Source Attribution Modeling to Identify Sources of Regional Haze in Western U.S. Class I Areas

Gail Tonnesen, EPA Region 8Pat Brewer, National Park Service

Tom Moore, WESTAR/WRAP CMAS, Chapel Hill, NC, October 6, 2015

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Regional Haze• Clean Air Act goal is to achieve

natural visibility at Class I areas by 2064.

• Regional haze metrics rely on IMPROVE monitoring data:

light extinction: bext (Mm-1)

visual range = 3.91/bext

deciviews = 10 ln(bext/10 Mm-1)

20 Mm-1 = 200 km = 11 dv 100 Mm-1 = 40 km = 24 dv

• States submit SIPs every 10 years showing progress on improving visibility.

• Regional Haze goal is linear progress in reducing haze (in deciviews) on the worst 20% days and no degradation on the best 20% days.

• Uniform rate of progress (aka Glidepath) is defined as the slope of the line from baseline worst 20% deciviews to the natural deciviews.

• Model simulations did not show progress below the glidepath at some western Class I areas, but modeled progress was evaluated on the 20% worst days that included wildfires.

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Example of tracking progress:o At Sawtooth Wilderness Area episodic natural

events (e.g. wildfires), not anthropogenic emissions, dominate the 20% worst visibility days.

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2010

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Annual Avg.

5-yr avg.

Glidepath

Dec

ivie

ws

Sawtooth Wilderness Area, ID

2012 IMPROVE daily data (bext)

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Regional Haze Research Topics

• Improved estimates of natural visibility conditions:o Need site specific and seasonally varying estimates of natural haze.o Can we quantify contributions from wildfires and other extreme

episodic events?o How well can models estimate natural visibility conditions?o Can we use source apportionment modeling to distinguish domestic

versus international contributions to natural haze?

• Model evaluation – how accurately do models predict:o The species composition of PM2.5o Seasonal variations in speciated PM2.5o Ammonia limited chemical regimeso Source attribution and model response to emissions reductions

• International transport:o How reliable are model estimates for international transport?o Need evaluation of global scale chemistry-transport models.

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• Annual CAMx simulation with a 12 km grid over the western US.:o Model performance evaluation completed for ozone and speciated PM2.5. o CAMx APCA used for ozone source apportionment and CAMx PSAT used for PM2.5

source apportionment.

WestJumpAQMS 2008 Modeling

• PSAT source regions treat each of the western states, Eastern US, MX, CA, off-shore shipping and boundary conditions.

• Source Sector Categories:o Total anthropogenic emissions o Biogenic Emissionso 3 classes of fire emissions: Wild fires, Prescribed fires and agricultural burning

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Nested 36/12/4-km CAMx Domains

Lateral BC from MOZART Global Model

25 CAMx layers from the surface to the lower stratosphere.

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CAMx 2008 Monthly average fractional bias 36 &12 km grids compared to IMPROVE data, averaged for all sites. More detailed results at:

http://www.wrapair2.org/WestJumpAQMS.aspx

SO4 NO3

OC EC

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Summary of Aggregate Model Performance

• Model performance (using monthly averages) is similar for the 36, 12 and 4 km grids.

• Model is biased high for nitrate and EC, biased low for OC, and biased low for sulfate in spring & summer.

• However, we should also evaluate model performance at individual Class I areas and for individual days.

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MPE and PSAT results for Example class I Areas

• Rocky Mt National Park (ROMO)• Lassen Volcanic National Park• For each site, compare IMPROVE data and model performance.• Show PSAT model estimate of U.S. anthropogenic contribution.• Focus on the 20% worst visibility days:

– How does the seasonal distribution of the worst days compare for the model and the IMPROVE data?

– Does the seasonal distribution change for worst US contribution to have versus the total haze?

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Aero

sol E

xtinc

tion

(Mm

-1)

Modeled Aerosol Extinction (ROMO1)

WJ_ESeaSalt

WJ_ECM

WJ_ESoil

WJ_ELAC

WJ_EOMC

WJ_EAmmNO3

WJ_EAmmSO4

20% Worst Day

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MPE and PSAT results for Example class I Area: Rocky Mtn National Park

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Aero

sol E

xtinc

tion

(Mm

-1)

IMPROVE Aerosol Extinction (ROMO1)

IMP_ESeaSalt

IMP_ECM

IMP_ESoil

IMP_ELAC

IMP_EOMC

IMP_EAmmNO3

IMP_EAmmSO4

20% Worst Day

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Extin

ction

(Mm

-1)

Ammonium Nitrate Extinction: IMPROVE and CAMX (ROMO1)

IMP_EAmmNO3 WJ_EAmmNO3 11

CAMx performance for sulfate and nitrate: Rocky Mtn National Park

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Extin

ction

(Mm

-1)

Ammonium Sulfate Extinction: IMPROVE and CAMX (ROMO1)

IMP_EAmmSO4 WJ_EAmmSO4

Biased high in winter

Biased high in winter Biased low

in summer

Biased low in spring & summer

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Aero

sol E

xtinc

tion

(Mm

-1)

Modeled Aerosol Extinction (ROMO1)

WJ_ESeaSalt

WJ_ECM

WJ_ESoil

WJ_ELAC

WJ_EOMC

WJ_EAmmNO3

WJ_EAmmSO4

20% Worst Day

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CAMx PSAT anthropogenic extinction: ROMO

