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INCORPORATING UNCERTAINTY INTO AIR QUALITY MODELING & PLANNING – A CASE STUDY FOR GEORGIA

7 th Annual CMAS Conference 6-8 th October, 2008. INCORPORATING UNCERTAINTY INTO AIR QUALITY MODELING & PLANNING – A CASE STUDY FOR GEORGIA. Antara Digar, Daniel S. Cohan, Dennis Cox, Wei Zhou Rice University & Maudood Khan, James Boylan Georgia Environmental Protection Division.

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INCORPORATING UNCERTAINTY INTO AIR QUALITY MODELING & PLANNING – A CASE STUDY FOR GEORGIA

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  1. 7th Annual CMAS Conference 6-8th October, 2008 INCORPORATING UNCERTAINTY INTO AIR QUALITY MODELING & PLANNING – A CASE STUDY FOR GEORGIA Antara Digar, Daniel S. Cohan, Dennis Cox, Wei Zhou Rice University & Maudood Khan, James Boylan Georgia Environmental Protection Division

  2. Introducing the Project This project is funded by U.S. EPA – Science To Achieve Results (STAR) Program Grant # R833665 JAMES BOYLAN MICHELLE S. BERGIN DANIEL S. COHAN (PI) DENNIS COX ANTARA DIGAR MICHELLE BELL ROBYN WILSON

  3. Background & Objective Measure: Control Emission NOx VOC SOx NH3 PM PM2.5 O3 Non-attainment In U.S. Controlling Multiple Pollutants How Much to Control ? Which Measure is Effective? Scientists & Air Quality Modelers have come up with techniques to estimate Sensitivity of O3 and PM2.5 to their precursor emissions But in reality the model inputs are sometimes uncertain GOAL: Estimate this Uncertainty Uncertainty in Model Input causes Uncertainty in O3 & PM2.5 Sensitivities

  4. Model Used Achieving the Goal CMAQ - High-order Decoupled Direct Method • H- High-order sensitivity analysis • N- Nonlinear relationship between secondary pollutants and its precursor emission • N- Non-liner sensitivity model can be used to determine the impact of uncertain Emission inventory, Photochemical rate constants, Deposition velocities on O3 and PM2.5 sensitivity to their precursor emission control HDDM determines slope at any point by calculating the local derivative at that point C A CA CB B E -E ‘E’ denotes precursor emission; ‘C’ denotes secondary pollutant concentration Source: Hakami et. al. 2003; Cohan et. al. 2005

  5. Introducing Uncertainty Effect of Uncertain Input Parameters Ozone Modeled value Actual value A A CA Actual value Modeled value B E E* A* CB -D EA Effect of Control Strategy (Emission Reduction) High-or Self Sensitivity Sensitivity to parameter j if j is uncertain: Cross Sensitivity EVOC EB Sensitivity to parameter j if k  j is uncertain: EA Source: Cohan et. al., 2005

  6. HDDM in Selection of Control Strategy Control measures • % reduction in regional emission (NOx, VOC, NH3, etc.) • Specific amount of reduction at power plant (NOx, SOx) Pollutant Levels & Exposure Metrics • O3 at worst monitor • O3 population exposure • PM2.5 at worst monitor • PM2.5 population exposure • Uncertainty in emission inventory • Uncertainty in reaction rate constants • Uncertainty in deposition velocities Uncertainties

  7. Example Case Control measures Pollutant Levels & Exposure Metrics • % reduction in regional NOx emission • Specific amount of reduction at power plant • O3 at worst monitor • O3 at Atlanta • PM2.5 at worst monitor • PM2.5 population exposure • Uncertainty in emission – self/cross (NOx, VOC, etc.) • Uncertainty in reaction rate constants • Uncertainty in deposition velocities Uncertainties

