efficient characterization of uncertainty in control strategy impact predictions efficient...
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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: [email protected] on to http://uncertainty.rice.edu/