Comparison of PM Source Apportionment and Sensitivity Analysis in CAMx

1. Comparison of PM Source Apportionment and Sensitivity Analysis in CAMx Bonyoung Koo, Gary Wilson, Ralph Morris, Greg Yarwood ENVIRON Alan Dunker General Motors R&D Center 8th Annual CMAS Conference October 19-21, 2009 Chapel Hill, North Carolina

2. Probing Tools in CAMx • Source Apportionment • Ozone Source Apportionment Technology (OSAT) • Particulate Source Apportionment Technology (PSAT) • Reactive Tracer Source Apportionment (RTRAC) • Sensitivity Analysis • Decoupled Direct Method (DDM) for gas and particulate species • Higher-order DDM (HDDM) for gas-phase species • Process Analysis • Integrated Process Rate (IPR) • Integrated Reaction Rate (IRR) • Chemical Process Analysis

3. Probing Tools in CAMx • Source Apportionment – Tagged Species • Ozone Source Apportionment Technology (OSAT) • Particulate Source Apportionment Technology (PSAT) • Reactive Tracer Source Apportionment (RTRAC) • Sensitivity Analysis • Decoupled Direct Method (DDM) for gas and particulate species • Higher-order DDM (HDDM) for gas-phase species • Process Analysis • Integrated Process Rate (IPR) • Integrated Reaction Rate (IRR) • Chemical Process Analysis

4. Probing Tools in CAMx • Source Apportionment • Ozone Source Apportionment Technology (OSAT) • Particulate Source Apportionment Technology (PSAT) • Reactive Tracer Source Apportionment (RTRAC) • Sensitivity Analysis • Decoupled Direct Method (DDM) for gas and particulate species • Higher-order DDM (HDDM) for gas-phase species • Process Analysis • Integrated Process Rate (IPR) • Integrated Reaction Rate (IRR) • Chemical Process Analysis

5. Probing Tools in CAMx • Source Apportionment • Ozone Source Apportionment Technology (OSAT) • Particulate Source Apportionment Technology (PSAT) • Reactive Tracer Source Apportionment (RTRAC) • Sensitivity Analysis • Decoupled Direct Method (DDM) for gas and particulate species • Higher-order DDM (HDDM) for gas-phase species • Process Analysis • Integrated Process Rate (IPR) • Integrated Reaction Rate (IRR) • Chemical Process Analysis

6. Brute-Force Method DCBFM BFM Pollutant Concentration 0 E1 E0 Emission

7. First-Order Sensitivity DCDDM DCBFM DDM BFM Pollutant Concentration 0 E1 E0 Emission

8. Source Apportionment DCDDM DCBFM DDM DCPSAT BFM Pollutant Concentration PSAT 0 E1 E0 Emission

9. Zero-Out Contribution DCDDM DDM BFM Pollutant Concentration DCPSAT = DCBFM PSAT 0 E0 Emission

10. PM Modeling Episode • February & July from the St. Louis 36-/12-km 2002 PM2.5 SIP modeling • Urban & rural receptors: • 2 PM2.5 NAAs • 6 Federal Class-I areas • BFM reductions of 20% and 100% in various emission species from anthropogenic sources • PSAT and DDM Chicago PM2.5 NAA (CNAA), St. Louis PM2.5 NAA (SNAA), Mingo wilderness area (MING), Hercules-Glades wilderness area (HEGL), Upper Buffalo wilderness area (UPBU), Caney Creek wilderness area (CACR), Mammoth Cave national park (MACA), and Sipsey wilderness area (SIPS)

11. Contributions of Point-Source SO2 to PM2.5 Sulfate February July

12. Contributions of Point-Source SO2 to PM2.5 Sulfate February Oxidant-limiting effects July

13. PM2.5 Sulfate Changes due to Point SO2 Emiss Reductions

14. PM2.5 Sulfate Changes due to Point SO2 Emiss Reductions Oxidant-limiting effect

15. PM2.5 Sulfate Changes due to Point SO2 Emiss Reductions Non-linear responses

16. PM2.5 Sulfate Changes due to On-road MV Emiss Reductions

17. PM2.5 Sulfate Changes due to On-road MV Emiss Reductions Indirect effect February: Reducing NOx emissions Lower acidity of the aqueous phase More SO2 dissolves in the aqueous phase More sulfate produced Negative Sensitivity

18. PM2.5 Sulfate Changes due to On-road MV Emiss Reductions Indirect effect July: Reducing NOx emissions Less oxidant available to oxidize SO2 Further reduction in sulfate Positive Sensitivity

19. PM2.5 Ammonium Changes due to Area NH3 Emiss Reductions

20. PM2.5 Nitrate Changes due to Area NOx Emiss Reductions

21. PM2.5 Nitrate Changes due to On-road MV Emiss Reductions Less indirect effect because NOx dominates on-road MV emission

22. PM2.5 SOA Changes due to Area VOC Emiss Reductions

23. Primary PM2.5 Changes due to On-road MV Emiss Reductions

24. Summary • 1st-order DDM sensitivities agree well with the BFM model responses to small emission changes (20%) • With large emission changes, non-linearity comes into play • For SOA and primary PM2.5, the DDM works relatively well even with 100% emission reductions • PSAT and zero-out are nearly equivalent in cases with no indirect effect • PSAT starts to deviate from the zero-out contribution as indirect effects from limiting reactants or non-primary precursor emissions become important

25. Summary (cont.) • Source sensitivity and source apportionment are equivalent for pollutants that are linearly related to emissions; However, when they are different: • PSAT is best at apportioning PM pollutants to sources emitting their primary precursors (e.g., sulfate to SO2, nitrate to NOx) • DDM sensitivities are more accurate than PSAT in determining the impact of emissions that have indirect effects on secondary PM • PSAT works better at estimating the impact of zeroing-out a source while DDM does generally better when a fraction of emissions are eliminated from the source

26. Summary (cont.) • BFM (zero-out) also has limitations: • Computationally expensive and subject to numerical noises • Sum of the BFM source contributions will not always equal the simulated concentrations in the base case

27. Acknowledgement • Funded by the Coordinating Research Council For more details… Koo, B., G. M. Wilson, R. E. Morris, A. M. Dunker and G. Yarwood.   2009.   “Comparison of Source Apportionment and Sensitivity Analysis in a Particulate Matter Air Quality Model.”  Environ. Sci. Technol., 43 (17), pp 6669-6675.  doi: 10.1021/es9008129