  8. Sensitivity of O3 to precursor emission = f(Ei, Rj, Vdk, …) Our Approach

  9. Methodology Sensitivity of secondary pollutant to any parameter j given both j and any other input parameter k  j is also uncertain: SURROGATE MODEL CMAQ-HDDM MONTE CARLO Input Parameter • Sensitivity estimated by CMAQ-HDDM • PDFs for input parameters from literature • Monte Carlo Sampling • Develop output PDFs using Surrogate Model • Characterize uncertainty in output sensitivity, S* Output Sensitivity

  10. Applying to Georgia – A Case Study(May 30 – June 06, 2009) ALGA 12km domain

  11. Accuracy of CMAQ-HDDM Sensitivity of Ozone to NOx Emission Impact of Uncertainty in ENOx R2 > 0.99 Limitation: CMAQ-HDDM is not yet capable of handling high-order PM sensitivities, hence BF will be used for such cases (Self Sens) Impact of Uncertainty in R(NO2 +OH) (Cross Sens) Brute Force HDDM

  12. EVOC ENOX ESOX First Scenario: ENH3 EPM Uncertain Emission Inventory

  13. Case 1A: Self sensitivity Control measures Pollutant Levels & Exposure Metrics Reduction in NOx emission • Atlanta O3 • Scherer O3 NOx emission uncertain by ±30% Uncertainties

  14. If NOx emission is larger than expected, O3 _ENOx generally increases but some locations have NOx disbenefit Impact of Uncertainty in ENOx Sensitivity of O3 to Atlanta NOx Sensitivity of O3 to Scherer NOx

  15. Case 1B: Cross Sensitivity Control measures Pollutant Levels & Exposure Metrics Reduction in VOC emission • Atlanta O3 • Scherer O3 NOx emission uncertain by ±30% Uncertainties

  16. If ENOx is larger than expected, sensitivity of O3 to EVOC is slightly increased Impact of Uncertainty in ENOx Sensitivity of O3 to Atlanta VOC Sensitivity of O3 to Scherer VOC

  17. HRVOCs+NO3products HRVOCs+O3products O3+NONO2 NO2+hNO+O NO2+NO3N2O5 Second Scenario: NO2+OHHNO3 HRVOCs+OHproducts Uncertain reaction Rate

  18. Case 2: Cross Sensitivity Control measures Pollutant Levels & Exposure Metrics Reduction in NOx emission • Atlanta O3 • Scherer O3 R(NO2+OH) uncertain by ±30% Uncertainties

  19. If R(NO2+OH  HNO3) is larger than expected, sensitivity of O3 to ENOx decreases Impact of Uncertainty in R(NO2+OH) Sensitivity of O3 to Atlanta NOx Sensitivity of O3 to Scherer NOx

  20. Preliminary Findings • Uncertain NOx emissions inventory: • A larger NOx inventory generally increases the sensitivity of Ozone to ENOx, however some locations show NOx disbenefit • A larger NOx inventory increases the sensitivity of Ozone to EVOC • Uncertain Reaction Rate of HNO3 formation: • A larger rate than expected greatly decreases the Ozone sensitivity to ENOx

  21. Overall Project Goal AnOptimum Control Strategy ANALYSIS OUTCOME • Control Strategy that satisfies the 3 criteria • Reduces multiple pollutants (air quality) • Cost Effective (economic) • Maximum health benefit (health) Response of pollutant sensitivity to uncertainty (CMAQ-HDDM) air quality Impact on pollutant level at worst monitor Cost of Emission Control (Lit / AirControlNET / CoST) economic Impact on Population Exposure & Human Health Impact on Population Exposure Health Impacts & Benefits of Emission Control (BENMAP) health

  22. Future Plan of Action • Estimate cost of control strategies • Calculate health benefits for a given population exposure • Interlink CMAQ-HDDM sensitivity output with health and cost assessment • Select control strategy that reduces multiple pollutants (O3 and PM2.5) based on maximum health benefit and minimum cost of implementation

  23. Acknowledgement : • U.S. EPA • For funding our project • GA EPD • For providing emission data • Byeong Kim for technical assistance • CMAS

  24. Contact: antara@rice.edu Log on to http://uncertainty.rice.edu/ For further information & updates of our project

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