EFFICIENT CHARACTERIZATION OF EFFICIENT CHARACTERIZATION OF UNCERTAINTY IN CONTROL STRATEGY IMPACT UNCERTAINTY IN CONTROL STRATEGY IMPACT

PREDICTIONS PREDICTIONS

8th Annual CMAS Conference19-21th October, 2009

Antara Digar and Daniel S. CohanRice University

AIR QUALITY PROBLEMSAIR QUALITY PROBLEMS Non-attainment of multiple pollutants (ozone & PM2.5) in

multiple regions across US

O3O3 PM2.5PM2.5

NOxNOx VOCVOC SOxSOx NH3NH3 PMPM

Measure: Control Emission

Issues:Issues:

Controlling Multiple Pollutants Nonlinear ChemistryHow Much to Control ?

Which Measures are most Effective?

COCO PbPb

Secondary

Pollutants

CHALLENGES IN PLANNING ATTAINMENTCHALLENGES IN PLANNING ATTAINMENT

The Attainment Limbo

Does(DVF = Base DV * RRF)

attain EPA standard?

Monitors measure pollution levels

“Base Design Value”

Model predicts relative reduction

Base

Future

“RRF” = Future/Base

YES: Attainment

demonstrated

NO: Add more controls

WHAT IF ADDITIONAL CONTROLS NEEDED TO ATTAINWHAT IF ADDITIONAL CONTROLS NEEDED TO ATTAIN

Add more controlsE

States need to target additional pollutant reduction by adding more emission controls: Therefore, in order to attain target Cextra= DVF - NAAQS

CCHECK

C Cextra

CHECK C Cextra

Yes

No

Repeat

Selection based on $$ & feasibility

ModelModelImplement Implement

Control Control StrategyStrategy

DRAWBACKS OF CURRENT PRACTICEDRAWBACKS OF CURRENT PRACTICE

CAUSES OF UNCERTAINTY IN PAQMCAUSES OF UNCERTAINTY IN PAQM

Due to imperfections in the model’s numerical

representations of atmospheric chemistry

and dynamics Emission and Reaction Rates

Boundary Conditions

Meteorology

Due to error in model input parameters

Output Pollutant Concentration (e.g. O3) or Impact (e.g.

O3)

PHOTOCHEMICAL AIR QUALITY MODELSPHOTOCHEMICAL AIR QUALITY MODELS

Emissions

Chemistry

Meteorology

E or E

Range of Output Pollutant

Concentration (e.g. O3) or

Impact (e.g. O3)

EFFECT OF PARAMETRIC UNCERTAINTYEFFECT OF PARAMETRIC UNCERTAINTY

-1 0 1 2 3 4 5 60

1

2

3

4

5

6

7

8x 10

4

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.60

0.5

1

1.5

2

2.5

3

3.5

4x 10

4

-1 -0.5 0 0.5 1 1.5 20

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 10

4

-0.015 -0.01 -0.005 0 0.005 0.01 0.015 0.02 0.0250

20

40

60

80

100

120

140

160

Uncertain Model Output

UncertainEmission

Uncertain Chemistry

Uncertain Boundary Conditions

METHODOLOGY METHODOLOGY FOR PREDICTING ‘FOR PREDICTING ‘C’ IMPACT OF EMISSION REDUCTIONC’ IMPACT OF EMISSION REDUCTION

MONTE CARLO

Sensitivity coefficients from HDDM or finite difference

Uncertainties of Input

Parameter

Output C

EMISSION REDUCTION

-1 0 1 2 3 4 5 60

1

2

3

4

5

6

7

8x 10

4

-1 0 1 2 3 4 5 60

1

2

3

4

5

6

7

8x 10

4

Pick an emission reduction scenario

Characterize probability distributions of uncertain input parameters

Compute sensitivity coefficients to emissions and uncertain inputs to create surrogate model equations

Apply randomly sampled (Monte Carlo) input parameters in surrogate model to yield probability distribution of ΔC

UNCERTAINTY IN INPUT PARAMETERSUNCERTAINTY IN INPUT PARAMETERSParameter Uncertainty Sigma Reference

Domain-wide NOx 40% (1) 0.336 a

Domain-wide Anthropogenic VOC 40% (1) 0.336 a

Domain-wide Biogenic VOC 50% (1) 0.405 a

All Photolysis Rates Factor of 2 (2) 0.347 b

R(All VOCs+OH) 10% (1) 0.095 a, b

R(OH+NO2) 30% (2) 0.131 c

R(NO+O3) 10% (1) 0.095 b

Boundary Cond. O3 50% (2) 0.203 a

Boundary Cond. NOy Factor of 3 (2) 0.549 a

Note:• Based on literature review ; All distributions are assumed to be log-normal

