Linking Meteorology, Air Quality Models and Observations to Characterize Human Exposures in Support of the Environmental Health Studies (Invited Presentation)

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Monday, 5 January 2015: 11:15 AM
228AB (Phoenix Convention Center - West and North Buildings)
Vlad Isakov, U.S. EPA, Research Triangle Park, NC; and V. Garcia and S. Arunachalam

Epidemiologic studies are critical in establishing the association between exposure to air pollutants and adverse health effects. Results of epidemiologic studies are used by U.S. EPA in developing air quality standards to protect the public from the health effects of air pollutants. A major challenge in environmental epidemiology is adequate exposure characterization. Numerous health studies have used measurements from a few central-site ambient monitors to characterize air pollution exposures. Relying solely on central-site ambient monitors does not account for the spatial-heterogeneity of ambient air pollution patterns, the temporal variability in ambient concentrations, nor the influence of infiltration and indoor sources. Central-site monitoring becomes even more problematic for certain air pollutants that exhibit significant spatial heterogeneity. Statistical interpolation techniques and passive monitoring methods can provide additional spatial resolution in ambient concentration estimates. In addition, spatio-temporal models, which integrate GIS data and other factors, such as meteorology, have also been developed to produce more resolved estimates of ambient concentrations. Models, such as the Community Multi-Scale Air Quality (CMAQ) model, estimate ambient concentrations by combining information on meteorology, source emissions, and chemical-fate and transport. Hybrid modeling approaches, which integrate regional scale models with local scale dispersion models, provide new alternatives for characterizing ambient concentrations. This presentation shows two examples of linking multiple models and observations to characterize exposures on regional and urban scales in order provide adequate inputs to the epidemiologic studies. In the first example, we show how the hybrid air quality modeling approach used in the Near-road EXposures to Urban air pollutant Study (NEXUS) provided spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. The NEXUS study investigated whether children with asthma living in close proximity to major roadways in Detroit, MI, (particularly near roadways with high diesel traffic) have greater health impacts associated with exposure to air pollutants than those living farther away. Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the AERMOD and RLINE dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the CMAQ model and the Space/Time Ordinary Kriging (STOK) model. The exposure metrics, capturing spatial and temporal variability across the health study domain were used in the epidemiologic analyses. Preliminary results of the epidemiologic analyses using model-based exposure estimates indicate a potential to help discern relationships between air quality and health outcomes. In second example, we show how the analysis of air parcels transported from the Midwest into New York City combined with classifications of corresponding ozone concentrations could help establishing associations with distinguishable daily respiratory–related hospital admissions. This study classified the transport of polluted air parcels for ten consecutive summer seasons (June, July and August) between 1997 and 2006 to identify exposed and unexposed groups within eight New York State regions for purposes of evaluating the prevalence of respiratory–related hospital admissions between the two groups. The calculation of the transport direction was performed with the Hybrid Single–Particle Lagrangian Integrated Trajectory model (HYSPLIT). HYSPLIT was set to calculate back trajectories, i.e., to perform the calculation back in time to determine the origin of the transported air parcel. The study results revealed spatial differences in associations between the origin of the air parcel and daily maximum 8–h ozone concentrations. These examples show how the advanced modeling techniques combined with observational data could provide necessary information to epidemiological analyses in order to establish an association between air pollution and health outcomes.