Di Tian , Georgia Department of Natural Resources Daniel Cohan , Rice University

Georgia Environmental Protection Division

Uncertainty Analysis of Ozone Formation and Emission Control Responses using High-order Sensitivities

Di Tian, Georgia Department of Natural Resources

Daniel Cohan, Rice University

Sergey Napelenok, Atmospheric Sciences Modeling Division, NOAA In partnership with the U.S. EPA

Yongtao Hu, Michael Chang, Armistead Russell, Georgia Institute of Technology

October 2, 2007

6th Annual CMAS Conference, October 1-3, 2007


Overview

Source oriented air quality modeling (AQM) Uncertainty analysis – Monte Carlo method Reduced form AQM based on first and high-order

sensitivities Case study

Projected air quality during 2007 in the southeastern U.S. Three base episodes: 8/1 – 8/15/1999, 8/11 – 8/19/2000, 7/5

– 7/17/2001 Uncertainties in simulated ozone concentrations Uncertainties in simulated ozone reduction from emission

controls


Source-oriented Air Quality Modeling

Meteorology (MM5): Surface, PBL, Cumulus, Explicit Moisture Scheme, one-way nesting, FDDA, etc.

Emission processing (SMOKE v2.1): Temporal, spatial, speciation Air quality model (CMAQ v4.3): Advection, diffusion, chemistry,

cloud, deposition, etc

Meteorology

Emissions

ERCKuCt

Ciii

i

)()(

Air Quality Model

Chemistry


How reliable is air quality modeling?

Stationary Point NOX

Mobile NOX

Biogenic VOC

Anthropogenic VOC

Ozone (grid, time-step)Emissions Concentrations

SourceOriented

AQMN Runs

Simulation 1Simulation 2Simulation 3...Simulation N

Probability distributionmean,std,cov=std/mean,C97.5, C50, C2.5

Uncertainty Analysis – Monte Carlo Method

Quantify uncertainties and provide information to policy makers

Computationally Expensive!!!

If one AQM run takes 10hr, 1000 runs x 10hr/run = 10,000hrs

For 10 types of emissions controls, 10,000hrs x 10 = 100,000hrs


Reduced-Form Ozone AQM (RFAQM)

Ozone sensitivities to different emission sources Provide detailed insight into complicated responses

• First and second-order sensitivities (Hakami, 2004 and Cohan, 2005) Vary in Space and time CMAQ-DDM: Decoupled Direct Method Calculate sensitivities/responses of gas and aerosol phase concentrations to

emission changes together with concentrations Computationally efficient Source apportionment and control strategy development

N

i

N

jjiji

N

iii SSCC

1 1

)2(,

1

)1(0 2

1

Nonlinear ozone response to emissions

Taylor expansions:


Air Quality Modeling - FAQS

Episode N MOC (ppbv) MB (ppbv) RMSE (ppbv) MNB (%) MNE (%)

1999 26032 67.6 -3.10 17.2 -2.69 20.3

2000 14104 63.6 -1.10 14.9 -0.51 19.1

2001 15783 59.1 1.40 12.6 4.19 17.4

Model Performance Statistics

Fall Line Air Quality Studyhttp://cure.eas.gatech.edu/faqs/index.html

Three Episodes: based on CART analysis8/1 – 8/15/19998/11 – 8/19/20007/5 – 7/17/2001

http://cure.eas.gatech.edu/faqs/index.html


Domain-wide daily NOX and VOC emissions during 2007 (tons per day)

1999 2007_1999

Projected Air Quality in 2007

NOX VOC

Sources 1999 2000 2001 1999 2000 2001

Stationary Point 2590 2190 2206 930

Stationary Area 468 3300

Mobile Onroad 2060 2000 2040 1260 1230 1270

Mobile Nonroad 1100 645

Biogenic 368 353 330 39900 37400 32000

Emissions in 2007:

•Growth factor: EGAS

•Controls: NOX SIP call, VOC RACT and MACT, etc


Emission Uncertainties

Expert elicitation (Hanna, 2001) Log-normal distributions 95% CI: (nominal / factor, nominal x factor)

• Point source: Factor of 1.5

• Other sources: Factor of 2

Non-road mobile emission uncertainties (Chi, 2004) NOX emissions: Factor of 1.6 VOC emissions: Factor of 1.5

Biogenic emission uncertainties using BEIS3 (Hanna, 2005)

Qualitative uncertainties NARSTO emission inventory assessment, 2005

E 2EE/2


40

60

80

100

120

140

160

8/3 8/4 8/5 8/6 8/7 8/8 8/9 8/10 8/11 8/12 8/13 8/14 8/15Date

Ozo

ne

con

cen

trat

ion

(p

pb

v)

Daily peak 8-hour ozone concentrations (ppb) and 95% CI

Downtown Atlanta, Georgia, base year 1999

Emission uncertainties, 95% CI

Uncertainties in Ozone Simulations (1)

Stationary point NOX emissions: factor of 1.5Non-point NOX emissions (onroad and nonroad mobile, area): factor of 2Biogenic VOC: factor of 2Anthropogenic VOC: factor of 2


