1. The Near-road Exposures and Effects of Urban air pollutant Study (NEXUS) investigating whether children with asthma living near major roadways in Detroit, MI have greater health impacts from air pollutants than those living farther away, particularly near roadways with high diesel traffic. Air quality modeling provides spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. Measurements collected at a subset of participant homes and three stationary sites are used for model evaluation. Traffic-related air pollutants (primary PM 2.5, EC, NO x,, CO, benzene) Regional background concentrations from other sources are estimated independently, then added to mobile, point, and area source estimates. Emissions estimates are refined based on evaluation with measurement data Comparison of tiered exposure estimates: Evaluate value added by providing more detailed characterization of exposures for use in epidemiologic analyses to establish association between air pollution and health outcomes. Air Quality Modeling Approach NEXUS Health Study Design Model Results for Use in Epidemiologic Study Primary research question: Do children with asthma living near major roadways with high traffic have greater health impacts associated with air pollutants than those living farther away, particularly for those living near roadways with high diesel traffic ? Health outcomes: Aggravation of asthma symptoms, inflammation and other biological responses, and respiratory viral infections. Exposure Estimates Time Activity Data Ambient Monitoring Data GIS-based Exposure Indicators Air Quality Modeling Human Exposure Modeling Existing Monitoring Network Data GIS Traffic Volume/Type Data Emissions Data/Modeling Meteorological Data/Modeling GIS Proximity and Land-use Data Land-Use/Topography Monitoring Data Air Quality Modeling Output Residential Data Input Data Needs Application and Evaluation for Health Studies NEXUS Monitoring Data Wind Speed/Direction Data Future work: Combining emissions-based dispersion modeling and measurements-based source apportionment techniques. Measurements are spread out throughout the city, but not continuous Air quality modeling can provide better coverage, but has limitations due to uncertainty in emission inventories Combining both methods helps to reduce uncertainty Air quality modeling is critical for the epidemiologic analysis: Air quality modeling provides detailed spatial coverage for the study area Gives hourly concentration estimates at specific receptors for the entire study period, where an LUR-type model would only provide spatial variability. Providing such detailed information by using monitoring is not feasible In this study, we use limited monitoring at selected locations to evaluate and calibrate model results DISCLAIMER: Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. Key Elements in Air Quality Modeling Multi-scale air quality modeling is capable of providing spatial and temporal exposure metrics for epidemiologic study. Integrated measurement and modeling approach: Utilize modeling in source-focused health study to estimate spatially and temporally varying exposures Collect limited measurements for evaluating and refining models Models provide pollutant surfaces capturing spatial and temporal variability across health study domain (Fall 2010 – Spring 2012) The authors would like to acknowledge contributions by Kevin Talgo, Alejandro Valencia, Brian Naess, Mohammad Omary, and Yasuyuki Akita (Institute for the Environment at UNC, Chapel Hill, NC) and Gary Norris, Ali Kamal, Carry Croghan, and Steve Perry (U.S. EPA, RTP, NC) and Paul Harbin a Community Partner at Large, Community Action Against Asthma, Detroit, MI. Mobile source estimates show spatial and temporal variability (Fall 2010 Ave.). Modeled exposure metrics show good agreement with measurements at NEXUS home locations and AQS sites. Background levels: A space/time ordinary kriging (STOK) method has been developed using AQS data and results from two annual simulations of the Community Multiscale Air Quality (CMAQ) model. The first (baseline) simulation represents all emissions in a broad region (covering the eastern US), the second simulation removes all anthropogenic emissions in the study domain. The ratios of concentrations predicted by CMAQ in these two simulations for the Detroit region, along with AQS data from background sites in the region, were used to estimate background pollutant concentrations. Near road exposure metrics are based on modeled concentrations at “minigrid” receptors. modeled roadlink modeled receptors 50m 300m Air Quality Estimates (Sep – Nov. 2010) EC/CO Ratio Air quality modeling provides inputs for epidemiologic analyses: time series of exposure estimates at all NEXUS study locations and schools, for each day of the study period, and for multiple pollutants. PM 2.5 Dispersion Modeling (RLINE/ AERMOD) Applying Multi-scale Air Quality Models to Support Epidemiologic Studies Michelle Snyder, Vlad Isakov, David Heist, Janet Burke, Sarah Bereznicki (U.S. Environmental Protection Agency, RTP, NC USA), Sarav Arunachalam (University of North Carolina, Chapel Hill, NC USA), and Stuart Batterman (University of Michigan, Ann Arbor, MI USA) Mobile sources: RLINE (under development) is a research-level, line- source dispersion model under development by EPA’s Office of Research and Development as part of the ongoing effort to further develop tools for a comprehensive evaluation of air quality impacts in the near-road environment. This model is used in conjunction with traffic activity and primary mobile source emission estimates to model hourly exposures at study participants’ home and school locations. Stationary sources: Industrial sources, such as stacks from manufacturing facilities, are modeled using AERMOD. These sources and their emissions are obtained from the latest official National Emissions Inventory (NEI). Temporal profiles are applied to stack emissions using SMOKE based temporal profiles. NO X Multiple cohort design: High Traffic/High Diesel (HTHD), High Traffic/Low Diesel (HTLD), and Low Traffic (LT) based on roadway classification and traffic counts. Estimates from multiple sources are combined to give spatiotemporally resolved concentrations at participant locations. Location and strength of PM 2.5 point sources and diesel traffic roadways in the NEXUS domain. Study participant locations categorized by proximity to roadway type.