Evaluation of Emission Control Strategies for Regional Scale Air Quality: Performance of Direct and Surrogate Techniqu

1. Evaluation of Emission Control Strategies for Regional Scale Air Quality: Performance of Direct and Surrogate Techniques Presented at the 6th Annual CMAS Conference Friday Center, UNC-Chapel Hill October 1-3, 2007 S. Isukapalli, S. Wang, S. Napalenok* T. Kindap, and P. Georgopoulos Computational Chemodynamics Laboratory (CCL) Environmental and Occupational Health Sciences Institute (EOHSI)A Joint Institute of UMDNJ-RW Johnson Medical School and Rutgers University170 Frelinghuysen Road, Piscataway, NJ 08854 *Atmospheric Sciences Modeling Division, Air Resources Laboratory, NOAA in partnership with the USEPA

2. Acknowledgments • Alper Unal, WRI • Talat Odman and Yongtao Hu, Georgia Institute of Technology • USEPA (Funding for Center for Exposure and Risk Modeling) • NJDEP (Base funding for the Ozone Research Center) • OTC Modeling Group Centers (Emissions inventories, Meteorology, etc.) CMAS Conference, 2007 2

3. Overview • Surrogate Modeling Techniques • HDMR • DDM • Automatic Differentiation • Response Surface Modeling • Case Study • Emissions and Regions • Estimates vs Brute Force • Results and Discussion CMAS Conference, 2007 3

4. Use of surrogate models for emission control analysis • Emissions control analysis is a multi-dimensional problem • Geographic regions [states/counties, etc.] • Types of emissions [point, biogenic, area, mobile, etc.] • Primary emissions [NOx, VOC, etc.] • Some times, multi-objective problems • Ozone, PM2.5, etc. • Direct model simulation is expensive • 2 hours/day for OTC-12 domain simulation (8 Opteron nodes) • Surrogate models can provide significant speedups • Construction of surrogate models is often parallelizable CMAS Conference, 2007 4

5. Use of surrogate models for emission control analysis • Can provide a “Fast Equivalent Operational Model” (FEOM) • Can also be used in Uncertainty Propagation CMAS Conference, 2007 5

6. Use of surrogate models for emission control analysis • Several techniques exist for surrogate modeling • Response Surface Methods (Deterministic and Stochastic) • High Dimensional Model Representations (HDMR) • Local Gradient-Based Methods • Decoupled Direct Method (DDM) • Adjoint Sensitivity Analysis Method • Automatic Differentiation • Features • Black-box models (Response Surface; HDMR; etc.) • Some changes to code (Automatic Differentiation) • Extensive changes to model code/new modules (DDM; Adjoint Sensitivity) CMAS Conference, 2007 6

7. High Dimensional Model Representation (HDMR) System (a mathematical model; e.g. CMAQ): Input I: Output O: • HDMR expresses model outputs as expansions of correlated functions: CMAS Conference, 2007 7

8. The expressions of HDMR component functions are optimal choices for the output f (x) over the desired domain of the input variable space such that the HDMR expansion converges very rapidly • Cut-HDMR: • In practice, the HDMR expansion functions are represented as a set of low dimensional look-up tables CMAS Conference, 2007 8

9. Decomposition of variance: • The total variance s2(g) attributable to all inputs can be decomposed into individual contributions CMAS Conference, 2007 9

10. Automatic Differentiation (www.autodiff.org) Chain Rule on Computer Instructions dy(1) = 0.0 y(1) = 1.0 dy(2) = 0.0 y(2) = 1.0 do i = 1,n if (x(i) > 0.0) then dtemp = y(1)*dx(i) + x(i)*dy(1) temp = x(i) * y(1) dy(1) = y(1)*dtemp + temp*dy(1) y(1) = temp * y(1) else dtemp = y(2)*dx(i) + x(i)*dy(2) temp = x(i) * y(2) dy(2) = y(2)*dtemp + temp*dy(2) y(2) = temp * y(2) endif enddo y(1) = 1.0 y(2) = 1.0 do i = 1,n if (x(i) > 0.0) then y(1) = x(i) * y(1) * y(1) else y(2) = x(i) * y(2) * y(2) endif enddo Problems: ADIFOR does not support F90/F95 Commercial tools unproven CMAS Conference, 2007 10

11. Decoupled Direct Method (DDM) • CMAQ-DDM 4.5 with CB4, Aero4, AQ • Serial version from Talat Odman, Georgia Inst. of Technology • Parallel version from Sergey Napalenok, USEPA CMAS Conference, 2007 11

12. Domain for the Case Study CMAS Conference, 2007 12

13. Case Study • Impact of reductions in NOx emissions from five states: • DE, MD, NJ, NY, and PA • Base Case: • OTC12 BaseB • Evaluate performance of HDMR and DDM as surrogate models • 10% overall • 25% overall • 75% overall except PA (zero reduction) CMAS Conference, 2007 13

14. Base Case (08/01/2002; Hours 14-17) CMAS Conference, 2007 14

15. 10% overall reduction; HDMR (08/01/2002; Hours 14-17) CMAS Conference, 2007 15

16. 25% overall reduction; HDMR (08/01/2002; Hours 14-17) CMAS Conference, 2007 16

17. 75% overall reduction [except PA]; HDMR (08/01/2002; Hours 14-17) CMAS Conference, 2007 17

18. 10% overall reduction; DDM (08/01/2002; Hours 14-17) CMAS Conference, 2007 18

19. 25% overall reduction; DDM (08/01/2002; Hours 14-17) CMAS Conference, 2007 19

20. 75% overall reduction [except PA]; DDM (08/01/2002; Hours 14-17) CMAS Conference, 2007 20

21. Discussion • Approximations appear to break at about 25% changes in emissions • Can be used for screening purposes for small variations • Potential mix of “global” and “gradient-based” sensitivities CMAS Conference, 2007 21