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Aero

sol E

xtinc

tion

(Mm

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Modeled Anthropogenic US Aerosol Extinction (ROMO1)

5ANT_US_ECM

5ANT_US_ESoil

5ANT_US_ELAC

5ANT_US_EPOA

5ANT_US_EAmmNO3

5ANT_US_EAmmSO4

20% Worst Day

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East

US AZ CA CO KS ID MT

OK

OR

WA

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ND SD NE

NV UT TX N

M

Cana

da

Mex

ico

Offs

hore

Extin

ction

(M

m-1

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Contributions to anthro sulfate: annual average

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ROMO: contributions to sulfate

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Modeled Total AmmSO4 Extinction and Anthropogenic US Extinction (ROMO1)WJ_EAmmSO4 5ANT_US_EAmmSO4

NAT_US0%

NAT_NonUS1%

AGF_US0%

AGF_NonUS0%

RXF_US0%

RXF_NonUS0%

WLF_US1%

WLF_NonUS0%

ANT_US43%

ANT_NonUS6%

BC49%

IC0%

Ammonium Sulfate (AmmSO4)Avg. Modeled Extinction by Source Category

(ROMO1)

NAT_US

NAT_NonUS

AGF_US

AGF_NonUS

RXF_US

RXF_NonUS

WLF_US

WLF_NonUS

ANT_US

ANT_NonUS

BC

IC

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Aero

sol E

xtinc

tion

(Mm

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Modeled Aerosol Extinction (LAVO1)

WJ_ESeaSalt

WJ_ECM

WJ_ESoil

WJ_ELAC

WJ_EOMC

WJ_EAmmNO3

WJ_EAmmSO4

20% Worst Day

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Lassen Volcanic National Park

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Aero

sol E

xtinc

tion

(Mm

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IMPROVE Aerosol Extinction (LAVO1)

IMP_ESeaSalt

IMP_ECM

IMP_ESoil

IMP_ELAC

IMP_EOMC

IMP_EAmmNO3

IMP_EAmmSO4

20% Worst Day

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Extin

ction

(Mm

-1)

Ammonium Sulfate Extinction: IMPROVE and CAMX (LAVO1)

IMP_EAmmSO4 WJ_EAmmSO4

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Extin

ction

(Mm

-1)

Ammonium Nitrate Extinction: IMPROVE and CAMX (LAVO1)

IMP_EAmmNO3 WJ_EAmmNO3

Biased high in winter

Biased high in winter and spring

Biased high for wildfire

Biased low in spring

Biased high for wildfire

CAMx performance for sulfate and nitrate: Lassen Volcanic National Park

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CAMx PSAT anthropogenic extinction: Lassen

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Aero

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(Mm

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Modeled Anthropogenic US Aerosol Extinction (LAVO1)

5ANT_US_ECM

5ANT_US_ESoil

5ANT_US_ELAC

5ANT_US_EPOA

5ANT_US_EAmmNO3

5ANT_US_EAmmSO4

20% Worst Day

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1/08 2/08 3/08 4/08 5/08 6/08 7/08 8/08 9/08 10/08 11/08 12/08

Aero

sol E

xtinc

tion

(Mm

-1)

Modeled Aerosol Extinction (LAVO1)

WJ_ESeaSalt

WJ_ECM

WJ_ESoil

WJ_ELAC

WJ_EOMC

WJ_EAmmNO3

WJ_EAmmSO4

20% Worst Day

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Lassen: source contributions to sulfate

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Aero

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(Mm

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Modeled Total AmmSO4 Extinction and Anthropogenic US Extinction (LAVO1)WJ_EAmmSO4 5ANT_US_EAmmSO4

NAT_US0%

NAT_NonUS2%AGF_US

0%AGF_NonUS

0%RXF_US

0%RXF_NonUS

0% WLF_US13%

WLF_NonUS0%

ANT_US9%

ANT_NonUS4%

BC72%

IC0%

Ammonium Sulfate (AmmSO4)Avg. Modeled Extinction by Source Category

(LAVO1)

NAT_US

NAT_NonUS

AGF_US

AGF_NonUS

RXF_US

RXF_NonUS

WLF_US

WLF_NonUS

ANT_US

ANT_NonUS

BC

IC

How much confidence do we have in global model estimates of boundary conditions?

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Next Steps• Global CTM evaluation and improvements:

o How well do global CTMs perform for natural, anthropogenic and fire emissions?o Include source apportionment info in global models and pass through to nested

regional models. o Estimates of future trends in international transport.o Need funding for global modeling improvements.

• Updated CAMx and CMAQ source apportionment simulations for more recent years: 2011, 2014:o Need improved treatment of NH3 emissions and fate. o Improved model seasonal performance for sulfate and nitrateo Improved treatment of episodic events: wild fires, wind blown dust.o Assessment of seasonal variability in natural visibility conditions.o Assessments of international transport (natural and anthropogenic contributions).o Need funding to support regional modeling studies.

• More research is needed to improve estimates of international transport:o Same global/regional modeling platforms that would be useful for regional haze

analysis can also be used to study background ozone in the western U.S.

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Acknowledgments

• ENVIRON performed CAMx PSAT simulations• Air Resource Specialists helped with data analysis