Air Quality Modeling for Exposure Assessment

• Activity Number: 326 - Exploring the Impact of Air Pollution on Alzheimer's Disease and Other Indicators of Dementia
• Type: Topic Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Qi Ying* and Xiaohui Xu and Eun Sug Park and Richard Smith and Eric Whitsel and James Stewart and Melinda Power
• Companies: Texas A&M University and Texas A&M University and TTI and University of North Carolina Chapel Hill and University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill and George Washington University
• Keywords: emission inventory; data fusing; ozone; PM2.5

Abstract

Large uncertainties still exist in the air quality model predicted exposures partly due to uncertainties in the emission input data and the spatial resolution used in the simulations. Here we describe the air quality modeling efforts to support exposure assessment for the Atherosclerosis Risks in Communities (ARIC) cohort from 1990 to 2012 using two anthropogenic emission inventories, the National Emission Inventory NEI) from US EPA and the Emissions Database for Global Atmospheric Research (EDGAR) from the Joint Research Center of the European Commission. Differences of the two inventories in the Maximum daily average 8-hr (MDA8) O3 and 24-hr PM2.5 exposures using US EPA’s Community Multiscale Air Quality model (CMAQ) are studied in detail. A data fusing approach to provide improved exposure estimations by combining the two sets of model results, and the observation data at monitors is explored. In addition, high-resolution (~100 m) near-road PM2.5 exposure generated using a dispersion model based on detailed road networks and annual vehicle-miles-traveled data are compared with 1-km resolution CMAQ predictions to assess the spatial heterogeneity of near-road PM2.5 exposure.