Estimating individualized exposure impacts from ambient ozone levels: A synthetic information approach. (May 2018)

• Record Type: Journal Article
• Authors: Pires, Bianica; Korkmaz, Gizem; Ensor, Katherine; Higdon, David; Keller, Sallie; Lewis, Bryan; Schroeder, Aaron
• Is Part Of: Environmental modelling & software. Volume 103(2018)
• Page Start: 146
• Page End: 157
• Publication Date: 2018-05
• Subjects: Synthetic populations -- Air quality -- Ozone -- Microenvironment -- Personal exposure; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.02.007
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: There is ample evidence that short-term ozone exposure is associated with increased respiratory symptoms. Many studies, however, aggregate the population, activities, or concentration levels of the pollutant across space and/or time, failing to capture critical variations in the exposure levels. We couple spatiotemporal air quality estimates of ozone with a synthetic information model of the Houston Metropolitan Area, allowing us to attach exposure levels to individuals based on exact times, geo-locations, and microenvironments of activities. Several scenarios of the model are run at different levels of resolution. When we maintain the spatiotemporal resolution of the data, the proportion of the population that experiences sharp increases in short-term exposure increases substantially. This can be particularly important if experienced by sensitive populations given the increased risk for adverse health effects. We find that individuals in the same zip code, neighborhood, and even household have varying levels of exposure. Highlights: Estimate personal exposure using a synthetic information model of the population. Synthetic information includes activity patterns and geocoded locations. Develop a spatiotemporal model of ozone concentration levels. Utilize high performance computing to couple the two models. Increased sensitivity to detecting variations in exposure within the population.