References: aDeguillaume et al. 2007; bHanna et al. 2001; cJPL 2006

UNCERTAINTY IN PREDICTING IMPACT OF CONTROL UNCERTAINTY IN PREDICTING IMPACT OF CONTROL STRATEGY STRATEGY

12km grid resolution12km grid resolution

Uncertainty In Atlanta Ozone Uncertainty In Atlanta Ozone Attainment ModelingAttainment Modeling

Summer Ozone Episode: Summer Ozone Episode:

May 29 – June 16, May 29 – June 16,

2002 meteorology; 2002 meteorology;

Year 2009 emissionsYear 2009 emissions

ATTAINMENT PLANNING OPTIONSATTAINMENT PLANNING OPTIONS

Targeted Ozone Reduction is

Perfectly Known

Targeted Ozone Reduction is

Uncertain

Option 1 Option 2

‘Likelihood of Attainment’ when

CASE STUDY: Ozone attainment at worst Atlanta monitor (Confederate Avenue),

accounting for parametric uncertainty

Choose your own adventure

ATTAINMENT LIKELIHOOD FUNCTIONSATTAINMENT LIKELIHOOD FUNCTIONS

Targeted O3 Reduction Perfectly Known

Attainment Likelihood Function A

-6 -4 -2 0 2 4 6 80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ozone Reduction (ppb)

Attainment

Non- Attainment

Option 1:

Targeted Ozone Reduction is Perfectly Known

IF O3 ≥ Targeted Reduction,

THEN Attainment,

ELSE Non-Attainment

Option 2:

Targeted Ozone Reduction Uncertain (due to uncertain weather/meteorology)

Suppose, future weather causes

Actual Target = Target ± 3 ppb

(assume normally distributed)

-6 -4 -2 0 2 4 6 80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ozone Reduction (ppb)

Weather causes 3 ppb uncertainty in target

Attainment Likelihood Function B

Attainment

Non- Attainment

FINAL LIKELIHOOD OF ATTAINMENTFINAL LIKELIHOOD OF ATTAINMENT

-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 100

0.005

0.01

0.015

0.02

Attainment Likelihood Function A

Attainment Likelihood Function A

Attainment Likelihood Function B

Attainment Likelihood Function B

75% considering fixed target

68% considering

variable targetOzone Reduction (ppb)

Ozone Impacts From Monte Carlo / Surrogate Model

Hypothetical Emission Reduction: Implement all identified Atlanta region NOx control options, and replace Plant McDonough with natural gas

Uncertainties Considered: Domain-wide emission rates, reaction rates, and boundary conditions

Output: Probability distribution of ΔC for 8-hour ozone at Confederate Avenue monitor, for days exceeding ozone threshold

COMPARISON OF TWO SCENARIOSCOMPARISON OF TWO SCENARIOS

LIKELIHOOD OF ATTAINMENT AS A FUNCTION OF LIKELIHOOD OF ATTAINMENT AS A FUNCTION OF CONTROL STRATEGYCONTROL STRATEGY

ASSUMING TARGET IS KNOWN ASSUMING TARGET IS UNCERTAIN

Probability Plots for Different Scenarios

SUMMARYSUMMARY Uncertainty is typically neglected in modeling impact of SIP

control measures

Efficient new method to characterize probabilistic impact of controls under parametric uncertainty

Demonstration for Atlanta ozone case study

Can flexibly apply with alternate control amounts and input uncertainties

Can compute likelihood of attaining a known or uncertain pollution reduction target

Likelihood of attainment is far more responsive to amount of emission control if the target is known (fixed)

FUTURE PLAN OF ACTIONFUTURE PLAN OF ACTION Explore the likelihood of ozone attainment under different

available control scenarios

Extend to winter episode for PM2.5

Assess which controls are most effective at improving attainment likelihood & health

Jointly consider uncertainty in cost, AQ sensitivity, and health estimates

ACKNOWLEDGEMENT ACKNOWLEDGEMENT U.S. EPA

For funding our project (STAR Grant # R833665)

GA EPDFor providing emission data and baseline modeling

CMAS

For further information & updates of our project

Contact: antara@rice.eduLog on to http://uncertainty.rice.edu/