Inverse Modeling and Attainment Analysis for Improved ... · CMAQ adjoint Compile emissions,...

Inverse Modeling and Attainment Analysis for Improved Decision Support of PM2.5 Air

Quality Regulations Daven K. Henze, Matt Turner, University of Colorado Boulder Patrick L. Kinney, Darby Jack, Ying Li, Columbia University Loretta Mickley, Daniel Jacob, Harvard University Drew Shindell, NASA GISS Randall Martin, Dalhousie University Amir Hakami, Carleton University

Partners at US EPA: AMAD - Sergey Napelenok - Robert Pinder - Alice Gilliland - S.T. Rao Emissions Group - Doug Solomon - Sally Dombrowski OAQPS - Bryan Hubbell

Project overview

Earth System Models drivers: - GISS Model E - GEOS-Chem - WRF air quality predictions: - CMAQ - GEOS-Chem

Data Assimilation - CMAQ adjoint - GEOS-Chem adjoint

Earth Observations remote sensing: - SCIAMACHY / OMI

NO2

- TES NH3

in situ: - IMPROVE, STN, CASTNet, AMoN, CAMNet, NADP, CalNex

Predictions / Products - Emissions - Control Strategies

Development of effective PM2.5 control strategies hindered by: - uncertainty in knowledge of sources - lack of information relating emissions from specific sectors, locations and species, to health endpoints. Here we are using the tools outlined on the left to: - constrain estimates of PM2.5 sources - develop improved emissions mitigation strategies with detailed source – receptor analysis

- provide new tools to the EPA for long term adoption of these techniques.

Implement CMAQ aerosol data assimilation capabilities

Schedule and Milestones (MS)

Develop constraints on PM2.5 precursor emissions

Source attribution of PM2.5 health impacts

MS: Provide constraints on NH3 seasonality and NOx mobile sources to NEI

MS: Source attribution to NAAQS RIA

Year 2 Year 1 Year 3 Year 4

Climate change impacts on AQ

MS: Climate impacts on control strategies.

MS: Public release of CMAQ adjoint

Compile emissions, meteorology, process satellite data

Started fall 2009

Milestone #2: Constraining emissions of NH3

X - 32 ZHU ET AL.: INVERSE MODELING RESULTS OF NH3 EMISSIONS

BALES ET AL.: SHORT TITLE X - 3

(a)

(b)

Figure 1. Global figure caption (a) describes the first subfigure; (b) describes the second subfigure;

m = 1.22 r2 = 0.94

2 4 6 8 10 12 14 16

x 106

2

4

6

8

10

12

14

16

x 106

True

Mo

de

led

r2 =1

m =1

r2 =0.935

m =1.22

(a)

(b) (c)


3. Actual application

For an application with real data, we will use TESobservations throughout 2009 and compare these tomodel estimates from the GEOS-Chem chemical trans-port model in a global 2� ⇥ 2.5� simulation.

Acknowledgments. (Text here)

ReferencesDubovik, O., T. Lapyonok, Y. J. Kaufman, M. Chin, P. Ginoux,

R. A. Kahn, and A. Sinyuk (2008), Retrieving global aerosol

sources from satellites using inverse modeling, Atmos. Chem.Phys., 8 (2), 209–250.

Elbern, H., H. Schmidt, O. Talagrand, and A. Ebel (2000), 4D-

varational data assimilation with an adjoint air quality model

for emission analysis, Environ. Modell. Softw., 15, 539–548.

(a)


(a)

(b)


(a)

2 4 6 8 10 12 14 16

x 106

2

4

6

8

10

12

14

16

x 106

True

Mo

de

led

r2 =1

m =1

r2 =0.865

m =1.21

m = 1.21 r2 = 0.87

(b)

(c)

Figure 2. Global figure caption (a) describes the first subfigure; (b) describes the second subfigure; (c) describes thesecond subfigure;






sources from satellites using inverse modeling, Atmos. Chem.

Phys., 8 (2), 209–250.

(b)


(a)

(b)


(a)

(b)

2 4 6 8 10 12 14 16

x 106

2

4

6

8

10

12

14

16

x 106

True

Mo

de

led

r2 =1

m =1

r2 =0.992

m =1.03

m = 1.03 r2 = 0.99

(c)







sources from satellites using inverse modeling, Atmos. Chem.Phys., 8 (2), 209–250.

Elbern, H., H. Schmidt, O. Talagrand, and A. Ebel (2000), 4D-

varational data assimilation with an adjoint air quality model

for emission analysis, Environ. Modell. Softw., 15, 539–548.

