Amit Marmur, Jim Mulholland and Ted Russell Georgia Institute of Technology

1. Air-quality Modeling of PM2.5 Mass and Composition in Atlanta:Results from a Two-Year Simulation and Implications for Use in Health Studies Amit Marmur, Jim Mulholland and Ted Russell Georgia Institute of Technology 10/19/2004, Models3 workshop Georgia Institute of Technology

2. Overview • Characterization and health-effects of PM2.5 in Atlanta: • SEARCH/ ASACA: PM2.5 measurements (ARA, GA-Tech) • SOPHIA/ ARIES: health effects of PM2.5 (Emory University) • Spatial representativeness and error estimation in PM2.5 components • Use of 3D air quality models in epidemiologic studies Georgia Institute of Technology

3. PM 10 O 1/93 3 NO - 2 CO 8/00 SO 2 PM 2.5 coarse PM ultrafines PM metals 8/98 2.5 - PM sulfate 2.5 8/00 PM acidity 2.5 PM OC 2.5 PM EC 2.5 OHC 0.92 1.00 1.08 Risk Ratio per one StDev increase in pollutant level ED Study: Cardiovascular Disease Georgia Institute of Technology

4. Issues • The use of data from a single (central) monitoring site in epidemiologic studies: • How representative is it of the entire city/region? • What are the associated errors (measurement + “exposure” error) • Can air-quality models provide useful information, either on coarse (central value) or fine (local exposure) domains? • Do AQ models capture the day-to-day variability? • Is the cause of the health outcome measured? Is it necessarily a single pollutant? • Health outcomes from specific sources, rather than specific pollutants (receptor modeling) Georgia Institute of Technology

5. Spatial Representativeness Georgia Institute of Technology

6. PM2.5 Monitoring Sites in Atlanta SEARCH (ARA): JST – Jefferson St. YK - Yorkville (Yorkville) ASACA (GA-Tech): FT – Fort McPherson TU – Tucker SD – South Dekalb FYG – Fort Yargo Georgia Institute of Technology

7. Average daily values of PM2.5 components(g/m3, 1999-2001) Georgia Institute of Technology

8. PM2.5 Correlelogram Georgia Institute of Technology

9. SO4-2 Correlelogram Georgia Institute of Technology

10. NO3- Correlelogram Georgia Institute of Technology

11. EC Correlelogram Georgia Institute of Technology

12. OC Correlelogram Georgia Institute of Technology

13. Mid-Talk Conclusions • Total PM2.5 and SO4-2 are highly correlated throughout the domain • correlation is not a function of distance between sites • measurement error plays a major role • NO3- and NH4+ slightly less correlated throughout the domain • correlation decreases slightly with distance • measurement error and regional effects are both evident (local availability of NH3?) • Correlations for EC are significantly lower • local and regional effects (“spatial error”) • “scientific” measurement error • Correlations for OC (40% secondary) are also relatively low • some local effects (“spatial error”) • higher “scientific” measurement error (volatility? sampling vs. analysis) Georgia Institute of Technology

14. Use of 3D Air-Quality Models Georgia Institute of Technology

15. Domains and Model-Setup • Coarse domain: 36km, 78x66 cells • Fine domain: 12km, 14x14 cells, centered around Atlanta • MM5: Pleim Xiu LSM, FDDA runs • Smoke: NEI 99 inventory, year 2000 • CMAQ: saprc99, 6 vertical layers • Have compared to same periods using 12 layers • Simulation period: Jan 2000 - Dec 2001 Georgia Institute of Technology

16. Coarse domain Fine domain 36km x 36km cells5148 cells total2376 km x 2808 km 12km x 12km cells196 cells total168 km x 168 km Georgia Institute of Technology

17. Results: PM2.5 Georgia Institute of Technology

18. Results: SO4-2 Georgia Institute of Technology

19. Results: NO3- Georgia Institute of Technology

20. Results: EC Georgia Institute of Technology

21. Results: OC Georgia Institute of Technology * - divided by 1.4

22. Spatial Resolution – 12km Domain - OC R values: 0.89-0.97 (0.55 and lower in measurements) Georgia Institute of Technology

23. Conclusions • CMAQ has been used to: • suggest whether some sites are more representative than others • JST site • evaluate the direct use of CMAQ in health studies • “regional” values • local exposure • Simulating “regional” values – Model Performance: • good for SO4-2 and PM2.5 (good as data?) • reasonable for EC and OC • OC biased low… inventory? • poor for NO3-, NH4+ • high nitrate in winter • temperature effects or too much ammonia? • “Local” exposure: • a finer 12km domain does not capture the spatial variance • comparing 4 km and 12 km results from FAQS modeling finds similar result. Georgia Institute of Technology

24. Acknowledgments • This work was supported by subcontractors to Emory University under grants from the U.S. Environmental Protection Agency (R82921301-0, RD83096001), the National Institute of Environmental Health Sciences (R01ES11199 and R01ES11294), and Georgia Power/ Southern Company. • We would also like to thank ARA (Atmospheric Research and Analysis) for both providing access to data used in this analysis and ongoing discussions. Georgia Institute of Technology