1. Evaluation of Emission Control Strategies for Regional Scale Air Quality: Performance of Direct and Surrogate Techniques Presented at the 6 th Annual CMAS Conference Friday Center, UNC-Chapel Hill October 1-3, 2007 Computational Chemodynamics Laboratory (CCL) Environmental and Occupational Health Sciences Institute (EOHSI) A Joint Institute of UMDNJ-RW Johnson Medical School and Rutgers University 170 Frelinghuysen Road, Piscataway, NJ 08854 *Atmospheric Sciences Modeling Division, Air Resources Laboratory, NOAA in partnership with the USEPA S. Isukapalli, S. Wang, S. Napalenok* T. Kindap, and P. Georgopoulos

2. CMAS Conference, 2007 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.)

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

4. CMAS Conference, 2007 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, PM 2.5, 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

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

6. CMAS Conference, 2007 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)

7. CMAS Conference, 2007 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:

8. CMAS Conference, 2007 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

9. CMAS Conference, 2007 9 Decomposition of variance: The total variance  2 (g) attributable to all inputs can be decomposed into individual contributions

10. CMAS Conference, 2007 10 Automatic Differentiation (www.autodiff.org) 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 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 Chain Rule on Computer Instructions Problems: ADIFOR does not support F90/F95 Commercial tools unproven

11. CMAS Conference, 2007 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

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

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

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

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

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

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

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

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

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

21. CMAS Conference, 2007 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