Fine particulate matter and cardiovascular disease: Comparison of assessment methods for long-term exposure.

BACKGROUND: Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however few studies have compared results across different exposure assessment methods. METHODS: We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002-2009 and resided in NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12km spatial resolutions, and satellite-based models at 10km and 1km resolutions. Case status was either a coronary artery disease (CAD) index >23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (µg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status. RESULTS: We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12km-CMAQ models. CONCLUSIONS: Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data.

Air quality modeling in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS).

A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) conducted in Detroit (Michigan, USA). Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and Research LINE-source dispersion model for near-surface releases (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 Community Multi-scale Air Quality (CMAQ) and the Space-Time Ordinary Kriging (STOK) models. To capture the near-road pollutant gradients, refined "mini-grids" of model receptors were placed around participant homes. Exposure metrics for CO, NOx, PM2.5 and its components (elemental and organic carbon) were predicted at each home location for multiple time periods including daily and rush hours. The exposure metrics were evaluated for their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area.