(c)

Figure 4. Tests for the possible impacts of inversion error, retrieval bias and measure-

ment error: (a) retrieval algorithm with a polluted profile as an initial guess; (b) modified

retrieval algorithm with a moderate profile as the initial guess; (c) model profiles from

the true model were ascribed error of the same size as the measurement error.

April

July

October

Initial Optimized ln(Optimized/Initial)

0 2.33 4.67 7.00 [106 kg] -2.00 -0.67 0.67 2.00 [unitless]

Figure 5. NH3

emissions from GEOS-Chem before and after the assimilation

D R A F T July 23, 2012, 11:33am D R A F T

Large uncertainties - seasonality - fertilizer vs animals - primary sources vs redistribution

Impacts of NH3 - governs 10-30% of PM2.5 mass - aerosol water and phase --> clouds & climate - deposition of reactive nitrogen

EPA’s national emission inventory (NEI) uses inverse modeling to constrain seasonality (Gilliland et al., 2006) Room to improve upon this using new data (TES, AMoN) and new inverse models (Shephard et al., 2011; Pinder et al., 2011; Zhu et al., submitted, Paulot et al., in prep)

Constraints from TES

Range of total US NH3 emissions Park (2004) Paulot Paulot

Zhu et al., submitted

Paulot et al., in prep

+80%

+57%

+33%

Milestone #2: Constraining emissions of NH3 ZHU ET AL.: INVERSE MODELING RESULTS OF NH3 EMISSIONS X - 35

April

July

October

GC

(ppb)

GC

(ppb)

AMoN (ppb) 0 1 2 3 4 5 6

01

23

45

6

Observation NH3, (ppb)

GE

OS!

Ch

em

, N

H3

, (p

pb

)

October

y =

0.494

+

0.564

x

0 1 2 3 4 5 6

01

23

45

6


GE

OS!

Ch

em

, O

ptim

ize

d,

NH

3,

(pp

b)

October

y =

0.455

+

0.999

x

0 1 2 3 4 5 6

01

23

45

6


GE

OS!

Ch

em

, N

H3

, (p

pb

)

October

y =

0.494

+

0.564

x

0 1 2 3 4 5 6

01

23

45

6


GE

OS!

Ch

em

, O

ptim

ize

d,

NH

3,

(pp

b)

October

y =

0.455

+

0.999

x

R2=0.545 RMSE=0.952

NMB=-0.138

R2=0.693 RMSE=0.862

NMB=0.166

0 2 4 6 8 10 12

02

46

810

12


GE

OS!

Chem

, N

H3, (p

pb)

July

y =

1.5

+

0.51

x

0 2 4 6 8 10 12

02

46

810

12


GE

OS!

Chem

, O

ptim

ized, N

H3, (p

pb)

July

y =

2.22

+

1

x

0 2 4 6 8 10 12

02

46

810

12


GE

OS!

Chem

, N

H3, (p

pb)

July

y =

1.5

+

0.51

x

0 2 4 6 8 10 12

02

46

810

12


GE

OS!

Chem

, O

ptim

ized, N

H3, (p

pb)

July

y =

2.22

+

1

x

R2=0.281 RMSE=1.990

NMB=-0.045

R2=0.365 RMSE=3.534

NMB=0.659

0 2 4 6 8 10

02

46

81

0


GE

OS!

Ch

em

, N

H3

, (p

pb

)

April

y =

0.215

+

0.225

x

0 2 4 6 8 10

02

46

81

0


GE

OS!

Ch

em

, O

ptim

ize

d,

NH

3,

(pp

b)

April

y =

!0.189

+

1.02

x

0 2 4 6 8 10

02

46

810


GE

OS!

Chem

, N

H3, (p

pb)

April

y =

0.215

+

0.225

x

0 2 4 6 8 10

02

46

810


GE

OS!

Chem

, O

ptim

ized, N

H3, (p

pb)

April

y =

!0.189

+

1.02

x

R2=0.497 RMSE=1.834

NMB=-0.069

R2=0.406 RMSE=2.107

NMB=-0.678

Optimized Initial

Figure 8. Comparison of GEOS-Chem NH3

concentrations with observations from

AMoN sites before and after the assimilation. The square of the correlation coe�cient

(R2), root mean square error (RMSE), and normalized mean bias (NMB) are shown.

Black solid lines are regressions. Grey dashed lines are 1:1.

