1. 1 Comparison of CAMx and CMAQ PM2.5 Source Apportionment Estimates Kirk Baker and Brian Timin U.S. Environmental Protection Agency, Research Triangle Park, NC Presented at the 2008 CMAS Conference

2. 2 Background Photochemical model source apportionment is a useful tool to efficiently characterize source contribution to PM2.5 Implemented particulate source apportionment in CMAQv4.6 Compared the source apportionment results with other model system: CAMx Existing inputs developed for Milwaukee pilot project used for comparison of source apportionment results

3. 3 PPTM & PSAT The Particle and Precursor Tagging Methodology (PPTM) has been implemented in CMAQ v4.6 Particulate Source Apportionment Technology (PSAT) has been implemented in CAMx v4.5 Tracks contribution to mercury and PM sulfate, nitrate, ammonium, secondary organic aerosol, and inert species Estimates contributions from emissions source groups, emissions source regions, and initial and boundary conditions to PM2.5 by adding duplicate model species for each contributing source These duplicate model species (tags) have the same properties and experience the same atmospheric processes as the bulk chemical species The tagged species are calculated using the regular model solver for processes like dry deposition and advection as bulk species Non-linear processes like gas and aqueous phase chemistry are solved for bulk species and then apportioned to the tagged species

4. 4 PM2.5 Source Apportionment Modeling for Milwaukee Pilot Project CAMx v4.5 and CMAQ v4.6 12 km modeling domain 4 months in 2002: Jan, Apr, Jul, Oct Evaluating 24-hr average contributions from 11 source regions, the rest of the modeling domain, & boundary conditions Emissions processed separately for each source region

5. 5 Region 12 – All non-tagged areas in domain Region 13 – Boundary conditions Source Regions

6. 6 Model Performance Daily 24-hr PM predictions at Milwaukee (550790026) and Waukesha (551330027) county STN monitors over all modeled days Model-Model estimates shown at right CMAQ tends to predict more nitrate than CAMx

7. 7 Model Performance CMAQCAMx

8. 8 Contribution Estimation Evaluated contribution at Milwaukee (5) and Waukesha (1) monitors PM2.5 = SO4+NO3+NH4+POC+EC Examined 1) top 10% days, 2) average over all days, and 3) compared daily estimates –Days included in top 10% analysis: Q1=6, Q2=6, Q3=0, Q4=3 Contribution from 11 source regions (counties), ICBC, all other non-tagged sources Did not track SOA due to low model estimations and resource constraints

9. 9 Total PM2.5 Contribution Estimation

10. 10 24-hr Avg Total PM2.5 Contribution Estimation: Top 10% Days CMAQCAMx

11. 11 CMAQ CAMx 4-month average total PM2.5 contributions from source areas 1-6 Region = 1 2 3 4 5 6

12. 12 CMAQ CAMx 4-month average total PM2.5 contributions from source areas 7-11 Region = 7 8 9 10 11

13. 13 Distribution of 24-hr avg Contribution Estimations

14. 14 24-hr avg Contributions estimated by CMAQ and CAMx

15. 15 24-hr avg Contributions estimated by CMAQ and CAMx

16. 16 24-hr avg Contributions estimated by CMAQ and CAMx

17. 17 24-hr avg Contributions estimated by CMAQ and CAMx

18. 18 Domain Maximum 24-hr avg Initial Condition Contribution

19. 19 Remarks CMAQ estimates more nitrate and as a result estimates larger nitrate contributions CMAQ seems to estimate larger local contributions from primarily emitted species Spatial extent of average contributions similar between models Average contributions over high model days very similar at the Milwaukee/Waukesha monitors Initial contributions drop out of model after 5-7 days Would like to compare with CMAQ-DDM for future work

20. 20 Acknowledgements Tom Braverman, US EPA ICF International (Sharon Douglas and Tom Myers)

21. 21 Kenosha County 24-hr max contribution Sulfate Nitrate Primary OC JANAPRJUL OCT