marmur presentation

Air-quality Modeling of PM2.5 Mass and Composition in Atlanta:Results from a Two-Year Simulation and Implications for Use in Health Studies: 

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

Overview: 

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

ED Study: Cardiovascular Disease: 

ED Study: Cardiovascular Disease

Issues: 

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)

Spatial Representativeness: 

Spatial Representativeness

PM2.5 Monitoring Sites in Atlanta: 

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

Average daily values of PM2.5 components(g/m3, 1999-2001): 

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

PM2.5 Correlelogram: 

PM2.5 Correlelogram

SO4-2 Correlelogram: 

SO4-2 Correlelogram

NO3- Correlelogram: 

NO3- Correlelogram

EC Correlelogram: 

EC Correlelogram

OC Correlelogram: 

OC Correlelogram

Mid-Talk Conclusions: 

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)

Use of 3D Air-Quality Models: 

Use of 3D Air-Quality Models

Domains and Model-Setup: 

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

Coarse domain: 

Coarse domain Fine domain 36km x 36km cells 5148 cells total 2376 km x 2808 km 12km x 12km cells 196 cells total 168 km x 168 km

Results: PM2.5: 

Results: PM2.5

Results: SO4-2: 

Results: SO4-2

Results: NO3-: 

Results: NO3-

Results: EC: 

Results: EC

Results: OC: 

Results: OC * - divided by 1.4

Spatial Resolution – 12km Domain - OC: 

Spatial Resolution – 12km Domain - OC R values: 0.89-0.97 (0.55 and lower in measurements)

Conclusions: 

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.

Acknowledgments: 

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.