D R A F T July 23, 2012, 11:33am D R A F T

Constraints provided by TES lead to improved estimates of NH3 measured at AMoN sites in April and October (right)

Constraints provided by TES shift the seasonality towards higher emissions in April, consistent with updates to CMAQ NH3 bidirectional flux model (left)

CMAQ

CMAQ bidi

GC initial

GC opt

Jeong et al., 2011 AGU

Zhu et al., submitted

Milestone #1: Online observation operator for assimilating NO2 into CMAQ

1. compare vertical columns

2. compare slant columns

1.  Leads to more rigorous slant vertical column comparison 2.  Emphasizes altitudes where OMI is most sensitive

OMI DOMINO CMAQ

Pye et al., 2010

+

GEOS-Chem / CMAQ annual BC baseline mortalities (BenMAP*)

[ug/m3]

[mortalities over 30 yr old]

Milestone #3: Source attribution of BC aerosol related mortality Question: How much do emissions from each location / species / sector contribute to mortality? Approach:

+ response factor (0.005827) = 14,000 deaths Apportion sources contributing to these mortalities using adjoints, and use tool for EPA’s Regulatory Impact Analysis (RIA)

*EPA’s health impact model

Adjoint modeling for source-receptor analysis:

Sensitivity of all model concentrations to one model source

t0 Perturbation at source region

Forward

tn

Changes of concentration

Forward Model (source-oriented)




Forward

tn


Forward Model (source-oriented) Sensitivity of model concentration in specific location to many model sources

Adjoint Model (receptor-oriented)

t0 area of possible origin

adjoint

tn

Concentration at the receptor



Forward

tn


Forward Model (source-oriented) Adjoint Model (receptor-oriented)

t0 area of possible origin

adjoint

tn

Concentration at the receptor

example responses: national exceedence, statewide mortality,...


Sensitivity of model response over any region to many model sources

All sectors Fossil Fuel (12,600)

Biomass Burning (200) Biofuel (900)

Annual mortalities from BC emissions in each grid cell

Milestone #3: Source attribution of BC aerosol related mortality

Receptor modeling allows us to identify states where emissions reductions would have the greatest benefit

% contrib. to US mort. - % US emissions Disparities between state contributions to national emissions vs contributions to national health impacts


Emissions 3

2

1

0

[Gg BC/month]

Contributions to mortality 1200

800

400

0

[mortality]

preliminary results (April)

smaller ß benefit à larger per BC reduction

-4 4 2 -2 0

[mortality]


Contributions of emissions to mortality in Pennsylvania:

-  A significant fraction of mortality in PA is owing to emissions outside PA - Important in light of recent court ruling against interstate controls

preliminary results (April)

note: preliminary analysis, complete annual average results in progress

Milestone #3: source attribution of PM2.5 related mortality

From fossil fuel SO2 (26,300) From fossil fuel NOx (20,300)

Source contributions to national mortality from PM2.5 - total estimated to be 103,000 / yr compare to 130,000 / yr from EPA study (Fan et al., 2012)) - contributions by location / sector / species:

Analysis valuable for determining health impacts of future emissions control strategies, particularly jointly addressing PM2.5 and O3

Summary of Progress

MS #1: New inverse modeling and source apportionment tools: - aerosol microphysical adjoint (Turner et al., 2011a, 2011b, posters) - ANISORROPIA thermodynamic adjoint (Capps et al., ACP, 2012) - online OMI NO2 observation operator (Pye et al., 2010, poster) - GEOS-Chem nested adjoint model MS #2: Remote sensing constraints on emissions: - NEI underestimates NH3 sources (Zhu et al., submitted to JGR) - TES imparts a different seasonality than wet deposition for NH3 sources - Transport & chemistry important for constraining even very short-lived species (Turner et al., GRL, 2012) MS #3: Source attribution of health impacts: - with GEOS-Chem and CMAQ for BC aerosol - for inorganic PM2.5 with GEOS-Chem MS #4: Accounting for interactions between AQ regulations and climate - source attribution of aerosol indirect effects from specific precursors (Karydis, submitted to GRL) - drive GEOS-Chem simulations with higher resolution AR5 climatology

$ Budget $ 1,200,000 over four years

Budget update from year 3 report (08/01/2009 – 07/31/2012):

• Includes funding from year 4, expenses up through year 3 • Uncosted funds largely owing to - slow processing of subcontract to Harvard, Columbia - challenges hiring / maintaining postdocs jointly at CU Boulder / EPA

0" 50" 100" 150" 200" 250" 300" 350" 400"

Facili-es"and"Admin"

Wages,"Benefits,"Tui-on"

Opera-ng"Expenses"

Travel"

Fixed"Assests"

Columbia"Subcontract"

Harvard"Subcontract"

$"(thousand)"

Spent"(Year"3)"Encumbered"Budget"(4"Years)"