Cut-off = 40 ppbv, error bar refers to variability in such ratios, 95% range

Uncertainties in Ozone Simulations (2)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

C50/norminal C97.5/C50 C50/C2.5

rati

o

1999 2000 2001

-20

0

20

40

60

80

100

120

140

160

180

40 60 80 100 120 140 160 180

nominal concentrations (ppbv)

conc

entr

atio

n pe

rcen

tiles

(pp

bv)

2.5th 50th 97.5th

1999

Summary by different base yearsScatter plots for 95% CI


Emission Control Responses

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~)1(

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CCE

Ozone concentrations when emissions are reduced by a factor femis

Ozone reduction (ppb)

Control efficiency (%)

Nonlinear ozone response to emissions

Ozone reduction per unit emissions (ppt/tons per day)

j

jjemisjemis

j P

SfSf

P

C~

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~21


Uncertainties in Emission Control Responses (1)

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N

iii SSCC

1 1

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1

)1(0 2

1

Nonlinear ozone response to emissions Random emissions Pj

1)1(

,1 ~~

j

jemisfj

j

jj

P

Pf

P

P

Ozone responses to controls of Atlanta point source emissions

0

0.5

11.5

2

2.5

3

3.54

4.5

5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1emission reduction

ozo

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n (

pp

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50th

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2.5

5

7.5

10

12.5

15

17.5

20

22.5

25


ozon

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duct

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(ppt

/tpd

)

nominal

50th

0

0.5

1

1.5

2

2.5

3

3.5

4


con

tro

l eff

icie

ncy

(%

)

nominal

50th

Ozone reduction (ppb) Ozone reduction (ppt/tpd) Control efficiency (%)

Peak 8-hr ozone, base year 1999, Downtown Atlanta, Georgia


Uncertainties in Emission Control Responses (2)

N

i

N

jjiji

N

iii SSCC

1 1

)2(,

1

)1(0 2

1

Nonlinear ozone response to emissions Random emissions Pj

1)1(

,1 ~~

j

jemisfj

j

jj

P

Pf

P

P

Ozone responses to controls of Atlanta onroad mobile source emissions

Ozone reduction (ppb) Ozone reduction (ppt/tpd) Control efficiency (%)

Peak 8-hr ozone, base year 1999, Downtown Atlanta, Georgia

-10

-5

0

5

10

15

20

25

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

emission reduction

ozo

ne

re

du

ctio

n (

pp

b)

nominal

50th

-20

-100

10

20

3040

50

60

7080

90

100

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

emission reduction

ozon

e re

duct

ion

(ppt

/tpd

)

nominal

50th

-10

-5

0

5

10

15

20

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

emission reduction

con

tro

l eff

icie

ncy

(%

)

nominal

50th


Ozone Reduction (ppb) base year 1999, cutoff = 80ppb

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

80 90 100 110 120 130 140 150

ozone concentration (ppb)

ozon

e re

duct

ion

(ppb

)

97.5th 50th 2.5th 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

80 90 100 110 120 130 140 150ozone concentration (ppb)

ozon

e re

duct

ion

(ppb

)

97.5th 50th 2.5th

-5

-4

-3

-2

-1

0

1

2

3

4

80 90 100 110 120 130 140 150


ozon

e re

duct

ion

(ppb

)

97.5th 50th 2.5th-5

-4

-3

-2

-1

0

1

2

3

80 90 100 110 120 130 140 150


ozon

e re

duct

ion

(ppb

)

97.5th 50th 2.5th

Atlanta Point

Atlanta Mobile OnroadAtlanta Mobile Nonroad

Outside Atlanta Point


Summary of Uncertainties in Emission Control Responses: base year 1999

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

0.1 0.2 0.3 0.4 0.5emission reduction

ozon

e re

duct

ion

(ppb

)Atlanta pointoutside Atlanta pointAtlanta mobile onroadAtlanta mobile nonroad

0.0

5.0

10.0

15.0

20.0

25.0


ozon

e re

duct

ion

(ppb

/tpd)

Atlanta pointoutside Atlanta pointAtlanta mobile onroadAtlanta mobile nonroad

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0


Con

trol

effi

cien

cy (

%)

Atlanta pointoutside Atlanta pointAtlanta mobile onroadAtlanta mobile nonroad

Ozone reduction (ppb) Ozone reduction (ppt/tpd)

Control efficiency (%)

0

2

4

6

8

10

12

14


Neg

ativ

e re

duct

ion

(%)

Percents of 95% CI overlapping 0


Summary

RFAQM developed using first and second order ozone sensitivities

Computationally efficient for detailed uncertainty analysis Uncertainties in ozone simulations

Easily redo for different emission uncertainties Uncertainties in emission control responses

Don’t need to rerun AQM for different emission controls Large nonlinear relationships of ozone to mobile source emissions Emission controls can lead to increased ozone concentrations

Future work Incorporate cost-benefit analysis $$$, evaluate their associated

uncertainties

Documents

Di Tian , Georgia Department of Natural Resources Daniel Cohan , Rice University