Towards an in silico Experimental Platform for Air Quality: Houston, TX as a Case Study

dc.contributor.authorPires, Bianicaen
dc.contributor.authorKorkmaz, Gizemen
dc.contributor.authorEnsor, Katherineen
dc.contributor.authorHigdon, Daviden
dc.contributor.authorKeller, Sallie A.en
dc.contributor.authorLewis, Bryan L.en
dc.contributor.authorSchroeder, Aaronen
dc.date.accessioned2017-10-17T18:48:50Zen
dc.date.available2017-10-17T18:48:50Zen
dc.date.issued2015en
dc.description.abstractIn this paper we couple a spatiotemporal air quality model of ozone concentration levels with the synthetic information model of the Houston Metropolitan Area. While traditional approaches often aggregate the population, activities, or concentration levels of the pollutant across space and/or time, we utilize high performance computing and statistical learning tools to maintain the granularity of the data, allowing us to attach specific exposure levels to the synthetic individuals based on the exact time of day and geolocation of the activity. We demonstrate that maintaining the granularity of the data is critical to more accurately reflect the heterogeneous exposure levels of the population across time within the greater Houston area. We nd that individuals in the same zip code, neighborhood, block, and even household have varying levels of exposure depending on their activity patterns throughout the day.en
dc.identifier.urihttp://hdl.handle.net/10919/79676en
dc.language.isoen_USen
dc.publisherCSSSAen
dc.relation.ispartof2015 Computational Social Science Society of America Conferenceen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSynthetic populationsen
dc.subjectAir qualityen
dc.subjectOzoneen
dc.subjectPersonal exposureen
dc.titleTowards an in silico Experimental Platform for Air Quality: Houston, TX as a Case Studyen
dc.typeConference proceedingen

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