Risks and Challenges

Management: - challenge to hire / maintain postdocs at EPA RTP through CU - initial plan was joint CU Boulder / EPA postdoc - significant bureaucratic barriers for internationals at EPA - US postdocs soon hired away to permanent positions - Henze’s group just getting started in 2009 - building group from scratch took time - building computer lab postponed 1 year owing to campus delays Technical: - CMAQ adjoint model still being finalized - TES NH3 data challenging to utilize - TES / OMI lifetime (consider CrIS, TROPOMI, GEO-CAPE for future) - Driving GEOS-Chem with high resolution GISS climatology difficult Scheduling: - NAAQS review process at EPA a constantly moving target - challenge to make our applications relevant to their needs

Applications Readiness Level Present

ARL 4: Components technically integrated -  CMAQ aerosol aerosol verified on a component-by-component basis -  GEOS-Chem aerosol adjoint on North American nested grid -  EPA data sets (BenMAPs) used to calculate mortality owing to PM2.5 exposure integrated into both, agree with BenMAPs to within >99%. ARL 4: Organizational challenges identified and managed: -  Farhan Akhtar, Shannon Capps trained with these tools, currently or soon to be at EPA offices to facilitate long-term adoption

Goal: ARL 6 by end of 2012, ARL 8 by end of 2013 ARL 5: Prototype system: to be released this fall ARL 5: Potential determined and articulated: - presentation at CMAS 2012, build on 2011 workshop & town hall event ARL 6: Prototype system beta-tested in a simulated operational environment: - already ongoing

References

Turner, M., D. K. Henze, A. Hakami, S. Zhao, J. Resler, G. Carmichael, C. Stanier, J. Baek, P. Saide, A. Sandu, A. Russell, G. Jeong, A. Nenes, S. Capps, P. Percell, R. W. Pinder, S. Napelenok, H. Pye, J. O. Bash, T. Chai, D. Byun, Aerosols Processes in the CMAQ Adjoint, International Aerosol Modeling Algorithms Conference, Davis, CA, Nov 30 - Dec 2, 2011a. Turner, M., D. K. Henze, A. Hakami, S. Zhao, J. Resler, G. Carmichael, C. Stanier, J. Baek, P. Saide, A. Sandu, A. Russell, G. Jeong, A. Nenes, S. Capps, P. Percell, R. W. Pinder, S. Napelenok, H. Pye, J. O. Bash, T. Chai, D. Byun, High Resolution Source Attribution of PM Health Impacts with the CMAQ Adjoint Model, 10th Annual CMAS Conference, Chapel Hill, NC, Oct 24-26, 2011b. Capps, S. L., D. K. Henze, A. G. Russell, and A. Nenes, Quantifying relative contributions of global emissions to PM2.5 air quality attainment in the U.S, A54C-07, or presentation, AGU Fall Meeting, San Francisco, CA, 5-9 Dec, 2011. Best Student Poster Award. Pye, H. O. T., S. L. Napelenok, R. W. Pinder, D. K. Henze, R. V. Martin, and K. W. Appel, Evaluation of CMAQ NO2 predictions over the U.S. using ground-based and satellite observations, 5th International GEOS-Chem Conference, Cambridge, MA, May 2-5, 2011. Capps, S. L., D. K. Henze, A. Hakami, A. G. Russell, and A. Nenes, ANISORROPIA: the adjoint of the aerosol thermodynamic model ISORROPIA, Atmos. Chem. Phys., 12, 527-543, 2012. Turner, A., D. K. Henze, R. V. Martin, and A. Hakami, Modeled source influences on column concentrations of short-lived species, Geophys. Res. Lett., 39, L12806, doi:10.1029/2012GL051832, 2012. Karydis, V. A., S. L. Capps, R. H. Moore, A. Russell, D. K. Henze, and A. Nenes, Using a global aerosol model adjoint to unravel the footprint of spatially-distributed emissions on cloud droplet number and cloud albedo, submitted to GRL, 2012. L. Zhu, D. K. Henze, K. E. Cady-Pereira, M. W. Shephard, M. Luo, R. W. Pinder, J. O. Bash, G. Jeong, Constraining U.S. ammonia emissions using TES remote sensing observations and the GEOS-Chem adjoint model, submitted to JGR, 2012.

bonus slides!

1 2 3 4 5 6 7 8 9 10 11 120

200

400

600

800

1000

1200

1400

1600M

orta

litie

s

month

fossil fuelbio fuelbiomass burn

Monthly mortalities owing to BC emissions from entire domain

Technical results: source attribution of carbonaceous aerosol related mortality

Interpretation of adjoint model results Example: - Value of 35 in grid box over your county means? --> emissions in your county contributed to 35 premature mortalities - where were those 35 mortalities? --> Adjoint only tells us they were somewhere in the

entire US.


mortality per ug/m3


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