EFFICIENT CHARACTERIZATION OF UNCERTAINTY IN CONTROL STRATEGY IMPACT PREDICTIONS EFFICIENT CHARACTERIZATION OF UNCERTAINTY IN CONTROL STRATEGY IMPACT PREDICTIONS 8 th Annual CMAS Conference th October, 2009 Antara Digar and Daniel S. Cohan Rice University

AIR QUALITY PROBLEMS  Non-attainment of multiple pollutants (ozone & PM 2.5 ) in multiple regions across US

O3O3 O3O3 PM 2.5 NOx VOC SOx NH 3 PM Measure: Control Emission Issues: Controlling Multiple Pollutants  Nonlinear Chemistry How Much to Control ? Which Measures are most Effective? CO Pb Secondary Pollutants CHALLENGES 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 ATTAIN Add more controls EE States need to target additional pollutant reduction by adding more emission controls: Therefore, in order to attain  target  C extra = DVF - NAAQS CC CHECK  C  C extra CHECK  C  C extra Yes No Repeat Selection based on $$ & feasibility Model Implement Control Strategy

DRAWBACKS OF CURRENT PRACTICE

CAUSES 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. O 3 ) or Impact (e.g.  O 3 ) PHOTOCHEMICAL AIR QUALITY MODELS Emissions Chemistry Meteorology E or  E

Range of Output Pollutant Concentration (e.g. O 3 ) or Impact (e.g.  O 3 ) EFFECT OF PARAMETRIC UNCERTAINTY Uncertain Model Output Uncertain Emission Uncertain Chemistry Uncertain Boundary Conditions

METHODOLOGY FOR PREDICTING ‘  C’ IMPACT OF EMISSION REDUCTION MONTE CARLO Sensitivity coefficients from HDDM or finite difference Uncertainties of Input Parameter Output  C EMISSION REDUCTION  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 PARAMETERS ParameterUncertaintySigmaReference Domain-wide NOx  40% (1  ) 0.336a Domain-wide Anthropogenic VOC  40% (1  ) 0.336a Domain-wide Biogenic VOC  50% (1  ) 0.405a All Photolysis Rates Factor of 2 (2  ) 0.347b R(All VOCs+OH)  10% (1  ) 0.095a, b R(OH+NO2)  30% (2  ) 0.131c R(NO+O3)  10% (1  ) 0.095b Boundary Cond. O3  50% (2  ) 0.203a Boundary Cond. NOy Factor of 3 (2  ) 0.549a Note: Based on literature review ; All distributions are assumed to be log-normal References: a Deguillaume et al. 2007; b Hanna et al. 2001; c JPL 2006

UNCERTAINTY IN PREDICTING IMPACT OF CONTROL STRATEGY 12km grid resolution Uncertainty In Atlanta Ozone Attainment Modeling Summer Ozone Episode: May 29 – June 16, 2002 meteorology; Year 2009 emissions

ATTAINMENT PLANNING OPTIONS Targeted Ozone Reduction is Perfectly Known Targeted Ozone Reduction is Uncertain Option 1Option 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 FUNCTIONS Likelihood of attainment Targeted O 3 Reduction Perfectly Known Attainment Likelihood Function A Ozone Reduction (ppb) Attainment Non- Attainment Option 1: Targeted Ozone Reduction is Perfectly Known IF  O 3 ≥ 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) Ozone Reduction (ppb) Weather causes  3 ppb uncertainty in target Attainment Likelihood Function B Likelihood of attainment Attainment Non- Attainment

FINAL LIKELIHOOD OF ATTAINMENT Probability Density Attainment Likelihood Function A Attainment Likelihood Function B 75% considering fixed target 68% considering variable target Ozone Reduction (ppb) Ozone Impacts From Monte Carlo / Surrogate Model  Hypothetical Emission Reduction: Implement all identified Atlanta region NO x 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 SCENARIOS

LIKELIHOOD OF ATTAINMENT AS A FUNCTION OF CONTROL STRATEGY ASSUMING TARGET IS KNOWN ASSUMING TARGET IS UNCERTAIN Probability Plots for Different Scenarios

SUMMARY  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 ACTION  Explore the likelihood of ozone attainment under different available control scenarios  Extend to winter episode for PM 2.5  Assess which controls are most effective at improving attainment likelihood & health  Jointly consider uncertainty in cost, AQ sensitivity, and health estimates

ACKNOWLEDGEMENT U.S. EPA For funding our project (STAR Grant # R833665) GA EPD For providing emission data and baseline modeling CMAS

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