIB

E

CA

CR

MIN

G

UP

BU

EV

ER

CH

AS

SA

MA

CO

HU

OK

EF

RO

MA

SIP

S

SH

RO

GR

SM

LIGO

MA

CA

SW

AN

JAR

I

DO

SO

SH

EN

Org

anic

Car

bo

n M

ass

(

g/m

3 )

0.00

0.05

0.10

0.15

0.20

0.25

0.30

AC

AD

1

LYB

R1

BR

IG1

BO

WA

1

ISLE

1

BIB

E1

CA

CR

1

MIN

G1

UP

BU

1

EV

ER

1

CH

AS

1

SA

MA

1

CO

HU

1

OK

EF

1

RO

MA

1

SIP

S1

SH

RO

1

GR

SM

1

LIGO

1

MA

CA

1

SW

AN

1

JAR

I1

DO

SO

1

SH

EN

1

Org

anic

Car

bo

n M

ass

(

g/m

3 )

Organic Carbon Mass (OCM) Annual Average International Transport

by CAMx/PSAT and GEOS-CHEM models

Reasonably Good Agreement of Two

Methods, As much as a Factor of Two Different (LYBR), but most fairly

close

Larger differences in OCM. CAMx/PSAT includes OCM from

Biogenic Sources so expected to be higher, but frequently lower?

CAMx/PSAT

GEOS-CHEM


0.00

0.02

0.04

0.06

0.08

0.10

AC

AD

LY

BR

BR

IG

BO

WA

ISLE

BIB

E

CA

CR

MIN

G

UP

BU

EV

ER

CH

AS

SA

MA

CO

HU

OK

EF

RO

MA

SIP

S

SH

RO

GR

SM

LIG

O

MA

CA

SW

AN

JA

RI

DO

SO

SH

EN

Ele

me

nta

l Ca

rbo

n (

g/m

3 )0.00

0.02

0.04

0.06

0.08

0.10

AC

AD

1

LY

BR

1

BR

IG1

BO

WA

1

ISL

E1

BIB

E1

CA

CR

1

MIN

G1

UP

BU

1

EV

ER

1

CH

AS

1

SA

MA

1

CO

HU

1

OK

EF

1

RO

MA

1

SIP

S1

SH

RO

1

GR

SM

1

LIG

O1

MA

CA

1

SW

AN

1

JAR

I1

DO

SO

1

SH

EN

1

Ele

me

nta

l Ca

rbo

n (

g/m

3 )Elemental Carbon (EC) Annual

Average International Transport by

CAMx/PSAT and GEOS-CHEM

models

Differences in fires may be

affecting results. Large Quebec fires in 2002

affect CAMx/PSAT. Mex

fires in GEOS-CHEM?

CAMx/PSAT

GEOS-CHEM


Accounting for International Transport

• Run PSAT for 2002 and 2018 separating controllable and uncontrollable (or US vs. International Transport) components– Approach 1: Add “International Transport” component to

Natural Conditions for 2064 goal and redefine 2018 URP goal from 2000-2004 Baseline to new 2064 goal (in deciviews)

– Approach 2: Redefine URP goal based on Controllable haze only. 2064 endpoint would be zero (no man-made impairment)

• Examples of these approaches using current 2018 PSAT run follows– International Transport = Uncontrollable =

Mex+Can+BCs+SOA_B


Uniform Rate of Reasonable Progress Glide PathBig Bend NP - 20% Data Days

17.1016.42

14.73

13.03

11.349.64

7.956.93

16.36

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Haz

ines

s In

dex

(Dec

ivie

ws)

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

Big Bend: Standard URP Calculation = 31% of URP goal



17.10 16.7615.90

15.0414.17

13.3112.45 11.94

16.36

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Haz

ines

s In

dex

(Dec

ivie

ws)

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

Big Bend: Approach 1 IntlTrans in 2064 Natural Conditions = 62%



19.94 18.61 15.29 11.96 8.64 5.32 1.99 0.0018.51

0

20

40

60

80

100

120

140

160

180

200

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Ext

inct

ion

(1/M

m)

Glide Path Observation Method 1 Prediction

Big Bend: Approach 2 URP Based on Controllable Haze = 31%


Big Bend Example

• Standard URP = 31%

• Intl Trans in 2064 = 62%

• Controllable URP = 31% (???)– Could not do this correctly since only had

geographic PSAT for 2018 and natural emissions were included in US portion

• Also need 2002 source apportionment

• CENRAP intends to correct this with Base F modeling


CENRAP PSAT Next Steps• International Transport and Natural Conditions

Analysis combined with Control Strategy Design Analysis– Source Regions: CENRAP States and Nearby

WRAP, MRPO and VISTAS States– Source Categories: EGU, Non-EGU Point, On-

Road Mobile; Off-Road Mobile; Natural Emissions (Biogenics, Wildfires, Non-Ag WBD); Remaining Anthropogenic (e.g., area, Ag WBD)

– 2002 and 2018 Base Case emissions


CENRAP PSAT Source Regions

-2736 -2412 -2088 -1764 -1440 -1116 -792 -468 -144 180 504 828 1152 1476 1800 2124 2448-2088

-1872

-1656

-1440

-1224

-1008

-792

-576

-360

-144

72

288

504

720

936

1152

1368

1584

1800


Midwest RPO Modeling• MM5 Meteorology

– 2001, 2002, 2003 36-km for regional haze & PM2.5

– 2002 12-km for 8-hour ozone• EMS Emissions (Base K)

