Armistead (Ted) Russell Air Resources Engineering Center Environmental Engineering

1. SUPPORTING INTEX THROUGH INTEGRATED ANALYSIS OF SATELLITE AND SUB-ORBITAL MEASUREMENTS WITH GLOBAL AND REGIONAL 3-D MODELS: Bottom-up Emissions Inventory Development and Inverse Modeling Armistead (Ted) Russell Air Resources Engineering Center Environmental Engineering Georgia Institute of Technology Georgia Institute of Technology

2. Objectives • Use satellite, in-situ and aircraft observations to evaluate chemical transport model (CTM) results to identify likely emissions biases using inverse modeling • Oxidized nitrogen species (NO2, NO, HNO3, PAN, PM-nitrate) • HCHO • CO • SO2-sulfate • PM • Evaluate satellite observations • Consistency with well-characterized emissions and “analyzed” air quality fields • Examine spatial variability and ground/aircraft-based monitor spatial representativeness Georgia Institute of Technology

3. Approach • Develop “accurate” emissions inventories for 2003-2004 • Model processes (mobile, area, biogenic) (10-50+% unc.) • CEM for major point sources (<15% unc.) • Simulate August 2003 air quality • Use inverse modeling to identify likely inventory biases/timing issues • Identify conditions where model works better/worse • Simulate INTEX study periods • Evaluate model • Compare results to satellite observations • Assess mass consistency between observations and model simulations • Use model results to address objectives Georgia Institute of Technology

4. Emissions: Nationwide NOX NOX Anthropogenic VOC PM10 SO2 EPA National Air Quality and Emissions Trends Report, 2003 Georgia Institute of Technology

5. Emissions Inventory: Northeast NOX PM2.5 SO2 VOC 2003 Emission Inventory, Fall Line Air Quality Study (FAQS) Georgia Institute of Technology

6. Emissions Inventory: Northeast States Georgia Institute of Technology

7. Top SO2 Emitters (Nationwide) Georgia Institute of Technology

8. Top NOX Emitters (Nationwide) Georgia Institute of Technology

9. Chemical Transport Modeling • Use MM5/SMOKE/CMAQ-DDM3D • CMAQ-DDM3D • SAPRC99 (more detailed chemical species, part. HCHO) • DDM3D provides sensitivity fields directly • Conduct inverse modeling to identify likely emissions biases • Use source-air quality sensitivities and observations to modify emissions estimates • Modifications viewed as suggestive, not absolute. Georgia Institute of Technology

10. Air quality Model Domain horizontal domain vertical structure 36km grid over US, southern Canada and northern Mexico corresponds to the RPO (Regional Planning Organization) unified grid Georgia Institute of Technology

11. Model Parameters (P) State Variables: Sensitivity Parameters: Inputs (P) Model Sensitivity analysis • Given a system, find how the state (concentrations) responds to incremental changes in the input and model parameters: If Pj are emission, Sij are the sensitivities/responses to emission changes: This is done automatically using DDM-3D Georgia Institute of Technology

12. Sensitivity Analysis • Calculate sensitivity of gas and aerosol phase concentrations and wet deposition fluxes to input and system parameters • sij(t)=ci(t)/pj • Brute-Force method • Must run the model a number of different times • Inaccurate sensitivities may result due to numerical noise propagating in the model • DDM - Decoupled Direct Method • Use direct derivatives of governing equations • Initial and boundary conditions, horizontal transport, vertical advection and diffusion, emissions, chemical transformation, aerosol formation, and scavenging processes Georgia Institute of Technology

13. Atmospheric Advection-Diffusion Equation and corresponding sensitivity equation • ADE equation (IC/BCs not shown) • Sensitivity equation (semi-normalized) , Pj is unperturbed field Georgia Institute of Technology

14. DDM-3D NOo NO2o VOCio ... T K u, v, w Ei ki BCi ... 3-D Air Quality Model O3(t,x,y,z) NO(t,x,y,z) NO2(t,x,y,z) VOCi(t,x,y,z) ... decoupled DDM-3D Sensitivity Analysis J Georgia Institute of Technology

15. Inverse Modeling and Sensitivity Analysis • Inverse modeling involves using observations along with a physical model (e.g., traditional air quality) model to estimate model parameters and inputs, e.g., emissions Inputs Model Output ~ Need how model responds: Sensitivity Observations Georgia Institute of Technology

16. Emissions Inventory Assessment usingInverse Modeling/Four Dimensional Data Assimilation (FDDA) INPUTS Emissions inventory (Mobile, area, biogenic, point sources) Pollutant distribution (spatial & temporal) (e.g. Ozone, NOx, NOy, SO2, CO, VOCs); and sensitivity fields Air Quality Model + DDM-3D Other inputs that remain as defined in the base case scenario New emissions distribution by source that minimize the difference between observations and simulations Ridge regression Module Observations taken from routine measurement networks or special field studies Main assumption in the formulation: A driving source for the discrepancy between predictions and observations is the emission estimates Georgia Institute of Technology

17. Estimated emission adjustments forSoutheast emissions using FDDA * * Using only IMPROVE measurements * Includes mobile and area sources Georgia Institute of Technology

18. Plan • Applying approach to August 2003 • Identify initial inventory and model performance issues • Look at impact of blackout • Extend inverse method to use satellite observations • Apply to INTEX period • Further assess inventory • Reconcile bottom-up and top-down emissions estimates Georgia Institute of Technology

19. Considerations • SO2 emissions estimates most accurately quantified • Good ability to simulate sulfate (dominant PM species in east) • NOx emissions estimates quantified better where major point sources dominate: • Ohio River Valley (e.g., West VA) • Southeast (TN-NC) • Interesting experiments over time • Plants applying NOx and SO2 controls • 25-85% reductions • Seasonal variation (summer season application) • Blackout Georgia Institute of Technology