1. 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

2. Georgia Environmental Protection Division 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

3. Georgia Environmental Protection Division 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 Meteorolog y Emissions Air Quality Model Chemistry

4. Georgia Environmental Protection Division How reliable is air quality modeling? Stationary Point NO X Mobile NO X Biogenic VOC Anthropogenic VOC Ozone (grid, time-step) Emissions Concentrations Source Oriented AQM N Runs Simulation 1 Simulation 2 Simulation 3. Simulation N Probability distribution mean,std,cov=st d/mean,C 97.5, C 50, C 2.5 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

5. Georgia Environmental Protection Division 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 Nonlinear ozone response to emissions Taylor expansions:

6. Georgia Environmental Protection Division Air Quality Modeling - FAQS Model Performance Statistics Fall Line Air Quality Study http://cure.eas.gatech.edu/faqs/index.html Three Episodes: based on CART analysis 8/1 – 8/15/1999 8/11 – 8/19/2000 7/5 – 7/17/2001

7. Georgia Environmental Protection Division Domain-wide daily NO X and VOC emissions during 2007 (tons per day) 19992007_1999 Projected Air Quality in 2007 Emissions in 2007: Growth factor: EGAS Controls: NOX SIP call, VOC RACT and MACT, etc

8. Georgia Environmental Protection Division 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 2E E/2

9. Georgia Environmental Protection Division 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 1.5 Non-point NOX emissions (onroad and nonroad mobile, area): factor of 2 Biogenic VOC: factor of 2 Anthropogenic VOC: factor of 2

10. Georgia Environmental Protection Division Cut-off = 40 ppbv, error bar refers to variability in such ratios, 95% range Uncertainties in Ozone Simulations (2) 1999 Summary by different base yearsScatter plots for 95% CI

11. Georgia Environmental Protection Division Emission Control Responses Ozone concentrations when emissions are reduced by a factor f emis Ozone reduction (ppb) Control efficiency (%) Nonlinear ozone response to emissions Ozone reduction per unit emissions (ppt/tons per day)

12. Georgia Environmental Protection Division Uncertainties in Emission Control Responses (1) Nonlinear ozone response to emissions Random emissions P j Ozone responses to controls of Atlanta point source emissions Ozone reduction (ppb)Ozone reduction (ppt/tpd)Control efficiency (%) Peak 8-hr ozone, base year 1999, Downtown Atlanta, Georgia

13. Georgia Environmental Protection Division Uncertainties in Emission Control Responses (2) Nonlinear ozone response to emissions Random emissions P j 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

14. Georgia Environmental Protection Division Ozone Reduction (ppb) base year 1999, cutoff = 80ppb Atlanta Point Atlanta Mobile Onroad Atlanta Mobile Nonroad Outside Atlanta Point

15. Georgia Environmental Protection Division Summary of Uncertainties in Emission Control Responses: base year 1999 Ozone reduction (ppb)Ozone reduction (ppt/tpd) Control efficiency (%)Percents of 95% CI overlapping 0

16. Georgia Environmental Protection Division 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