– Starting to Migrate to CONCEPT• CAMx Air Quality Model

– CMAQ may be used for corroborative analysis– Update model for SOA treatment– Using PSAT and OSAT

• Aggressive Movement to 2005 Modeling Year– Driven by 8-hour ozone and PM25 issues


Midwest RPO SOA Updates• Update Biogenic Emissions Model to MEGAN

and Generate all SOA precursor Species– E.g., sesquiterpenes

• Update CAMx SOA Module– Treat new SOA precursors– Separate emissions for SOA species from gas-phase

chemistry species– Slightly different than VISTAS SOAmods update that

is plug and play with current biogenic emissions

• Considering directly emit Condensable Gases (CG) from mobile sources


Midwest RPO Controls• Need to address 8-hr ozone and PM2.5 as

well as regional haze

• BART for non-EGUs

• Beyond CAIR scenario for EGUs

• Fuel scenarios for urban ozone/PM2.5

• Combined with Northeast to look at regional diesel retrofit controls


VISTAS Modeling• 2002 36/12 km MM5• SMOKE 36/12 km• CMAQ 2002 36/12 km

– Looked at CAMx early on but dropped due to resource and time constraints

– Led to addition of biogenic SOA treatment

• Just finished Base G (final) Modeling– 2009 8-hour ozone and PM25 projections under ASIP– Visibility projections 36-km vs. 12-km similar– Using both 36 km and 12 km grid for 2009 and 2018

projections


2018 Base G Visibility Projections

• 2018 36/12 km Base G OTB Base Case– With CAIR but Without BART

• New and Old IMPROVE equation– New Natural Conditions for New IMPROVE from

VIEWS

• Previously presented preliminary 2018 36 km Base G visibility projections– Data substitution updates since then


Data Substitution Updates• New data substitution database received from ARS

on October 19, 20061. Add one more site (CADI1) to New IMPROVE equation database

Old IMPROVE still not supporting CADI1

2. Update MING1 with latest data from UC Davis

3. Other minor updates (BRET1, etc.)

• Always use newest data when available– Still using old substitution data for CHAS1 as missing data in 2003

& 2004 not in new database

• Display using “DotPlots”, percentage of achieving 2018 URP goal


CMAQ 2018g1a/Typ02g Method 1 predictions for VISTAS+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%C

OH

U1

DO

SO

1

GR

SM

1

JAR

I1

LIG

O1

CA

DI1

MA

CA

1

SH

EN

1

SH

RO

1

SIP

S1

CH

AS

1

EV

ER

1

OK

EF

1

RO

MA

1

SA

MA

1

SW

AN

1

BR

ET

1

BR

IG1

CA

CR

1

HE

GL1

MIN

G1

UP

BU

1

Per

cen

t o

f ta

rget

red

uct

ion

ach

ieve

d

CMAQ New IMPROVE Algorithm 12km

CMAQ Old IMPROVE Algorithm 12km

CMAQ New IMPROVE Algorithm 36km

CMAQ Old IMPROVE Algorithm 36km

VISTAS non-VISTAS


CMAQ Method 1 predictions for VISTAS+ sites Across RPOs

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

CO

HU

1

DO

SO

1

GR

SM

1

JAR

I1

LIG

O1

CA

DI1

MA

CA

1

SIP

S1

SH

RO

1

SH

EN

1

CH

AS

1

EV

ER

1

OK

EF

1

RO

MA

1

SA

MA

1

SW

AN

1

BR

IG1

BR

ET

1

CA

CR

1

HE

GL

1

UP

BU

1

MIN

G1

Per

ce

nt

of

targ

et

red

uc

tio

n a

ch

iev

ed

VISTAS New Algo 12km (baseG)VISTAS Old Algo 12km (baseG)VISTAS New Algo 36km (baseG)VISTAS Old Algo 36km (baseG)CENRAP New Algo 36km (18e2 SOA)CENRAP Old Algo 36km (18e2 SOA)MwRPO Old Algo 36km (R4s1a)

Comparison of VISTAS 2018 36/12 km Base G New/Old IMPROVE projections with CENRAP 36 km New/Old and MRPO 36 km Old IMPROVE projections

CENRAP/MRPO Visibility Projections Now Much More Consistent with VISTAS


.

.

.

.

.Hercules Glade, MO

.

.

Likely to meet (>110%)

May meet (90-110%)

Likely not meet (<90%)

VISTAS 2018 Base G Uniform Rate of Progress AssessmentUsing New IMPROVE equation to calculate visibility


VISTAS Next Steps

• QA/QC of 2009 and 2018 projections

• Area of Influence (AOI) analysis– Identify sources within AOI

• BART control definitions

• 2018 strategy runs and projections

• States perform local PM2.5 and 8-hour ozone modeling


MANE-VU Modeling• NESCAUM, OTC, MARAMA, UMD,

States mainly in-house analysis• Contribution Report – look at various

methods for where PM came from– Back Trajectories -- Residence Time– PMF Receptor Modeling– REMSAD Tagged Species– CMAQ

• Joint study with MRPO on regional diesel retrofit controls


Potential Effects on WRAP

• 2018 visibility projections across RPOs starting to converge

• WRAP may want to consider processing existing WRAP 2002/2018 PSAT results to look at International Transport/Natural Emissions issues at WRAP Class I areas

• CENRAP 2018 visibility projections consistent with WRAP

WRAP Regional Modeling Center, Modeling Forum-AoH Meeting, San Diego, CA Nov 26, 2006 Overview of...

Documents

Transcript of WRAP Regional Modeling Center, Modeling Forum-AoH Meeting, San Diego, CA Nov 26, 2006 Overview of...