1 / 24

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

Air-quality Modeling of PM 2.5 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. Overview.

Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related