1. Background The Near-road EXposures to Urban air pollutants Study (NEXUS) investigated whether children with asthma living in close proximity to major roadways in Detroit, MI have greater health impacts associated with exposure to 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 were used for model evaluation. Traffic-related air pollutants (primary PM 2.5 EC/OC; EC from diesel; NO x and CO; benzene) Regional Background concentrations from other sources are estimated independently, then added to mobile, point, area source estimates. Emissions estimates 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 Design Model Results for Use in Epidemiologic Study Objective: Study effects of traffic-related air pollution on the respiratory health of asthmatic children in Detroit Primary research question: Do children with asthma living near major roadways with high traffic have greater health impacts associated with exposure to 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 Preliminary results using simple proximity-based exposure metrics in epi models with health outcomes were mixed (i.e. expected effect but not statistically significant), so may not be adequate for this study. However, advanced model-based exposure metrics may help to discern the relationships between air quality and health outcomes (example shown to the right). Air quality modeling provides critical information for the epidemiologic analysis: Air quality modeling can provide detailed spatial coverage for the study area Model estimates hourly concentrations 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) Preliminary Results of Epidemiologic Analysis The authors would like to acknowledge contributions by Toby Lewis, Tom Robins, Graciela Mentz (University of Michigan), Paul Harbin a Community Partner at Large, Community Action Against Asthma (Detroit, MI), Sarah Bereznicki, Gary Norris, and Ali Kamal (U.S. EPA, RTP, NC), Kevin Talgo, Alejandro Valencia, Brian Naess, Mohammad Omary, and Yasuyuki Akita (Institute for the Environment at UNC, Chapel Hill, NC). HT/HD HT/LD LT Mobile source estimates show spatial and temporal traffic and diesel patterns such as in Diesel EC/CO ratios. Modeled exposure metrics show good agreement with NO x measurement at NEXUS home locations. AM PEAK MOBILE SOURCES: RLINE (under development) is a research-level, line-source dispersion model being developed by EPA’s Office of Research and Development as a 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 being used in conjunction with traffic activity and primary mobile source emission estimates to model hourly exposure for study participants’ home and school locations. BACKGROUND: A space/time ordinary kriging (STOK) method has been developed. This method combines AQS measurement data and results from two annual simulations of the Community Multiscale Air Quality (CMAQ) model. The baseline simulation represented all emissions in a broad region (covering the eastern US), and the second simulation removed all anthropogenic emissions in the NEXUS study domain. The ratios of concentrations predicted by CMAQ in these two simulations in the Detroit region, along with AQS data from background sites in the region, were used to estimate background pollutant concentrations. 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. Near road exposure metrics are based on modeled concentrations at “minigrid” receptors. modeled roadlinks modeled receptors 50m 300m Air Quality Estimates (Sep – Nov. 2010) FALL 2010 AVERAGE 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) Air Quality Modeling in Support of the Near-road EXposures and effects of Urban air pollutants Study (NEXUS) Vlad Isakov, Michelle Snyder, Janet Burke, Kathie Dionisio, David Heist, Steven Perry (U.S. EPA, RTP, NC) Sarav Arunachalam (UNC, Chapel Hill, NC), Stuart Batterman (UM, Ann Arbor, MI) Example: Asthma Control Test (ACT) scores using generalized estimating equation (GEE) models Each 1  g/m 3 increase in traffic related PM 2.5 is associated with a 0.5 decrease in ACT which obtained statistical significance (p-value 0.05). ACT scores have a maximum of 25 (good asthma control); scores below 19 suggest poor asthma control. 3-5  g/m 3 near roads leads to a 1.5 - 2.5 change in ACT score which is very significant. Near road gradients of modeled concentrations for NO X and PM 2.5