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Air Quality Modeling to Support Exposure and Health Studies

Air Quality Modeling to Support Exposure and Health Studies . S. Arunachalam Institute of the Environment University of North Carolina at Chapel Hill. 9 th Annual CMAS Conference Chapel Hill, NC October 11-13, 2010. Collaborators :. N. Davis, K. Talgo, B.H. Baek, A. Valencia, A. Hanna

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Air Quality Modeling to Support Exposure and Health Studies

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  1. Air Quality Modeling to Support Exposure and Health Studies S. Arunachalam Institute of the Environment University of North Carolina at Chapel Hill 9th Annual CMAS Conference Chapel Hill, NC October 11-13, 2010

  2. Collaborators: N. Davis, K. Talgo, B.H. Baek, A. Valencia, A. Hanna University of North Carolina at Chapel Hill R. Cook U.S. EPA, Office of Transportation and Air Quality V. Isakov U.S. EPA, Office of Research and Development,National Exposure Research Laboratory

  3. Characterization of Exposure at Multiple Scales for Multiple Pollutants is Needed to Address Varying Needs

  4. Key Science Questions • Are current air quality models adequate to address exposure/health research needs? • Interpolated monitor data • Land-use regression models • Air quality modeling (CMAQ, AERMOD, hybrid) • Data blending • How to improve spatial and temporal resolution of air quality modelsto support epidemiologic studies? • Improved fine-scale dispersion algorithms • Hybrid air quality modeling • Fine scale CMAQ (e.g. 1-km resolution) • How to deal with uncertainty of model predictions at fine scales? • This could be accomplished, for example, using a computational scheme that allows the estimation of concentrations over micro-environments inside grid-cells, where specific emission activity dominates

  5. Several Ongoing Projects where AQMs are Used to Support Exposure and Health Studies New Haven • Pollutants: PM2.5, NOX, O3, Air Toxics • Health Outcomes: Hospitalizations –asthma, cardiovascular disease • Models: AERMOD, CMAQ, Hybrid Atlanta • Pollutants: PM2.5, NOX, EC, CO, ozone, SO4 • Health Outcomes: ER visits – respiratory, cardiovascular disease • Models: AERMOD, CMAQ, Hybrid Baltimore – MESA Air (Multi-Ethnic Study of Atherosclerosis and Air Pollution) • Pollutants: PM2.5, EC, OC, SO4, NO3 • Health Outcomes: cardiovascular disease • Models: AERMOD, CMAQ, Hybrid Detroit – NEXUS (Near Road EXposure to Urban Air Pollutants Study) • Pollutants: PM2.5, PM species, O3, Air Toxics • Health Outcomes: Asthma • Models:Proximity Models, AERMOD, Hybrid

  6. AERMOD AERMOD Air pollution concentrations were predicted at 318 census block group sites in New Haven, Connecticut using AERMOD and CMAQ Example 1:Air Quality Modeling in New Haven, CT

  7. Objective of the Atlanta Study: Develop and evaluate various exposure metrics for traffic-related (CO, NOx and PM2.5 EC) and regional (O3 and SO4) pollutants by applying them to two epidemiologic studies on relationship between ambient air pollution and acute morbidity in Atlanta: Emergency Department (ED) and Implanted Cardio Defibrillator (ICD) studies. Monitoring network in Atlanta • Exposure Metrics Used: • Central site monitor (CS) • Local contribution (AERMOD) • Local + Regional (HYBRID) • Data: • 4 years of simulations: 1999 – 2002 • Hourly modeled concentrations • Receptors at 225 zip code centroids Example 2: Atlanta Air Quality Modeling Study

  8. Maps of Modeled Concentrations: Local Contribution (AERMOD) PM2.5 NOx

  9. Maps of Modeled Concentrations: Regional Contribution (GT model) PM2.5 NOx Note: Spatio-temporal model of background data provided by Mulholland, Georgia Tech

  10. Maps of Modeled Concentrations: Local + Regional (Hybrid) PM2.5 NOx

  11. Atlanta Air Quality Monitoring and Modeling to Characterize Spatial Heterogeneity and Homogeneity Monitoring network in Atlanta • PM2.5 is spatially homogeneous but has significant temporal variability • Unlike PM2.5, NOx has substantial spatial as well as temporal variability

  12. Census block group centroids Monitor locations Example 3:Air Quality Modeling in Baltimore, MD

  13. Spatial maps of modeled annual average PM2.5 concentrations AERMOD CMAQ • AERMOD predicts more heterogeneous pattern • However, the magnitude of these local contributions is much smaller, on the order of 10 to 20% of the CMAQ estimates. • CMAQ predicts higher concentration in the NW part of the modeling domain, with a gradient of about 5 mg/m3 (or 25% difference within 30-km distance) towards the SW • Overall, regional signal for PM2.5 dominates

  14. Spatial maps of modeled annual average NOx concentrations AERMOD CMAQ • For NOx, regional signal and local hot spots are equally important • As for PM2.5, CMAQ predicts higher concentration in the NW part of the domain • However, unlike for PM2.5, the gradient for NOx is steep - about 50 mg/m3 within 30-km distance • AERMOD predicts a lot of heterogeneity. • Magnitude of these local contributions is comparable or even higher than CMAQ • Overall, both local and regional contributions of NOx emission sources are important

  15. Example 4:Air Quality Modeling in Detroit, MI Near Road Exposure to Urban Air Quality Study (NeXUS) Initial screening analysis using AERMOD is underway Tools and databases being developed

  16. Zhu et. al., 2002 Why better characterization of near-road dispersion is needed? 1. Sharp gradient of pollutant gradients near roadways 2. Local-scale dispersion models required to resolve near-road pollutant gradients 100m resolution 200m resolution 300m resolution

  17. Resolving local scales when • modeling mobile source impacts • Model inputs required: • Spatially resolved locations of individual road links • Traffic activity for each road link • Emission factors • Need Practical Approach to Develop Inputs: • (1) Road links: Road link locations from TIGER ROAD network • (or GIS-validated network from Teleatlas) • (2) Traffic activity: Traffic volumes from State/Local agencies for road links from transportation models (TRAPLAN, DTIM, etc.) • Link (1) and (2) using GIS-based approach • (3) Emission factors: Emission factors as a function of Speed/Temperature from emission models (MOVES)

  18. Discussion • Air quality models provide key inputs to exposure assessment studies • Analyses of exposures to environmental contaminants, and of subsequent doses and effects is a multiscale problem • Spatial scales vary depending on exposure metric of interest • Choice of appropriate modeling tools key to accurate characterization of exposures • Hybrid modeling tools able to address this need • Approach has been incrementally refined with each application • Additional tools in development for modeling fine scales • GIS-based tools for processing onroad and port emissions • Post-processing data from MOVES for use in AERMOD • New Line Source Model (AERLINE) under development and testing (Collaboration with Dr. A. Venkatram) • Explore additional options for modeling sub-grid variability • Statistical approaches • Explicitly embed SGV in Grid-based models, etc. • Transfer of techniques/tools to other users (FAA, NAS, etc.)

  19. Acknowledgements: The United States Environmental Protection Agency, through its Office of Research and Development, partially funded and collaborated in the research described here under Contract Nos. EP-D-05-045 and EP-D-07-102 to the University of North Carolina at Chapel Hill.

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